TABLE OF CONTENTS. iii. Volume I

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3 iii TABLE OF CONTENTS Volume I Page CHAPTER I INTRODUCTION Historical Background Literature Review Research Objectives Research Procedure... 7 CHAPTER II RESERVOIR GEOLOGY Geologic Description Depositional Environments Rock Fabrics Summary CHAPTER III PERMEABILITY PREDICTION Definition of Permeability and Its Importance in Reservoir Characterization Literature Review: Capillary Pressure Data for Permeability Prediction Application of Predictive Models for Permeability to NRU Data Assessment of Core Data Quality CHAPTER IV CORE-LOG MODELING FOR PERMEABILITY PREDICTION Application of Predictive Models to NRU Data Literature Review: Use of Well Log Data for Permeability Prediction Conventional Core and Well Log Data Available for NRU Study Previous Permeability Modeling Efforts for the Clear Fork at the NRU Sources of Error Current Work New Rock-Log Model

4 iv Page 4.8 Summary CHAPTER V MATERIAL BALANCE DECLINE TYPE CURVE ANALYSIS Introduction Literature Review: Liquid Case (Radial Flow) Fetkovich-McCray Decline Type Curve (Radial Flow Case) Introduction and Review: Well with Infinite-Conductivity Fracture in the Center of a Bounded Circular Reservoir (Liquid Case) Summary CHAPTER VI RESERVOIR SURVEILLANCE Pressure Transient Test Overview Pressure Transient Testing at the NRU Data Acquisition Data Analysis Procedures Estimation of Average Reservoir Pressure Field Examples Water Injection Well Surveillance Reservoir Conformance Studies Field Example: Combining Waterflood Surveillance Diagnostic Tools Summary Volume II CHAPTER VII COMPLETION AND STIMULATION OPTIMIZATION Introduction Rock Mechanical Properties Study Laboratory Work Correlation of Laboratory-Derived Static and Dynamic Core Rock Properties Correlation with Well Log Data

5 v Page 7.6 Predictive Models for Static Elastic Moduli Well Completion Optimization Hydraulic Fracture Design Optimization CHAPTER VIII DATA INTEGRATION Identification of Interwell Reservoir Quality Trends Identification of Intrawell Reservoir Quality Trends Summary CHAPTER IX 10-ACRE INFILL WELL PERFORMANCE Initial Production Rate Comparison Incremental versus Accelerated Reserves Pressure Transient (Interval) Tests Production and Injection Data Analysis Summary CHAPTER X CONCLUSIONS Geology and Petrophysics Long-Term Production and Injection Data Analysis Reservoir Surveillance Rock Mechanical Properties Prediction Completion and Stimulation Data Integration and 10-Acre Well Performance Future Considerations NOMENCLATURE REFERENCES APPENDIX A CAPILLARY PRESSURE DATA FOR 10-ACRE INFILL WELLS APPENDIX B RELATIVE PERMEABILITY DATA 10-ACRE INFILL WELLS

6 vi Page APPENDIX C COMPUTED AXIAL TOMOGRAPHY SCAN SUMMARY, FORMATION RESISTIVITY, ROCK COMPRESSIBILITY AND MINI-PERMEAMETER DATA FOR 10-ACRE INFILL WELLS C.1 Fracture Detection, Lithologic Characterization and Core Screening of Heterogeneous Carbonate Cores from the North Robertson (Clear Fork) Unit Using X-Ray Computerized Tomography C.2 Formation Resistivity Data C.3 Rock Compressibility Data C.4 Mini-Permeameter Data Volume III APPENDIX D CONVENTIONAL CORE DATA FOR 10-ACRE AND 20-ACRE INFILL WELLS APPENDIX E WELL LOG DATA FOR CORED 10-ACRE AND 20-ACRE INFILL WELLS APPENDIX F CURRENT WATER SALINITIES, CORRELATION COEFFICIENTS FOR WELL LOG PERMEABILITY PREDICTORS, HFU MODEL PROBABILITIES AND NON-PARAMETRIC MODELING RESULTS BY ROCK TYPE F.1 Formation Water Salinities for the NRU (Clear Fork) F.2 Pearson Correlation Coefficients for Permeability Prediction by Rock Type F.3 Determination of Hydraulic Flow Units by Rock Type F.4 Determination of Non-Parametric Model Equations for Permeability Prediction by Rock Type APPENDIX G MATERIAL BALANCE DECLINE TYPE CURVE RELATIONS FOR UNFRACTURED WELLS (RADIAL FLOW CASE) G.1 Derivation of Material Balance Plotting Functions for Production Data G.2 The Arps Empirical Rate Decline Functions

7 vii Page G.3 Procedures for the Analysis of Production Data Using the Fetkovich-McCray Type Curves for Unfractured Wells APPENDIX H MATERIAL BALANCE DECLINE TYPE CURVE RELATIONS FOR THE CASE OF A WELL WITH AN INFINITE-CONDUCTIVITY VERTICAL FRACTURE IN A CIRCULAR BOUNDED RESERVOIR H.1 The Constant Rate Solution for a Well with a Uniform Flux or Infinite-Conductivity Vertical Fracture in a Circular Bounded Reservoir H.2 Plotting Functions for Decline Curve Analysis Using Type Curves for a Well with an Infinite-Conductivity Vertical Fracture H.3 Procedures for the Analysis of Production or Injection Data Using the Fetkovich-McCray Type Curves for Fractured Wells APPENDIX I ESTIMATION OF FLUID PROPERTIES USED FOR ANALYSES OF NRU (CLEAR FORK) PRESSURE TRANSIENT, PRODUCTION AND INJECTION DATA APPENDIX J MATERIAL BALANCE DECLINE TYPE CURVE MATCHES FOR ORIGINAL 40-ACRE PRODUCING WELLS Volume IV APPENDIX K APPENDIX L APPENDIX M MATERIAL BALANCE DECLINE TYPE CURVE MATCHES FOR 20-ACRE INFILL PRODUCING WELLS MATERIAL BALANCE DECLINE TYPE CURVE MATCHES FOR 10-ACRE INFILL PRODUCING WELLS MATERIAL BALANCE DECLINE TYPE CURVE MATCHES FOR WATER INJECTION WELLS Volume V APPENDIX N PRESSURE TRANSIENT ANALYSIS RELATIONS N.1 Semilog Analysis Relations

8 viii Page N.2 Type Curves for an Unfractured Well in a Homogeneous, Infinite- Acting Reservoir with Wellbore Storage and Skin Effects N.3 Type Curves for a Fractured Well with a Finite Conductivity Vertical Fracture in a Homogeneous, Infinite-Acting Reservoir with Wellbore Storage Effects APPENDIX O PRESSURE TRANSIENT TEST DATA O Raw Pressure Buildup Test Data (Acoustic Well Sounder) O Raw Pressure Falloff Test Data (Surface Spider TM Gauge) O and 1996 Raw Pressure Buildup Test Data (Memory Gauge and EM) O Raw Pressure Buildup and Drawdown Test Data (Memory Gauge) O Raw Pressure Buildup Test Data (Memory Gauge and AWS) APPENDIX P PRESSURE TRANSIENT TEST RESULTS P Pressure Buildup (Acoustic Well Sounder) Tests P Pressure Falloff (Surface Spider TM Gauge) Tests P and 1996 Pressure Buildup (Downhole Memory Gauge) Tests P Pressure Drawdown/Buildup Tests on Selected Completion Intervals (10-acre Infill Wells) P Pressure Buildup (Acoustic Well Sounder/Memory Gauge) Tests APPENDIX Q FORMATION TEST DATA FROM 10-ACRE INFILL WELLS APPENDIX R STATIC ELASTIC PROPERTIES FOR SELECTED CORE SAMPLES FROM 10-ACRE INFILL WELLS APPENDIX S DYNAMIC ELASTIC PROPERTIES AND AVERAGED WELL LOG RESPONSES FOR SELECTED CORE SAMPLES FROM 10-ACRE INFILL WELLS

9 ix Page APPENDIX T APPENDIX U APPENDIX V Volume VI GRAPHICAL COMPARISON OF STATIC, DYNAMIC AND FWS-CALCULATED ELASTIC MODULI FOR SELECTED CORE SAMPLES FROM 10-ACRE INFILL WELLS CORRELATION OF WELL LOG RESPONSES WITH LAB-MEASURED STATIC ELATIC MODULI 10-ACRE WELLS COMPARISON OF PREDICITVE MODEL RESULTS WITH LAB-MEASURED AND FWS-CALCULATED ELASTIC MODULI FOR CORED AND NON-CORED 10-ACRE INFILL WELLS V.1 Cored Wells V.2 Non-Cored Wells APPENDIX W PREDICTIVE MODEL AND FWS-CALCULATED IN-SITU STRESS PROFILES FOR 10-ACRE INFILL WELLS VITA

10 x LIST OF FIGURES Volume I FIGURE Page 1.1 Location of North Robertson (Clear Fork) Unit, Permian Basin, West Texas NRU production and injection history Stratigraphic units for the Permian Age on the Central Basin Platform Idealized depositional model for the Clear Fork Formation Permeability-porosity relationship for open marine deposits based on quick plug core data from NRU wells 1509, 1510 and Grainstone shoal with numerous pellets and fossil allochems NRU 3319 (Lower Clear Fork) Permeability-porosity relationship for grainstone shoal deposits from quick plug core data from NRU wells 1509, 1510 and Fusulinid shoal with numerous pellets and fusulinids NRU 1509 (Lower Clear Fork) Intershoal wackestone-packstone with pellets, fossil fragments and varying amounts of carbonate mud NRU 3319 (Lower Clear Fork) Permeability-porosity relationship for intershoal deposits based on quick plug core data from NRU wells 1509, 1510 and Reef with rugose coral (upper left of slab) and dense white bryzoan in near growth position NRU 1509 (Lower Clear Fork) Permeability-porosity relationship for reef center deposits based on quick plug core data from NRU wells 1509, 1510 and Reef talus grainstone with prominent bedding NRU 1510 (Lower Clear Fork) Permeability-porosity relationship for reef talus deposits based on quick plug core data from NRU wells 1509, 1510 and Reef debris apron grainstone with numerous allochems NRU 1510 (Lower Clear Fork) Permeability-porosity relationship for reef debris apron deposits based on quick plug core data from NRU wells 1509, 1510 and

11 xi FIGURE Page 2.15 Permeability-porosity relationship for open lagoon deposits based on quick plug core data from NRU wells 1509, 1510 and Permeability-porosity relationship for restricted lagoon deposits based on quick plug core data from NRU wells 1509, 1510 and Dense and light gray burrowed island center NRU 3319 (Lower Clear Fork) Permeability-porosity relationship for island deposits based on quick plug core data from NRU wells 1509, 1510 and Permeability-porosity relationship for island beach deposits based on quick plug core data from NRU wells 1509, 1510 and Island sequence with carbonaceous unit overlying algal mat NRU 3319 (Lower Clear Fork) Permeability-porosity relationship for tidal flat deposits based on quick plug core data from NRU wells 1509, 1510 and Tidal flat algal mat NRU 3319 (Lower Clear Fork) Tidal channel with burrowed top and abundant carbonaceous plant fragments on top of algal mat NRU 3319 (Lower Clear Fork) Permeability-porosity relationship for tidal flat channel deposits based on quick plug core data from NRU wells 1509, 1510 and Permeability-porosity relationship for shallow sub-tidal deposits based on quick plug core data from NRU wells 1509, 1510 and Supratidal mud cracks and silty dolostone NRU 1509 (Upper Clear Fork) Permeability-porosity relationship for supratidal deposits based on quick plug core data from NRU wells 1509, 1510 and Solution collapse breccia with oil staining outlining broken fragments NRU 3533 (Upper Clear Fork) Permeability-porosity relationship for solution collapse breccia deposits based on quick plug core data from NRU wells 1509, 1510 and Calculation of Brooks and Corey pore distribution factor NRU 3533 (core #15B) and NRU 1510 (core #5D) Type curve for the prediction of absolute permeability from capillary pressure data

12 xii FIGURE Page 3.3 Prediction of air permeability from the Swanson correlating parameter, (S b /p c ) A, SCAL plug trimmed ends Air permeability estimate from data overlay NRU 3533, core sample #15B Air permeability estimate from data overlay NRU 3533, core sample #5B Prediction of air permeability from median pore throat radius, MPTR, SCAL plug trimmed ends Prediction of air permeability from Hagiwara equation SCAL plug trimmed ends Comparison between laboratory-measured k air and type curve-calculated k abs six core samples from four wells Prediction of absolute permeability NRU 1509, core #11A Prediction of absolute permeability NRU 3533, core #15B Prediction of absolute permeability NRU 3319, core #30C CAT scan and spectral display for 2-mm. slice of Upper Clear Fork core sample, NRU MPTR versus gamma ray log MPTR versus compensated neutron log MPTR versus bulk density log MPTR versus photoelectric capture cross-section log MPTR versus deep resistivity log MPTR versus shallow resistivity log Graphical estimation of FZI Volumetric proportions of pore types in each rock type Core porosity versus core permeability for the Clear Fork interval Porosity-permeability relationship for one rock type Differentiating "pay" from "non-pay" reservoir rocks Differentiating "pay" reservoir rocks Differentiating rock types 3 and

13 xiii FIGURE Page 4.14 Whole core k MAX and model calculated permeability versus depth for NRU Whole core k MAX and model calculated permeability versus depth for NRU Comparison of visually determined rock types with model-calculated rock types for SCAL plug clip end samples Relationship between effective air, brine and oil permeabilities from mini-permeameter and native-state, unsteady-state relative permeability data NRU Whole core preparation for NRU infill wells Linear relationship between whole core k 90 and k MAX Dykstra-Parsons coefficient calculation for whole core data Dykstra-Parsons coefficient calculation for quick plug data Frequency histogram for whole core permeability measurements Frequency histogram for quick plug permeability measurements Density-neutron crossplot NRU Density-neutron crossplot NRU Density-neutron crossplot NRU NRU rock typing and pay prediction algorithm Initial lithology determination using bulk density and PE data Differentiating "pay" from "non-pay" rock types Segregating rock types 1 4 using deep resistivity data Segregating rock types 3 and 4 using shallow resistivity data Water salinities (ppm) for Lower Clear Fork (northern infill area) Water salinities (ppm) for Middle Clear Fork (northern infill area) Water salinities (ppm) for Upper Clear Fork (northern infill area) Graphical illustration of cementation factor calculation Whole core k MAX versus compensated neutron porosity Whole core k MAX versus bulk density Whole core k MAX versus deep resistivity

14 xiv FIGURE Page 4.39 Whole core k GEOM and model-calculated permeabilities versus depth for NRU Graphical illustration of HFU determination and assignment of FZI Whole core k GEOM and model-calculated permeabilities versus depth for NRU Whole core k GEOM versus HFU model-calculated permeabilities Optimal transformation of ln(gr) Optimal transformation of ln(lld) Optimal transformation of ln(φ c ) Summation of optimal transforms to predict whole core k GEOM Whole core k GEOM and model-calculated permeabilities versus depth for NRU Whole core k GEOM versus the non-parametric model-calculated permeabilities NMR permeability and FFI data matched with quick plug and whole core permeabilities for NRU Estimation of permeability cutoff for NRU Estimation of porosity cutoff for NRU Relation between unstressed effective air permeability and stressed oil permeability from native-state relative permeability tests Fetkovich q Dd and q Ddd type curves for an unfractured well (radial flow case) centered in a bounded circular reservoir Fetkovich-McCray q Dd, q Ddi and q Ddid type curves for an unfractured well (radial flow case) centered in a bounded circular reservoir Semilog production plot for simulated case #1 (constant p wf ) Log-Log production plot for simulated case #1 (constant p wf ) Rate functions for simulated case #1 (constant p wf ) Estimated movable oil from rate history for simulated case #1 (constant p wf ) Estimated movable oil from pressure drop normalized rate history for simulated case #1 (constant p wf ) Estimated movable oil from p cal for simulated case #1 (constant p wf )

15 xv FIGURE Page 5.9 Match of production data for simulated case #1 (constant p wf ) on the Fetkovich-McCray type curve for an unfractured well centered in a bounded circular reservoir Semilog production plot for simulated case #2 (variable p wf ) Log-Log production plot for simulated case #2 (variable p wf ) Rate functions for simulated case #2 (variable p wf ) Estimated movable oil from rate history for simulated case #2 (variable p wf ) Estimated movable oil from pressure drop normalized rate history for simulated case #2 (variable p wf ) Estimated movable oil from p cal for simulated case #2 (variable p wf ) Match of production data for simulated case #2 (variable p wf with shut-ins) on the Fetkovich-McCray type curve for an unfractured well in a bounded circular reservoir North Robertson (Clear Fork) Unit, Gaines County, Texas Producing bottomhole pressures for 10-acre infill wells Location of NRU Semilog production plot for NRU Log-Log production plot for NRU Match of production data for NRU 4202 on the Fetkovich-McCray type curve for an unfractured well in a bounded circular reservoir Estimated movable oil from rate history for NRU Location of NRU Semilog production plot for NRU Log-Log production plot for NRU Match of production data for NRU 1004 on the Fetkovich-McCray type curve for an unfractured well in a bounded circular reservoir Estimated movable oil from rate history for NRU Cartesian plot of p D versus t DA solutions with pseudosteady-state solutions superimposed for the infinite-conductivity fracture case

16 xvi FIGURE Page 5.30 Correlation of b Dpss and r ed values for fractured well solutions infinite-conductivity fracture case q Dd versus t Dd for infinite-conductivity fracture case (establishes "Fetkovich" type curve plotting functions for fractured wells) Fetkovich-McCray q Dd, q Ddi and q Ddid type curves for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Match of production data for NRU 4202 on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Final data match on log-log plot for NRU 4202 pressure buildup test data (Nov. 1988) Location of NRU Semilog production plot for NRU Log-Log production plot for NRU Match of production data for NRU 102 on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Estimated movable oil from rate history for NRU Semilog rate and Cartesian injection pressure versus time for NRU 102(WI) Match of injection data for NRU 102(WI) on the type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Estimated injectable water from a plot of pressure drop normalized injection rate versus cumulative injection for NRU 102(WI) Location of NRU Semilog production plot for NRU Log-Log production plot for NRU Match of production data for NRU 301 on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Estimated movable oil from rate history for NRU

17 xvii FIGURE Page 5.48 Semilog rate and Cartesian injection pressure versus time for NRU 301(WI) Pre-workover match of injection data for NRU 301(WI) on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Estimated injectable water from a plot of pressure drop normalized injection rate versus cumulative injection for NRU 301(WI) Post-workover match of injection data for NRU 301(WI) on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Downhole configuration for majority of NRU pressure buildup and pressure drawdown tests ( ) "Pressure" type curve for an unfractured well in an infinite-acting homogeneous reservoir "Pressure-Integral" type curve for an unfractured well in an infinite-acting homogeneous reservoir "Pressure" type curve for a well with a finite conductivity vertical fracture in an infinite-acting homogeneous reservoir "Pressure-Integral" type curve for a well with a finite conductivity vertical fracture in an infinite-acting homogeneous reservoir Preliminary type curve match for NRU 2703 pressure buildup test data for a well with a finite conductivity vertical fracture in an infinite-acting homogeneous reservoir Estimated bottomhole pressure using the RHM method Final data match on log-log plot for NRU 2703 pressure buildup test data (Dec. 1995) average reservoir pressure map Location of NRU Semilog analysis for NRU 905 pressure buildup test data Preliminary log-log results for NRU 905 pressure buildup test data Preliminary log-log results for NRU 905 pressure buildup test data Final data match on log-log plot for NRU 905 pressure buildup test data (June 1995)

18 xviii FIGURE Page 6.15 Location of NRU NRU 207 wellbore schematic and completion history Comparison of raw AWS and bottomhole memory gauge pressure buildup data for NRU Preliminary results for NRU 207 smoothed AWS pressure buildup test data Preliminary results for NRU 207 memory gauge pressure buildup test data Final data match on log-log plot for NRU 207 pressure buildup test data (Jan. 1997) Average reservoir pressure estimates for raw AWS and bottom hole memory gauge pressure buildup data for NRU 207 using the equation for a rectangular hyperbola (RHM) Fetkovich-McCray type curves for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Match of production data for NRU 3510 on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Final data match on log-log plot for NRU 3510 AWS pressure buildup test data (Nov. 1988) Match of water injection data for NRU 3510 on the Fetkovich-McCray type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Final data match on log-log plot for NRU 3510(WI) pressure falloff test data (Aug. 1994) Map of propagated fracture half-lengths from long-term injection data analyses Hall diagnostic plot Hall plot for NRU 403(WI) indicating pore plugging and possible fracture propagation Individual well and average reservoir parting pressure trends from step-rate tests performed at the NRU Waterflood recovery mechanisms Location of NRU

19 xix FIGURE Page injection and temperature profiles for NRU 1601(WI) injection and temperature profiles for NRU 2901(WI) Comparison of original open-hole and 1995 TND water saturations for NRU Estimated ultimate recovery for NRU Estimated ultimate recovery for NRU Estimated ultimate recovery for NRU Location of NRU Comparison of original open-hole and 1997 TND water saturations for the Upper Clear Fork NRU Comparison of original open-hole and 1997 TND water saturations for the Middle Clear Fork NRU Comparison of original open-hole and 1997 TND water saturations for the Lower Clear Fork NRU injection and temperature profiles for NRU 3511(WI) injection and temperature profiles for NRU 3516(WI) Idealized vertical pressure profiles for homogenous and heterogeneous reservoirs FT vertical pressure profiles for NRU 3532, 3533 and 3534 located in Section 329 (northern infill area) of the NRU Location of survey wells NRU 505, 1509 and 2705 in Section FT vertical pressure profiles for NRU 505, 1509 and 2705 located in Section 327 (southern infill area) of the NRU injection and temperature profiles for NRU 1591(WI) injection and temperature profiles for NRU 3004(WI) Comparison of original open-hole and 1997 TND water saturations for the Lower Clear Fork NRU NRU pore pressure gradient estimate from FT pressure data on seven unit wells in Sections 327, 329 and Qualitative indication of injection support, continuity and reservoir quality from raw pressure-time data Location of wells NRU 301(WI) and NRU 2601(WI)

20 xx FIGURE Page 6.55 Data match on semilog plot for NRU 301(WI) pressure falloff test data (Aug. 1994) Final data match on log-log plot for NRU 301(WI) pressure falloff test data (Aug. 1994) Hall plot for NRU 301(WI) Injection rate and bottomhole injection pressure for NRU 301(WI) Plot of injectivity factor (pressure drop normalized rate) versus cumulative water injected for NRU 301(WI) Match of pre-workover injection data for NRU 301(WI) on the type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Match of post-workover injection data for NRU 301(WI) on the type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir Hall plot for NRU 2601(WI) injection and temperature profiles for NRU 2601(WI) Injection rate and bottomhole injection pressure for NRU 2601(WI) Plot of injectivity factor (pressure drop normalized rate) versus cumulative water injected for NRU 2601(WI) Match of injection data for NRU 2601 (WI) on the type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir (prior to NRU 301 workover) Semilog plot of injection rates for communicating wells Cartesian plot of bottomhole injection pressures for communicating wells Hall plot for NRU 3004(WI) Final data match on log-log plot for NRU 3004(WI) pressure falloff test data (Aug. 1994) Match of injection data for NRU 3004(WI) on the type curve for a well with an infinite-conductivity fracture centered in a bounded circular reservoir

21 xxi FIGURE Page Volume II 7.1 Static Young's modulus versus confining pressure sample 1A Dynamic Poisson ratio versus net axial stress sample 20C Dynamic Poisson ratio versus confining pressure sample 34B Static E versus dynamic E for stress-unloading sequences on saturated core samples Static ν versus dynamic ν for stress-unloading sequences on saturated core samples Comparison of laboratory and well log compressional travel times for depth correlation Comparison of laboratory and well log shear travel times for depth correlation Laboratory measured static E versus FWS-calculated dynamic E Laboratory measured dynamic E versus FWS-calculated dynamic E Laboratory measured static ν versus FWS-calculated dynamic ν Laboratory measured dynamic ν versus FWS-calculated dynamic ν Laboratory measured static E versus gamma ray log response Laboratory measured static E versus bulk density log response Laboratory measured static E versus PE log response Laboratory measured static E versus compressional t log response Laboratory measured static E versus shear t log response Comparison of full and reduced non-parametric predictive models to illustrate data "over-fit." Optimal transformation of ln(φ N ) Optimal transformation of ln(pe) Optimal transformation of ln(ρ b ) Summation of optimal transforms to predict Young's modulus, E Comparison of model-predicted static E with lab-derived (core) static E and FWS-calculated dynamic E for NRU 1509 (LCF)

22 xxii FIGURE 7.23 Optimal transformation of ln( t c ) Optimal transformation of ln(gr) Optimal transformation of ln(φ N ) Optimal transformation of ln(ρ b ) Summation of optimal transforms to predict Poisson ratio, ν Page 7.28 Comparison of predicted static ν with lab-derived (core) static ν and FWS-calculated dynamic ν Calculated in-situ stress profile for NRU Typical hydraulic fracturing scenario for limited-entry perforating Typical hydraulic fracturing scenario when cluster perforating only the highest reservoir quality section of the completion interval Determination of optimal dimensionless fracture conductivity Comparison of one-year oil rate profiles for recently completed 10-acre infill wells Incremental cumulative oil production attributable to CO 2 fracs over a two-year production period Closure stress calculation for NRU 1510(WI) Upper Clear Fork Net pressure profile for NRU 1510(WI) Upper Clear Fork Closure stress calculation for NRU 3532 Middle Clear Fork Net pressure profile for NRU 3532 Middle Clear Fork Closure stress calculation for NRU 2705 Lower Clear Fork Net pressure profile for NRU 2705 Lower Clear Fork Estimated oil flow capacity, k o h, calculated from core-log model for the NRU 10- and 20-acre infill wells Estimated net hydrocarbon-feet, φhs o, calculated from core-log model for the NRU 10- and 20-acre infill wells "Contacted" original oil-in-place calculated from decline type curve analyses on the NRU original 40-acre development wells Estimated ultimate recovery calculated from the NRU original 40-acre development wells

23 xxiii FIGURE Page 8.5 Estimated oil flow capacity, k o h, calculated from decline type curve analyses on the NRU original 40-acre development wells Estimated ultimate recovery calculated from the NRU 20-acre infill wells Estimated oil flow capacity, k o h, calculated from decline type curve analyses on the NRU 20-acre infill wells estimated average reservoir pressure from pressure transient tests estimated average reservoir pressure from pressure transient tests Idealized illustration of Clear Fork depositional environment Correlation between infill well EUR and core-log model predicted net pay thickness Correlation between infill well EUR and core-log model predicted hydrocarbon pore volume Correlation between infill well EUR and core-log model predicted oil flow capacity Correlation between infill well EUR and core-log model predicted rock type 6 (anhydritic dolomite) Predictive equation for EUR using core-log model outputs as predictor variables 10-acre infill wells Predictive equation for EUR using core-log model outputs as predictor variables 20-acre infill wells Predictive equation for EUR using core-log model outputs as predictor variables 10-acre and 20-acre infill wells Comparison between model-predicted EUR and actual EUR for 10-acre infill wells Initial 10-acre infill drilling locations ( ) Unit oil rate increase attributable to initial 10-acre infill wells First year initial oil rates for 40-, 20- and 10-acre wells in the northern infill area First year initial oil rates for 40-, 20- and 10-acre wells in the southern infill area

24 xxiv FIGURE Page 9.5 Production interference effect caused by NRU 3604 infill well Final data match on log-log plot for NRU 3532 (Middle Clear Fork) pressure drawdown test data (May 1996) Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Initial rate profile for NRU Initial rate profile for NRU Initial rate profile for NRU Initial rate profile for NRU Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Initial rate profile for NRU Production interference effect due to addition of NRU Initial rate profile for NRU Production interference effect due to addition of NRU Injection interference effect due to addition of NRU 1510(WI) Injection interference effect due to addition of NRU 1512(WI) Injection interference effect due to addition of NRU 3536(WI) Injection interference effect due to addition of NRU 3539(WI) Relationship between IP and EUR for 10-acre infill wells

25 xxv LIST OF TABLES Volume I TABLE Page 1.1 NRU cumulative production and injection Classification of carbonate porosity Special core analysis (SCAL) database Comparison between lab data and type curve match results SCAL plug trim ends: air permeability and MPTR statistics SCAL plug trim ends segregated by rock type: air permeability and median pore throat radii statistics Use of well log responses to describe rock characteristics Relationship between well log response and permeability Conventional core database Well log database Pore type and classification at the NRU Capillary characteristics by rock type based on mercury injection Porosity, permeability and lithology by rock type Statistics for all core permeability data Statistics for whole core permeability (k GEOM ) data by rock type Pearson correlation coefficients for statistically significant predictor variables for whole core k 90 RT2 (77 sample measurements) Determination of flow units using ln (FZI) for rock type HFU probability for a given range of deep resistivity rock type Summary of non-parametric modeling results by rock type Summary of modeling results for 10-acre infill wells Summary of modeling results for 20-acre infill wells Rate and pressure histories for two simulated data cases Correlative values of b Dpss and r ed for a fractured well in a bounded circular reservoir (infinite-conductivity vertical fracture case) NRU shut-in bottomhole pressure estimates ( )

26 xxvi TABLE Page 6.2 Summary of results for well NRU Volume II 7.1 NRU core samples chosen for rock mechanical property study Summary of dynamic and static moduli for stress-unloading condition on saturated cores Depth-averaged well log data for sample 1A Results for 10-acre infill well core-log modeling Results for 10-acre infill well decline type curve analyses Results for 20-acre infill well core-log modeling Results for 20-acre infill well decline type curve analyses Relative production contribution of individual Clear Fork intervals Material balance decline type curve analysis results for 10-acre infill wells

27 328 CHAPTER VII COMPLETION AND STIMULATION OPTIMIZATION 7.1 Introduction The proper completion and stimulation of wells in the Glorieta/Clear Fork interval is the deciding factor in future well performance at the NRU. We can effectively target completion intervals using the results of our geologic and core-log modeling studies, however, a properly designed completion (i.e., perforation) strategy and hydraulic fracture stimulation typically determines each individual producing well's economic success or failure Rock Mechanical Properties Fracture stimulation treatments at the NRU have been performed using average or regional values for critical input parameters to the fracture simulation model, and a "this seems to work best in this area" approach was followed. We will identify the optimum values for static-state linear elastic properties (moduli) required for each fracture stimulation design at the NRU. Full-wave sonic log data (compressional wave and shear wave travel times) recorded on most of the 10-acre infill wells will be used together with the bulk density data to calculate rock mechanical properties such as shear modulus, bulk modulus, Young s modulus and Poisson ratio. These data are utilized in the calculation of both fracture closure pressure and fracture initiation pressure. Young's modulus of elasticity and the Poisson ratio are required input parameters for hydraulic fracture (HF) design. Dynamic elastic moduli can be estimated using the bulk density (g/cc) from the Compensated Density Log and shear and compressional travel times (µsec/ft) from the FWS Log using the following expressions: ν = 0.5 t s t c t s t c (7.1)

28 329 E =2.68x10 10 ρ b t s 2 (1 + ν).... (7.2) Where E is Young's modulus of elasticity (10 6 psi), ν is the Poisson ratio (fraction), t s is the shear wave travel time (µsec/ft), t c is the compressional wave travel time (µsec/ft) and ρ b is the bulk density (g/cc). In low permeability carbonate rock, there is typically a significant difference between the "dynamic" elastic moduli calculated from well logs and the "static" elastic moduli that are required for HF design. Recording a great number of FWS logs in order to define these elastic moduli in different areas of the NRU is felt to be cost-prohibitive, particularly when the resulting data are not representative values. We will therefore attempt to construct a predictive model for static elastic moduli using conventional well log data as predictor variables. This will allow for the computation of representative values for these parameters and negate the cost of additional FWS well logging operations. The utility of this predictive model will depend primarily on the accurate conversion of mechanical property data from the dynamic to the static state. The data derived from well logs is considered dynamic data due the method in which it is measured (acoustically). We obtained the data required for this conversion by performing both static and dynamic laboratory tests on approximately 16 core plug samples. After defining the relationship between static and dynamic mechanical properties, we then build transforms between conventional well log data (such as gamma ray and bulk density logs) and the static rock mechanical properties data required for hydraulic fracture stimulation design. In this way, we can optimize fracture design on a unit-wide basis at little or no cost to the operator Completion and Stimulation Strategies In the past, the entire Clear Fork interval has been selectively perforated and hydraulically fractured using "limited-entry" techniques (pump rates >2 bbl/min/perf). All intervals meeting porosity and water saturation cutoffs were opened to the wellbore.

29 330 This resulted in the generation of a series of short, fairly high conductivity, parallel fractures for each fracture treatment pumped. While the well is stimulated, it is not accessing much of the productive reservoir area. We will show that "point-source," or "cluster" perforating may yield better results for the NRU Clear Fork. Prior to fracture stimulation, the highest quality section of any particular interval is perforated over only a 10 to 30 foot interval at a high density (shots/ft) and the fracture is allowed to grow from a single point. If feed-in is limited after the well is placed on production, additional pays can be selectively perforated after fracture stimulation. This technique will give us a much better chance of isolating the fracture in productive intervals and somewhat limiting vertical fracture growth. The same techniques may be applied to new injection wells. In addition, vertical fracture growth will be modeled so that the optimum job size and number of fracture treatments required for the Clear Fork interval at the NRU can be determined Pore Pressure Estimation A major concern in an active waterflood environment is the estimation of reservoir/pore pressure, since these data are also required to estimate both the fracture closure and initiation pressure. In many areas of the unit, pore pressure varies significantly due to uneven waterflood support. As shown in Chapter VI, the Formation Test pressure data obtained during the 10-acre infill drilling program will be used to estimate pore pressures within the various reservoir intervals. 7.2 Rock Mechanical Properties Study Approximately 125 core plugs (primarily from reservoir pay intervals) were taken from four cored 10-acre infill wells at the NRU for special core analysis (SCAL). After performing CT scans on each core plug, 46 of the core plugs (3-inch length by 1.5-inch diameter) were sent to the laboratory. From these 46 SCAL samples, 16 were subsequently chosen for rock mechanical properties testing in order to determine the

30 331 relationship between static and dynamic mechanical rock properties (primarily Young's modulus and Poisson ratio), as summarized in Table 7.1, below. Table 7.1 NRU core samples chosen for rock mechanical property study. Brine- Saturated Bulk Density USS Oil-Water Core Depth Log Depth Grain Density Helium Porosity k o k air Sample (feet) (feet) (g/cc) (g/cc) (percent) (md) (md) 1A A A < A < A N/A N/A 7B B B B B B < B C < C N/A N/A 3D D Note: A NRU 1509, B NRU 3533, C NRU 3319 and D NRU The static-elastic mechanical properties of rock can be measured in the laboratory using a triaxial cell to determine the stress-strain relationship for any particular core sample. Dynamic-elastic mechanical properties can be measured in the lab via ultrasonic testing, or can be computed from compressional and shear velocity data available from Full- Wave Sonic (FWS) well logs. Differences between estimates for static and dynamic elastic mechanical properties (moduli) are a function of the reservoir lithology, rock and fluid properties and the insitu stress state. For many formations, the static and dynamic moduli may be almost equal, while for many others they may be distinctly different. Static measurements give a better indication of actual reservoir elastic mechanical behavior than dynamic measurements, but they are time-consuming and costly to obtain, and yield data at only discrete points within the reservoir (core points). Dynamic moduli can be determined

31 332 for the entire reservoir using sonic logs, but do not always compare well with either the static or dynamic lab-derived moduli. Our primary objective for this study is to obtain static Young's modulus and Poisson ratio data for the North Robertson (Clear Fork) Unit. We will use these data to improve our estimates of fracturing pressures and fracture dimensions during hydraulic fracturing. We will attempt to predict static lab-derived (core) moduli using conventional well log responses (other than the FWS Log), and then to build a predictive model that can be used to calculate static rock properties for HF design. 7.3 Laboratory Work A laboratory cell was utilized in order to place each core sample in a triaxial stress state. The cores were subjected to both an axial stress (overburden) and a radial stress (confining pressure) at reservoir temperature in order to model in-situ reservoir conditions. As the stress-strain data were being acquired for the calculation of static rock properties, simultaneous ultrasonic measurements of shear and compressional sonic waveforms were made for the calculation of dynamic rock properties. Loading (increasing stress) and unloading (decreasing stress) cycles were recorded at four different confining pressures: 4,000 psi, 4,500 psi, 5,000 psi, and 5,500 psi, for both dry and brine-saturated core samples Static Data At each of the four confining pressures, Young's modulus, E, (Eq. 7.3) was calculated from the slope of the axial stress versus axial strain curve using line-segment leastsquares fits for each loading and unloading sequence: E static = dσ a dε a.... (7.3) Where σ a is the axial stress (psi) and ε a is the axial strain (length/length).

32 333 The Poisson ratio, ν, (Eq. 7.4) was calculated from the slope of the radial strain versus axial strain curve using line-segment least-squares fits for each loading and unloading sequence: ν static = dε r dε a.... (7.4) Where ε r is the radial strain (length/length), and ε a is the axial strain (length/length). Therefore, for each of the core samples, we have four values for E and ν (one at each confining pressure) for both the loading and unloading cycles. Single values of E and ν were obtained for each loading and unloading cycle by averaging the data for each cycle. For the most part, this involved identifying the most obvious data trend and ignoring the data points that fell off the trend. For several core samples, valid data could not be obtained at all four confining pressures. If a definite trend could be identified, than three, or all four data points were used in the average. This resulted in four values for static Young's modulus and Poisson ratio for each core sample: 1) dry loading; dry unloading; 2) saturated loading; and, 3) saturated unloading. Fig. 7.1 (below) gives an example of how the average loading and unloading trends were calculated for confining pressure versus Young's modulus for saturated core sample 1A (NRU 1509). As we wish to perform our analyses as close to in-situ conditions as possible, we will utilize the saturated core data to construct our predictive model. Another of the primary reasons that static and dynamic rock properties usually differ is the frequency at which measurements are made. Static measurements are made at low frequency, well log measurements are usually in the khz range, and dynamic lab measurements are usually in the MHz range. As the frequency increases, rocks tend to become stiffer, and therefore, E LAB > E WELL LOG > E STATIC. It has been shown 122 that static rock moduli are affected by the presence of "microcracks" within the rock, while dynamic measurements are usually not. This is primarily because acoustic waves take the path of least resistance through the rock framework, and are not affected by the

33 334 internal pore structure (hence the use of a sonic log with an additional porosity log to identify secondary porosity). So that we may compare static and dynamic rock properties under optimal conditions, we wish to negate the effects of the microcracks, if possible. The microcracks will usually close at high overburden stresses, and will not re-open until well after all stress is relieved from the sample, therefore, it makes sense to use the unloading cycle data to construct our static-dynamic correlations. It just so happens that the unloading cycle for the saturated samples is the data set which exhibits the best correlation between static and dynamic lab data, as well as between static lab data and dynamic well log data. Static rock property data for each core sample is given in Appendix R. Figure 7.1 Static Young's modulus versus confining pressure sample 1A.

34 Dynamic Data Values for dynamic Young's modulus and Poisson ratio were measured at four different net axial stresses for both the loading and unloading sequences at each confining pressure. The compressional and shear wave first arrivals ( t c and t s ) were obtained for each net axial stress and utilized to calculate the compressional and shear wave velocities for each core sample (V p and V s, feet/sec). The wave velocities were then utilized to calculate E and ν using the following well-known relations for elastic moduli: 123 E dyn = ρ b V s 2 3 V p 2 V s 2 4 V p 2 V s (7.5) ν dyn = V p 2 2V s 2 2 V p 2 V s (7.6) The saturated bulk density, ρ b, was obtained for each core sample in the lab. These data are shown in Table 7.1. A single value of Young's modulus or Poisson ratio was obtained for each loading and unloading sequence by choosing a point that fell approximately in the middle of each loading and unloading sequence. The net stresses for all core samples ranged between 0 and 2,500 psi, therefore to maintain uniformity, E and ν were obtained for all core samples at a net stress of approximately 1,125 psi for each confining pressure as shown in Fig. 7.2, below. Therefore, as we saw for the calculation of static rock moduli, most core samples will have four values for dynamic E and ν (one at each confining pressure) for both the loading and unloading cycles. As before, a single value of E or ν was obtained for each loading or unloading cycle by averaging the data for each cycle. This resulted in four

35 336 values for dynamic Young's modulus and Poisson ratio for each core sample: 1) dry loading; 2) dry unloading; 3) saturated loading; and, 4) saturated unloading. Fig. 7.3 (below) gives an example of how the average loading and unloading trends were calculated for confining pressure versus Poisson ratio for saturated core sample 34B (NRU 3533). For the most part, because the dynamic moduli were not calculated from tangent slopes as the static moduli were, there are very few extraneous data points (i.e., small variance). Therefore, a simple mean could be used to identify the average loading and unloading trends for each confining pressure, without having to ignore points. Dynamic rock property data for each core sample are given in Appendix S. Figure 7.2 Dynamic Poisson ratio versus net axial stress sample 20C.

36 337 Figure 7.3 Dynamic Poisson ratio versus confining pressure sample 34B. 7.4 Correlation of Laboratory-Derived Static and Dynamic Core Rock Properties As was noted above, the stress-unloading data set for saturated core samples was chosen for the construction of a static-dynamic correlation. After identifying the average unloading trend for each saturated core sample (Table 7.2, below), we noted that there is no simple linear relationship between the static and dynamic core data obtained in the laboratory (Figs ), although some general trends can be identified. The primary goal of this analysis was not to find a relationship between static and dynamic lab data, but to find a method for predicting static rock properties from openhole log data. In particular, to build a correlation between conventional open-hole log data (i.e., Gamma Ray, Neutron Porosity, Bulk Density, Photoelectric Capture Cross Section, Resistivity, and Compressional t) and static moduli. In this way, we hope to be able to predict the static moduli used as input parameters for hydraulic fracture design without having to record or process full-wave sonic data. This is in keeping with our stated goal of stressing cost-effective tools for reservoir exploitation.

37 338 Table 7.2 Summary of dynamic and static moduli for stress-unloading condition on saturated cores. Core Depth Static E Dynamic E Static ν Dynamic ν Well Sample (feet) (10 6 psi) (10 6 psi) (dim.) (dim.) A D A B D B A C B B B A B B A C Note: dim. = dimensionless Figure 7.4 Static E versus dynamic E for stress-unloading sequences on saturated core samples.

38 339 Figure 7.5 Static ν versus dynamic ν for stress-unloading sequences on saturated core samples. 7.5 Correlation with Well Log Data Depth Correlation The raw core data was depth-correlated with open-hole log data by comparing core and well log porosities for the four cored 10-acre infill wells (1509, 1510, 3319, and 3533) so that a core-log correlation could be constructed for the prediction of static moduli from open hole well log responses. The depth corrections between core and well log data for each cored interval are shown in Table 7.1, above. Instead of utilizing a single well log value at a specific core depth to perform calculations, the well log data were averaged across a two-foot interval with the core point at the center, as shown in Table 7.3 and in Appendix S. The vertical resolution of

39 340 the individual well logging tools is anywhere from a few inches to several feet. By averaging the data we are sure to capture the depth interval from which the core sample was taken. Our assumption is that the vertical heterogeneity of the reservoir is such that we can assume homogeneity over a small interval. In addition, the laboratory-derived compressional and shear travel times were plotted versus the well log-derived travel times for depth correlation. The results are shown in Figs , and seem to indicate that our depth correlation may be slightly off for a few of the core samples. The core and well log porosities were re-checked and the depth correlation was found to be satisfactory. As a check, the well log-averaged interval was re-computed above and below the original interval without significant change in results. Therefore, any discrepancy between lab dynamic measurements and well log dynamic measurements is most probably due to: 1) measurement frequency lab dynamic measurements are in the MHz range, while well log measurements are in the khz range; or, 2) core sample preparation incomplete saturation, core end effects, stress state, etc. These and other sources of error will be discussed at the conclusion of this chapter. Table 7.3 Depth-averaged well log data for sample 1A. Well log depth feet and core depth feet. Well Log Depth (feet) GR (API) t c (µsec/ft) t s (µsec/ft) ρ b (g/cc) φ N (percent) PE (b/e) E (10 6 psi) ν (dim.) ' Ave:

40 341 Figure 7.6 Comparison of laboratory and well log compressional travel times for depth correlation. Figure 7.7 Comparison of laboratory and well log shear travel times for depth correlation.

41 Lab Moduli versus Full-Wave Sonic Log Moduli As a first step, well log-derived dynamic moduli were calculated in the same way as the lab-derived dynamic moduli (see Eqs ) using the compressional and shear velocity data from the Full-Wave Sonic Log and the Bulk Density from the Compensated Density Log. We once again see that, for the most part, the lab-derived static and dynamic moduli and the FWS-calculated dynamic moduli are not linearly related, as shown in Figs The static and dynamic core moduli are presented together with the FWS-calculated moduli for each core sample in Appendix T. Figure 7.8 Laboratory measured static E versus FWS-calculated dynamic E.

42 343 Figure 7.9 Laboratory measured dynamic E versus FWS-calculated dynamic E. Figure 7.10 Laboratory measured static ν versus FWS-calculated dynamic ν.

43 344 Figure 7.11 Laboratory measured dynamic ν versus FWS-calculated dynamic ν. The fact that we find no direct correlation between static and dynamic core-derived moduli and FWS-calculated moduli is not surprising. The Clear Fork has a high degree of heterogeneity, as well as low porosity and permeability, and it is extremely difficult to reproduce reservoir conditions in the lab. For more homogeneous reservoir rock, the core-derived dynamic moduli and the well log-derived dynamic moduli are often closely correlated. In fact, for the core samples taken from Well 3533, in which most of the reservoir rock consists of more homogeneous grainstone rock, the core-derived and FWS-calculated moduli correlate fairly well Lab Static Moduli as a Function of Individual Well Log Responses As stated above, the primary goal of this analysis was not to find a relationship between static/dynamic lab moduli and FWS-calculated moduli, but to find a method for predicting static rock mechanical properties from open-hole log data without the use of a

44 345 Full-Wave Sonic Log. We now examine the relationship between individual well log responses and static moduli, as shown in Figs for Young's modulus. The Gamma Ray Log, Bulk Density Log, PE Log and both the compressional and shear t well log responses appear to have some correlation to the static Young's modulus for the 16 core samples tested. We use this information as a starting point for the construction of a predictive model for static Young's modulus. Similarly, the Compensated Neutron Log, PE Log and both the compressional and shear t well log responses appear to have some correlation with the static Poisson ratio for the 16 core samples tested. We use this information as a starting point for the construction of a predictive model for static Poisson ratio. The PE log is especially well correlated with E and ν, indicating a fairly strong dependence on lithology. All the plots of lab-measured static E and ν as a function of individual well log responses are presented in Appendix U. Figure 7.12 Laboratory measured static E versus gamma ray log response.

45 346 Figure 7.13 Laboratory measured static E versus bulk density log response. Figure 7.14 Laboratory measured static E versus PE log response.

46 347 Figure 7.15 Laboratory measured static E versus compressional t log response. Figure 7.16 Laboratory measured static E versus shear t log response.

47 Predictive Models for Static Elastic Moduli SAS TM Model 70 Multivariate linear and non-linear models were constructed initially using a commercial statistical software package 70 with only marginal success. When using this software package, a functional relationship (log 10, ln, square root, etc.) must be assumed between the dependent (static moduli) and independent (well log responses) variables. While the static moduli could be matched at the core points, what resulted over the rest of the reservoir interval was an "over-fit" of the data that resulted in a large variation of the static moduli. In order to solve this problem, several independent variables were dropped from the model by optimizing on the adjusted coefficient of multiple determination, r 2 a, which results in the generation of an optimal reduced model by removing predictor variables with large collinearity. The resulting reduced model was still unsatisfactory, therefore, another approach was taken by using a technique that assumes no functional form between dependent and independent variables GRACE Model 71 The GRACE (Alternating Conditional Expectation) algorithm 71 produces an optimal correlation between a dependent variable and multiple independent variables by generating non-parametric transformations of both the dependent and independent variables. In simple terms, this means that functional relationships (log 10, log e, square root, etc.) do not have to be assumed between dependent and independent model variables, but that the algorithm itself chooses which data transformations are optimal. These transformations are a function of the data set being analyzed, which means that for each different set of data different transformations will result. In addition, since each independent (predictor) variable is transformed separately with respect to the transformed dependent variable, if independent variables are removed from the model, then the optimal transformations may change.

48 349 For this study, the best predictive models were obtained by taking the natural log of both the dependent and independent variables prior to transformation. The resulting optimal transformations are fit with simple second-order quadratic equations, as the use of higher order expressions did not improve the results. Initially, all well log responses (other than shear t, as we don't wish to use FWS data in our model) were included in our predictive models for Young's modulus and Poisson ratio. As above, this resulted in an "over-fit" of the data over non-cored intervals, as shown in Fig Using an iterative process, predictor variables were dropped until satisfactory reduced models were found for the prediction of static moduli from individual well logs. Figure 7.17 Comparison of full and reduced non-parametric predictive models to illustrate data "over-fit."

49 Predictive Model for Static Young's Modulus The best predictive model (r 2 = 0.84) found for static Young's modulus was a reduced model using the Compensated Neutron Porosity (percent lime matrix), Photoelectric Capture Cross Section (barns/electron) and Compensated Bulk Density (g/cc). The final result and the optimal transformations of the independent variables and the resulting quadratic fits are shown graphically in Figs , and in equation form below: ln(φ N Transform) = (ln φ N ) (ln φ N ) (7.7) ln(pe Transform) = (ln PE) (ln PE) (7.8) ln(ρ b Transform) = (ln ρ b ) (ln ρ b ) (7.9) The dependent variable can then be expressed as: ln E static = (Σ transforms) (Σ transforms) (7.10) for which: Σ Transforms = ln(φ N Transform) + ln(pe Transform) + ln(ρ b Transform).. (7.11) and: E static = exp (ln E static ).... (7.12)

50 351 Figure 7.18 Optimal transformation of ln(φ N ). Figure 7.19 Optimal transformation of ln(pe).

51 352 Figure 7.20 Optimal transformation of ln(ρ b ). Figure 7.21 Summation of optimal transforms to predict Young's modulus, E.

52 353 Plots of the model-predicted static E compared with the static E from the triaxial tests and the FWS-calculated dynamic E are given in Appendix V for the reservoir sections of all cored wells and several non-cored wells. An illustrative example is shown in Fig for the Lower Clear Fork section of NRU Overall, the predicted static Young's Moduli are slightly lower than the dynamic core and FWS-calculated dynamic Young's Moduli, and agree fairly well with the static laboratory core results. For most reservoir rocks, static E will usually be lower than dynamic E due to different measurement techniques (as was noted in the Section 7.3.1, above). It would appear that the dynamic E values used in previous HF designs have probably been in error. Any effect this has on HF design will be discussed below. Figure 7.22 Comparison of model-predicted static E with lab-derived (core) static E and FWScalculated dynamic E for NRU 1509 (LCF).

53 Predictive Model for Static Poisson Ratio The best predictive model (r 2 = 0.81) found for static Poisson ratio was a reduced model using the compressional t (µsec/ft), Gamma Ray (API Units), Compensated Neutron Porosity (percent lime matrix), and Compensated Bulk Density (g/cc). The final result and the optimal transformations of the independent variables and the resulting quadratic fits are shown graphically in Figs , and in equation form below: ln ( t c Transform) = (ln t c ) (ln t c ) (7.13) ln (GR Transform) = (ln GR) (ln GR) (7.14) ln (φ N Transform) = (ln φ N ) (ln φ N ) (7.15) ln (ρ b Transform) = (ln ρ b ) (ln ρ b ) (7.16) The dependent variable can then be expressed as: ln (ν static ) = (Σ transforms) (Σ transforms) (7.17) for which: Σ transforms = ln ( t c Transform) + ln (GR Transform) + ln (φ N Transform) +ln(ρ b Transform)... (7.18) and: ν static = exp (ln ν static ).... (7.19)

54 355 Figure 7.23 Optimal transformation of ln( t c ). Figure 7.24 Optimal transformation of ln(gr).

55 356 Figure 7.25 Optimal transformation of ln(φ N ). Figure 7.26 Optimal transformation of ln(ρ b ).

56 357 Figure 7.27 Summation of optimal transforms to predict Poisson ratio, ν. Plots of model-predicted static ν compared with the static ν from the triaxial tests and the FWS-calculated dynamic ν are given in Appendix V for the reservoir sections of all cored wells and several non-cored wells. An illustrative example is shown in Fig for the Upper Clear Fork section of NRU 3533.

57 358 Figure 7.28 Comparison of predicted static ν with lab-derived (core) static ν and FWScalculated dynamic ν. Overall, the predicted static Poisson ratios are lower than the laboratory core and FWScalculated dynamic Poisson ratios and agree fairly well with the static laboratory core results. As we see from Fig. 7.28, the core data indicate that there is a significant change in Poisson ratio across the reservoir interval. While the dynamic ν calculated from FWS data does not capture this variation, our predictive model appears to do rather well. While the static and dynamic Young's Moduli differ slightly, there is a significant difference between static Poisson ratios (lab and model-predicted) and dynamic Poisson ratios (lab and FWS-calculated) for the Clear Fork section. It would appear that the dynamic ν values used in previous HF designs have also been in error. The effect this has on HF design, if any, will be discussed below.

58 Results The predictive models for static moduli developed above were utilized to generate E, ν, and in-situ stress (closure pressure, σ x ) profiles across the reservoir sections of several 10-acre infill producing and injection wells at the North Robertson Unit. In Chapter VI, we found that the fracture initiation pressure and in-situ stress were calculated using the following expressions: FIP = Depth 2 ν (1 ν) (g ob αg pore ) + αg pore + σ ext... (7.20) σ x = Depth ν 1 ν g ob αg pore + g pore.... (7.21) For which g ob is the overburden pressure gradient, g pore is the pore pressure gradient, and α is the Biot elastic constant. The external stress, σ ext, accounts for any externallyapplied stresses (tectonic, thermal, etc.), which are negligible at the NRU. For our analyses, we used a value of 1.05 psi/ft for the overburden pressure gradient, psi/ft for the pore pressure gradient. We note that the Biot constant can vary between 0 and 1.0, however, 0.9 or 1.0 are commonly used values. It is important to note that the Biot constant can be much smaller than 1.0 and vary significantly throughout a reservoir interval. A smaller Biot constant will result in a lower value of fracture initiation pressure. After fracture initiation, the Biot constant in the direction of least principal stress (horizontal, in this case) is no longer a function of rock compressibility and may be assumed to be equal to 1.0, therefore, it is has no effect on the calculation of closure pressure. The Biot constant is defined as: α =1 K K ma.... (7.22)

59 360 Where K is the bulk modulus of elasticity of the rock and K ma is the bulk modulus of elasticity for rock with φ = 0. The Biot constant averaged approximately 0.33 across the Clear Fork interval based on bulk moduli calculated from the FWS, however, when we use these values to estimate FIP, we get unrealistic results. More believable results are obtained by simply using α = 0.9. As an example, for the Middle Clear Fork interval at a mean depth of 6,850 feet and under the current stress conditions, we have: FIP = 6850 ft (1.05 psi/ft psi/ft) psi/ft (1 0.25) FIP = 5,722 psia (0.835 psi/ft). σ x = 6850 ft psi/ft psi/ft psi/ft σ x = 4,508 psia (0.658 psi/ft). Appendix W contains plots of model-predicted and FWS-calculated in-situ stress as a function of depth for several NRU 10-acre infill wells. The model-predicted in-situ stress profiles match the FWS-derived profiles extremely well and are generated without the additional cost of recording and processing a FWS log. In addition, the modelpredicted data show more of the character that we would expect to see in a heterogeneous reservoir such as the Clear Fork. An illustrative example is provided in Fig. 7.29, below.

60 361 Figure 7.29 Calculated in-situ stress profile for NRU Sources of Error Mechanical Properties Study As for any experimental study, many assumptions have been made to arrive at the final result, and there are many factors that affect the quality of the results we have obtained. Before we make any conclusions about our results, we must first evaluate the possible sources of error.

61 Laboratory Errors A total of 125 special core plugs (originally, 3-inch length x 1.5-inch diameter) were taken from the cored intervals from four NRU wells. Only 16 of these cores were used for this study, which means that statistical bias was introduced. For this sample and population size, we should be able to predict Young's Moduli to within +8.0x10 5 (mean E = x10 6 psi) and Poisson ratios to within (mean ν = ) with 95 percent confidence. These error bounds are sufficient for our purposes, however, most of the core samples were taken from the higher quality reservoir rocks (see Table 7.1). A more representative sampling, including both reservoir and non-reservoir rock types with different depositional and diagenetic characteristics would have perhaps yielded better results and increased the model's statistical validity. If more core samples were analyzed, we could perhaps build different predictive models for static elastic moduli for different areas of the Unit, different rock types, or for different depositional environments. However, it is not clear that the extra time and cost required to perform such an expanded study would greatly improve the results, or improve our HF designs. We may have introduced error into the analysis as a result of averaging the static and dynamic core data for each stress-loading and unloading sequence in order to obtain a single value for each cycle. The static core data showed a great amount of variation due to the difficulty involved in measuring stress-strain relationships for a complex carbonate rock such as that found in the NRU Clear Fork. The saturated core data (unloading cycle) was utilized to build our predictive model. The complex pore structure of the Clear Fork rock may have resulted in incomplete saturation of several of the lower permeability/porosity core samples. The unloading data was utilized since it was felt that the "microcracks", which are opened as stress is relieved from the rock when it is brought to surface pressure conditions, and adversely affect the comparison of static and dynamic rock properties, would be closed during the loading cycle. The lab conditions that resulted during the

62 363 unloading cycle for saturated core samples may not have been representative of actual reservoir conditions. For any study in which core properties and well log responses are compared, it is very difficult to achieve proper depth correlation. Although we believe our depth correlation was fairly good, it should be noted that we are comparing data from a discrete core point with averaged well log responses across a two-foot interval with the core point at its center. Although these 16 core plugs were originally 3 inches x 1.5 inches, as the result of core preparation for SCAL testing and for mechanical properties testing, the edges were trimmed off most of the samples so that their lengths were reduced to between 1.35" and 1.95". This resulted in length-diameter ratios between 0.9 and 1.3. It has been found in previous studies 122 that the optimum length-diameter ratio is 2.0 in order to reduce or remove core end-effects. Since none of the core samples arrived at the lab with a lengthdiameter ratio of 2.0, it was not possible to investigate the effect that core length has on the calculation of Young's modulus and Poisson ratio, although this may introduce some error in the final results Predictive Model Errors There are no simple linear correlations between static moduli and conventional well log responses. Multivariate analyses had to be utilized for static moduli prediction. We believe that the use of a non-parametric approach (GRACE) greatly improved our predictive models, and they work well when predicting static moduli at the cored points. Whether they are representative across the entire reservoir interval remains to be seen Summary Static elastic moduli can be predicted fairly accurately from conventional well log responses. It would appear that there is no further need to record FWS logs for the determination of mechanical rock property data. The analysis of additional core samples may improve our predictive models, however, we note that the static elastic moduli and stress profiles generated for fracture simulation

63 364 have less of an affect on the final HF design than the perforating strategy, fluid rheology and fluid leakoff coefficient. Changing most of the rock mechanical properties by as much as 50 percent will have little impact on the fracture dimensions, as long as the changes are consistent. The input value used for Young's modulus in previous HF designs is approximately half of what we calculated from the open-hole log data (another reason to do those static-dynamic core tests). The regional Clear Fork value for Young's modulus of 6.5x10 6 psi is significantly different than our mean calculated value of 9.8x10 6 psi. However, this magnitude difference does not typically affect the HF design since all layers are normalized to the regional input value and the character of the mechanical property profile is retained (i.e., correct identification of sands and shales). It is unclear whether the additional cost and time required to perform additional studies would greatly enhance the predictive model or the HF design. Having said this, it is obviously preferable to use accurate values for Young's modulus, Poisson ratio and closure pressure in the initial stimulation design. These values have been significantly abused in past HF designs at the NRU. 7.7 Well Completion Optimization Overview We will investigate the affects of perforation geometry, pre-fracture acid jobs, fracture fluids, fracture proppants, injection volumes and pump rates on initial well potential (IP). Preliminary findings seem to show that reservoir quality plays the largest role in determining individual well IPs. We should be able to target smaller, more confined completion intervals in order to improve injector-producer conformance, confine vertical height growth and limit the propagation of multiple fractures. The gross completion interval extends from the top of the Glorieta to the base of the lower Clear Fork (1,300 feet). Historically, very little production can be attributed to the Glorieta at the NRU, therefore we concern ourselves only with the Clear Fork interval (6,150 7,250 feet).

64 Pre-Fracture Preparation: Perforating and Acidizing Pre-fracture cleanup acid jobs have been performed to remove near-well damage using between 1,000 and 3,000 gallons of 15 percent HCl acid with ball sealers (flow diverters). For intervals that have been perforated for limited-entry fracturing (> 2 bbl/min per perforation), the acid breakdown is a necessity prior to fracture stimulation to remove near-wellbore tortuosity and reduce perforation friction. As a result of these treatments, we typically have few problems when pumping the primary fracture stimulation. The two primary perforating scenarios utilized for the Clear Fork at the NRU are illustrated in Figs Limited Entry Perforating Wellbore Damage Removal - Not Effective HF Parallel Pancake Fractures Figure 7.30 Typical hydraulic fracturing scenario for limited-entry perforating. Too many open perforations result in propagation of multiple fractures with limited lateral growth.

65 366 Cluster Perforating Figure 7.31 Typical hydraulic fracturing scenario when cluster perforating only the highest reservoir quality section of the completion interval. Historically, limited-entry perforating has been used since the porosity and water saturation cutoffs used to determine pay resulted in a large percentage of the total interval being opened to the wellbore. Cluster perforating allows us to target the primary reservoir pay intervals and reduces the chance for the formation of multiple fractures. Since there are no barriers to vertical fracture growth, large intervals can be effectively propped from a point source. This method saves time, reduces completion costs and will typically result in the generation of more uniform fracture geometry with greater lateral extension (i.e., half-length).

66 Hydraulic Fracture Design Optimization In the past, many hydraulic fracture treatments at the NRU were performed without an adequate understanding of the rock physics and fluid rheology. With the advent of realtime monitoring systems and "on-the-fly" HF designs, we are now better able to predict exactly how hydraulic fractures propagate. Important fracture design parameters can now be calculated while the job is being pumped and we do not have to rely on simplified fracture models and regional input parameters. However, this does not decrease the importance of having accurate pre-job estimates of permeability, pore pressure and in-situ stress that can be used to guide the design process. This is why an in-depth reservoir characterization is so vital for any fracture design. Optimization of HF treatments has been an ongoing process during unit development. A major concern with regard to well completion work during each phase of unit development was the need for a limited-entry type fracture job to ensure that the entire productive section was being treated equally. This interval was completed and stimulated in two or three separate stages, depending on the location of the well within the unit. Results of the fracture treatments on the original 40-acre primary producers were poor due to the fact that the bottomhole treating pressure could not be maintained at a sufficiently high level for fracture propagation due to burst limitations on the casing. At the time of unitization in 1987, the average fracture job was approximately 1,000 barrels of fluid with 100,000 pounds of sand loaded at 1 8 lbm/gallon (ppg). Since there are no effective large-scale barriers to vertical fracture propagation in the NRU Clear Fork, sufficient non-perforated intervals were maintained in an attempt to prevent communication between successive fracture stages. Over the development history of the unit, the number of perforations per stage has been reduced in order to maintain limited-entry "types" of fractures. During the 20-acre infill program, the optimum number of perforations per stage was determined to be one perforation for each barrel per minute (BPM) injection rate using a 2D fracture simulation model. 124,125 We note that this does not actually meet the limited-entry definition of 2 bbl/min/perf,

67 368 however, any further reduction in the number of perforations resulted in wellbore screenouts. The average pump rate was between 35 and 40 BPM down 5.5" casing. At that time, fracture jobs were designed for the creation fracture half-lengths of 120 feet. The majority of HF designs at the NRU (on both producing and injection wells) have apparently resulted in the propagation of insufficient fracture half-lengths. This problem may be attributed to three primary causes: 1) the inability to correctly model the significant fracture fluid leakoff; 2) excessive vertical height growth; and, 3) the propagation of multiple fractures. As the pressure transient analyses presented in Chapter VI indicated, it appears previous stimulation treatments on producing wells did not achieve the designed fracture halflengths, although it appears that almost all the wells were sufficiently stimulated, and effective pressure sinks were created at the wellbore. Future fracture jobs must be designed to account for vertical height growth, fluid leakoff rate and the decreased well spacing that are defining characteristics of the NRU Clear Fork. We must design for fairly short (<200 feet) fractures with moderate fracture conductivity because the creation of longer fractures (at high rates during HF treatments) is not possible due to excessive vertical height growth and fluid leakoff. If completion and stimulation of only the most continuous layers of the reservoir can be accomplished, then long hydraulic fractures are not required. Previous production history has shown that regardless of the degree of reservoir continuity, long fractures are not necessary, and are in fact harmful to completion efficiency due to interwell communication. If well spacing is reduced to 10 acres (r e =- 372 feet) throughout the unit, then a fracture half-length of 200 feet is sufficient from both an economic and operational standpoint. Sufficient fluid must be pumped to over-come the significant fluid leakoff rate in the Clear Fork in order to create a foot propped fracture, and we must realize that the fracture will grow up and down as much as it does out (radial fracture geometry). Fortunately, the vertical growth is not so rapid as to cause a premature screenout in most cases. The few jobs that did screen out prematurely were wellbore screenouts due to insufficient injection rate or injection pressure. In addition, some of these screenouts

68 369 were caused by HF designs that called for 16/30 proppant, but did not account for the required increase in fracture width over 20/40 sand. Unfortunately, the reason they did not screenout is due to the fact that they were grossly over-designed with respect to injected fluid volumes. The additional volumes do not provide a deeper-penetrating fracture (excess growth is up and down) or increase the well's initial IP. Job size and the number of stages can be reduced and still provide adequate stimulation NRU Fracture Design Example From a performance and cost-effectiveness standpoint, it would appear that a lbm/gallon crosslinked Borate fluid will work well for hydraulic fracture jobs in the Clear Fork at the North Robertson Unit. A maximum sand concentration of 5 ppg with a 5 ppg resin-coated (RC) sand "tail-in" is more than sufficient for our fracture conductivity requirements. Example calculations are shown below are for a standard NRU fracture stimulation treatment with the following characteristics:! Depth to mid-perfs = 6,850 feet! Net pay thickness = 100 feet! Design fracture half-length = 200 feet! Expected gross fracture height = 400 feet! Desired proppant = 20/40 Ottawa sand! Desired fluid = 25 ppg gel (with Borate crosslinker)! Max sand concentration = 5 ppg! Injection rate = bpm! Casing size = 5.5 inches, 17 lbm/ft J Dimensionless Fracture Conductivity It has been shown by previous investigators, that for formation permeabilities of 1 md or more, the optimal dimensionless fracture conductivity, C fd, for any reservoir or well is approximately equal to Our own investigation indicated that by minimizing the f 3 variable (function 3) introduced by Cinco-Ley and Samaniego-V., 127 the number was actually closer to 1.52, as shown in Fig

69 370 Figure 7.32 Determination of optimal dimensionless fracture conductivity Due to conductivity reduction caused by proppant crushing and various chemical processes, we actually need to design for an initial dimensionless fracture conductivity of (= 1.52/0.30). This correction will account for the 70 percent reduction in longterm fracture conductivity 130 of the proppant pack associated with the use of 20/40 sand as a propping agent. Designing for initial dimensionless conductivities greater than this value will increase the cost of the stimulation, and increase the chances of a screenout (wellbore in worst case, tip in best case), without substantially increasing the well's productivity index (PI). Fracture conductivity can be defined as: wk f = π C fd x f k o.... (7.23)

70 371 Where w is the fracture width (feet) and k f is the proppant pack permeability (md). The effective oil permeability, k o, in this case will be 0.1 md, which is the mean value from material balance decline type curve analysis on all producing wells in the unit. The fracture half-length, x f, is 200 feet. Using these inputs, and accounting for the long-term reduction in the proppant pack conductivity, we have a design fracture conductivity of: wk f =(π) (5.067) (200 feet) (0.1 md) = md feet. A 20/40 Ottawa sand meets our conductivity requirements for the closure stress (3,500 psia) and temperature (115 o F) of this interval. Neither Jordan sand, nor Brady sand meet the conductivity requirement. More exotic proppants are not required at the NRU. A proppant should be chosen so that the long-term conductivity requirements are met, the created fracture width is large enough to safely receive the proppant and the diameter of the perforations is large enough to prevent premature screenout. The diameters for each individual proppant size may be calculated as: d prop = (small mesh) (large mesh) (7.24) Where d prop is the proppant diameter in inches. The diameters for proppants typically used at the NRU are:! 20/40 proppant diameter = in. = feet.! 16/30 proppant diameter = in. = feet.! 12/20 proppant diameter = in. = feet. As a rule of thumb, the created fracture width should be at least three times the proppant diameter. Therefore:! 20/40 proppant width requirement = in. = feet.! 16/30 proppant width requirement = in. = feet.! 12/20 proppant width requirement = in. = feet. If we wish to use a proppant size of 16/30 or above, we must design for a fracture with a width greater than in., or we may screenout.

71 372 In addition, the perforation diameter should be at least six times the proppant diameter:! 20/40 proppant perf dia. requirement = in. = feet.! 16/30 proppant perf dia. requirement = in. = feet.! 12/20 proppant perf dia. requirement = in. = feet. Perforation diameter should not be a problem since we typically shoot 0.41-inch diameter holes using inch or 4-inch diameter casing guns. In addition, the perforation diameter will only increase during injection due to friction-related wear. The permeability of 20/40 Ottawa sand is 180,000 md. To achieve our conductivity specifications, we will need to design for a propped fracture width of at least: w = md feet 180,000 md = feet = inches Required Fracture Fluid Volume To achieve a propped fracture width of inches, we will need a minimum created fracture width (at the end of pumping) that will be extremely close to our minimum fracture width requirement for 20/40 sand ( inches). Based on previous simulation results (function of the model used) and field experience, the propped fracture width is typically one-half to one-quarter of the created width. We will utilize a created fracture width of 0.1 inches for our initial volume calculation. Previous Clear Fork field experience indicates that for a 200-foot design fracture halflength, the fracture will grow both up and down approximately 200 feet (radial geometry). Therefore the gross height is approximately 400 feet. The fracture volume for a radial geometry is: V f =(π) (x 2 f )(w)... (7.25) V f =(π) (4x10 4 ft 2 )( feet)(7.48 gal/ft 3 ) = 7,833 gallons. Where V f is the fracture volume (ft 3 ). We note that this is only the volume of the desired fracture, and not the total fluid requirement, since we have not yet accounted for fluid leakoff to the formation. For the

72 373 Clear Fork, fluid efficiencies average less than 60 percent, therefore the actual job volume (not including pad volume) will most likely be approximately 70 percent more than the fracture volume. Fluid efficiency (FE) is calculated as: FE = V f V inj.... (7.26) Where V inj is the main fracture treatment volume (does not include pad volume) Proppant Requirements In order to calculate the proppant load for this fracture volume, we must consider our proppant schedule. If we are "ramping" proppant concentrations from 1 ppg up to 5 ppg, which should be more than sufficient for our conductivity requirements, our average proppant concentration will likely be on the order of 3.5 ppg, based on field results. Utilizing an average proppant concentration of 3.5 ppg, the equivalent slurry density, ρ slurry, (ppg) is: ρ slurry = (m prop + m ff ) m prop (SG prop )(ρ ff ) (7.27) ρ slurry = (3.5 lbm lbm) 3.5 lbm (2.65)(8.34 lbm/gal.) +1.0 = lbm/gallon. Where m prop is the mass of the proppant for one gallon of the mean slurry (lbm), m ff is the mass of one gallon of fracture fluid (lbm), ρ ff is the density of the "clean" fluid (ppg) and SG prop is the specific gravity of the proppant (dimensionless). Therefore, the total proppant required is simply: Proppant, lbm = (V f )(ρ slurry )... (7.28) Proppant, lbm = (7,833 gals.)(10.22 lbm/gal.) = 80,053 lbm.

73 374 This corresponds to a proppant concentration in the fracture of 0.64 lbm/ft 2. This will certainly meet our conductivity requirements, and the proppant load may be reduced if we can still achieve an initial dimensionless fracture conductivity of In fact, based on 2D fracture simulation, a total proppant load of 35,000 lbm will still meet our conductivity requirement. This is directly related to the contrast between the proppant permeability and the effective oil permeability, which is quite low (0.1 md). The final proppant stage concentration should be maintained as high as possible to enhance near-wellbore conductivity. Certainly, a ramped sand schedule from 1 to 5 ppg would meet our requirements. A 2D fracture simulator based on the Perkins-Kern-Nordgren 124,125 (PKN) fracture model was utilized to verify the estimates for fluid volume and proppant load. Model results indicated that in order to achieve the desired dimensions and conductivity, we must pump a total of approximately 14,000 gallons of fluid with a total proppant load of at least 35,000 lbm proppant for the main treatment. The mean sand concentration is 2.5 ppg. The fracture half-length is 210 feet, the created width is 0.12 inches and the propped width is inches. This results in a proppant concentration of only lbm/ft 2 in the fracture, however, given the reservoir's extremely low effective permeability to oil, this is all we require to meet our initial dimensionless fracture conductivity requirement of Using the same volume, at a mean sand concentration of 3.5 ppg (which roughly corresponds to a 1 5 ppg ramp), the proppant load is 49,000 lbm, C fd is 7.63, the propped width is inches and the fracture proppant concentration is 0.39 lbm/ft 2. These results are obviously idealized and a complete 3D fracture simulation should be performed on a case-by-case basis to verify the results. However, for a 200-ft designed fracture half-length, we would not recommend exceeding 50,000 lbm of proppant since it is not increasing the well's PI significantly. This proppant load is significantly less than that used on many recent fracture stimulations.

74 Pad Volume Fluid efficiencies for the Clear Fork are typically in the percent range. This corresponds to a pad volume of percent of the total job volume based on the following relationship: V pad = FE.... (7.29) 1 + FE Where FE is the fracture fluid efficiency. From field experience, we have found that increasing the pad size aids in overcoming the rapid fluid leakoff rate and unrestrained vertical height growth in the Clear Fork. A pad volume equal in size to the slurry volume (50 percent total job volume) is suggested for optimal results. This makes the total job volume for our idealized case equal to 28,000 gallons (667 bbls) of clean fluid. We would obviously like to utilize a fracturing fluid that provides perfect proppant support to achieve a well-distributed proppant pack. Under these temperature and in-situ fluid conditions, cross-linked Borate gels appear to be the most cost-effective choice. The use of enzyme-specific polymer breakers and similar "cleaner" fracturing fluids results in much better post-fracture clean up. Finding a fluid with better viscosity and proppant-carrying characteristics should also increase fracture width and improve conductivity. We should always study fracturing fluid properties closely in order to find the best compromise between viscosity and cost Recent Work Recently, the size of the fracture treatments has ranged from 15,000 gallons cross-linked borate fluid and 25,000 lbm of 16/30 Ottawa sand on new water injection wells to 70,000 gallons of 70-quality foamed CO 2 fluid and 150,000 lbm of 20/40 Ottawa sand. Resin-coated sand has been "tailed-in" on all producing wells to reduce sand flow back during production. We have utilized three-stage completion designs in an attempt to keep the treated intervals between 100 and 250 feet. It is apparent from our recent experiences that due to excessively large vertical fracture propagation, we can most likely fracture stimulate the Lower and Middle Clear Fork intervals together from one set of perforations. In the

75 376 past, it was felt the Tubb silt interval (10 30 feet thick) that carries across the NRU between the LCF and MCF provided enough hydraulic separation to inhibit vertical fracture propagation. However, we have found it provides virtually no barrier at all to upward or downward growth. The entire LCF/MCF productive interval is between 6,750 feet and 7,220 feet in most areas of the unit. Since we have achieved over 450 feet of propped vertical section on most of the recent moderate- to large-sized treatments, a combined stage could be considered on a well-by-well basis. This will of course depend on the relative locations of the "pay" quality intervals within the LCF and MCF Fracture Fluid Types We performed both CO 2 foam fracs (60 70 quality) and conventional cross-linked Borate fracs on an equal number of new wells, with fairly good results for both fluid systems. The conventional (crosslinked Borate) fracs have been flowed back immediately at 1 bpm to induce fracture closure, while the foam-treated wells have been shut-in 2 to 5 days after stimulation to allow the CO 2 to soak into the formation. The advantages of each type of fracture design are listed below.! CO 2 foam fracs Exceptionally clean fracturing fluid Results in increased relative oil permeability Induced solution gas drive reduces cleanup requirements Forms carbonic acid for near-well stimulation Reduces interfacial tension helps remove water blocks! Crosslinked Borate fracs Exceptionally clean fracturing fluid Provides low fluid loss without formation-damaging additives Possesses excellent proppant-carrying capacity Polymer-specific enzyme breakers aid in post-fracture cleanup 90 percent of original fracture conductivity retained (initially) Core studies indicate that the reservoir has mixed wettability characteristics. Some intervals are oil-wet and some are water-wet. This would seem to explain why some

76 377 operators in the area have had success using CO 2 foam fracs in the Clear Fork. A check was made of 3-month and 1-year producing rates to determine if there was a significant difference in the performance of the wells on which CO 2 foam fracs were performed and those on which conventional fracs were performed. The results are shown in Fig The CO 2 fracs performed extremely well in the northern infill area (Section 329) which is characterized by the highest reservoir quality in the unit. The crosslinked Borate fracs performed better in the southern infill areas. The average stabilized one-year rate for the CO 2 fracs was 52 STBO/day, while the wells with conventional fracs averaged 37 STBO/day. Figure 7.33 Comparison of one-year oil rate profiles for recently completed 10-acre infill wells. CO 2 fracs versus crosslinked Borate fracs.

77 378 We believe that differences in well performance are heavily influenced by the quality of reservoir rock in the areas where the wells were drilled. When we look at the projected difference in cumulative production between the two treatment types from the standpoint of initial average production rates (Fig. 7.34), we see that the incremental production attributable to a CO 2 HF treatment is 7,049 STBO/well over a two-year period. The additional income from this incremental production would be approximately 25% more than the added cost of the CO 2 treatment, however, the additional expenditures associated with post-treatment cleanup and well operation (i.e., treating for emulsions) for the CO 2 fracture treatments may cancel out the incremental production. Figure 7.34 Incremental cumulative oil production attributable to CO 2 fracs over a two-year production period.

78 379 An 80 STBO/day initial rate was used to normalize the oil production data. The cumulative production differential is calculated by integrating between the two production trends, as shown below: N p = 80 STB/day ( t) ( t) dt = 7,049 STB.... (7.30) It is our belief that future fracture stimulations should be performed using conventional fracturing fluids since we have found that the incremental income attributable to the use of energized fluids is negligible. This applies only to the Clear Fork at the NRU, for we know that CO 2 fracs have had great success in other areas Post-Fracture Evaluation Post-fracture treatment pressure transient tests performed over specific completion intervals indicate that we are obtaining fracture half-lengths between 106 and 125 feet with average pseudoradial flow skin factors of approximately -5.2 (Appendix P). Material balance decline type curve analyses performed on each new producing well after two years of production (Appendix L) indicated that the average fracture halflength was slightly over 100 feet and that the average near-wellbore skin factor was approximately These results are in agreement with the pressure transient results and indicate an improvement over the average stimulation characteristics of the 20-acre wells (Appendix K) and 40-acre wells (Appendix J), however, they still do not meet our design criteria for NRU fracture treatments NRU Fracture Diagnostic Examples Shown below, in Figs , are plots of closure pressure and net pressure for Upper, Middle and Lower Clear Fork fracture treatments in three 10-acre infill wells. Net pressure is defined as the difference between the bottomhole treating (injecting) pressure and the fracture closure pressure. This is basically the pressure required to keep

79 380 the fracture open and propagating after fracture initiation. It is highly dependent on the degree of near-wellbore tortuosity and the amount of perforation entry-hole friction. We take particular note of the response on the net pressure plot for NRU 3532 (Fig. 7.38) associated with unconfined vertical height growth. We see this characteristic shape on more than half of the recent fracture stimulations. This is due to the lack of barriers to vertical fracture propagation and to the excessive treatment volumes. NRU 1510(WI) received a smaller treatment since it was felt that continuous water injection would supply additional fracture extension. We see in Fig. 7.36, that when smaller treatments are performed the fracture profile indicates smooth fracture extension and no extensive vertical growth out of the completed interval. These smaller jobs were attempted on some of the newer injection wells in an effort to target water injection in specific unsupported intervals. In this case, the total job volume was actually too small approximately half the volume and one-quarter the proppant required for the "ideal" NRU stimulation treatment described in Section 7.8.1, and the interval had to be restimulated to accept injected water. We can also see the affect that limited-entry perforating has on the net pressure profile. Intermittent and sharp pressure increases during the early stages of treatments are related to isolated perforation intervals (typically the bottom set) screening out at the wellbore (Fig and Fig. 7.40). This reinforces our belief that cluster perforating is the proper technique to use in the Clear Fork at the NRU. We also find that the magnitude of the net pressure indicates that the pre-fracturing ball-out acid jobs are reducing perforation friction and eliminating near-wellbore tortuosity. The pertinent data for the three fracture treatments shown are summarized below. NRU 1510 (WI) Upper Clear Fork (Figs ): Maximum injection rate = 22.1 bpm Maximum surface-treating pressure = 2,906 psia Total job volume (with pad) = 14,472 gallons Max sand concentration = 8.50 ppg (ramp 2 to 8 ppg) Average sand concentration = 4.45 ppg Total proppant (20/40 sand) = 24,384 lbm

80 381 NRU 3532 Middle Clear Fork (Figs ): Maximum injection rate = 45.5 bpm Maximum surface-treating pressure = 3,666 psia Total job volume (with pad) = 34,986 gallons Maximum sand concentration = 6.32 ppg (ramp 1 to 6 ppg) Average sand concentration = 3.16 ppg Total proppant (20/40 sand) = 65,722 lbm NRU 2705 Lower Clear Fork (Figs ): Maximum injection rate = 31.3 bpm Maximum surface-treating pressure = 6,257 psia Total job volume (with pad) = 28,770 gallons Maximum sand concentration = 6.17 ppg (ramp 1 to 6 ppg) Average sand concentration = 3.44 ppg Total proppant (20/40 sand) = 40,871 lbm Figure 7.35 Closure stress calculation for NRU 1510(WI) Upper Clear Fork.

81 382 Figure 7.36 Net pressure profile for NRU 1510(WI) Upper Clear Fork. Figure 7.37 Closure stress calculation for NRU 3532 Middle Clear Fork.

82 383 Figure 7.38 Net pressure profile for NRU 3532 Middle Clear Fork. Figure 7.39 Closure stress calculation for NRU 2705 Lower Clear Fork.

83 384 Figure 7.40 Net pressure profile for NRU 2705 Lower Clear Fork Case Study 131 Surface and downhole tiltmeters 132 were utilized to measure hydraulic fracture-induced rock deformation on several fracture treatments in two NRU 10-acre infill wells, NRU 1514(WI) and NRU 3019(WI). Surface tiltmeters are used to measure fracture azimuth, dip and depth to the fracture center. Downhole tiltmeters are utilized to measure fracture geometry (vertical height growth and half-length). Net pressure modeling was performed in real-time in order to re-design the fracture treatment to produce optimal results. Based on the results of this study, we were able to model the orientation of hydraulically propagated fractures, quantify key input parameters for HF design and evaluate different perforating strategies. Quantifying fracture orientation is considered extremely important at the NRU because injection wells are oriented in a line-drive pattern for maximum support. Preferential fracture orientation was previously identified from pressure transient analyses and

84 385 decline type curve analysis of long-term production and injection data. We felt that variations in localized (individual well) pore pressure gradients due to long-term water injection might have an affect on hydraulically induced fractures at the NRU. The fracture treatments were pumped at barrels/minute (bpm) using an average of 28,700 gallons of crosslinked gel (25 lbm/1,000 gallons) with proppant densities ranging between 2 8 ppg. The average total proppant load was approximately 73,000 lbm. The pad volume was increased to account for the rapid fluid leakoff Fracture Azimuth and Dip The findings for fracture azimuth and dip confirmed previous results. The mean fracture azimuth determined for these treatments was N83 o E +7 o, or roughly east-west. The mean fracture dip was 86 o +5 o, or very close to exactly vertical in every case Fracture Geometry Two fracturing stages on NRU 1514(WI) were monitored with downhole tiltmeter tools. The processing of downhole tiltmeter data indicated that the mean propped fracture height for these treatments was 455 feet +70 and the mean propped fracture half-length was 315 feet Real-Time Net Pressure Modeling The match of field net pressure data indicated that the fractures at the NRU were indeed growing radially. Closure pressures were obtained during diagnostic tests prior to the main job in order to calculate the fracture gradient and leakoff coefficient. Step-down tests indicated that there was very little near-wellbore tortuosity or perforation friction for any of the treated intervals. The average net pressure for all jobs was only 250 psi. The mean created fracture half-length was estimated to be 246 feet and the mean propped half-length was 203 ft. The mean created and propped fracture heights were 532 feet and 439 feet, respectively. The average closure stress (fracture) gradient 0.72 psi/ft, with little variation. The mean leakoff coefficient was ft/min 1/2 and varied between and The

85 386 average fluid efficiency for the main treatments was 49 percent, while the mean FE for the minifrac diagnostic tests was 56 percent. The average proppant concentration in the fracture at closure was found to be 0.46 lbm/ft 2 and varied between 0.30 and These findings confirmed previous theories regarding hydraulic fracture propagation in the Clear Fork at the NRU, particularly unconfined vertical growth. Questions were raised concerning the degree of fracture conductivity, however, we have shown above that these levels (0.46 lbm.ft 2 ) exceed our fracture conductivity requirements. We feel that the total proppant load can still be reduced to 50,000 lbm without decreasing the well's potential Summary We believe that many recent fracture treatments have been over-designed with respect to fluid volumes and half-lengths. The average hydraulic fracture treatment over the life of the NRU has averaged 54,000 gallons of fluid and 120,000 lbm of proppant. We believe that the optimum job size is roughly half of the historical average. Although not obvious from post-fracture tracer surveys, which have a very limited depth of investigation, it is apparent that individual stages are overlapping and money is being wasted. Wells should be evaluated on a case-by-case basis to determine if the primary Clear Fork intervals can be effectively fracture-treated together. We believe fracture stimulations consisting of approximately 28,000 gallons total clean fluid (using a Boratecrosslinked system) and 50,000 lbm of 20/40 Ottawa sand are ideal for the NRU, and will provide the required fracture conductivity. In addition, the highest quality reservoir "pay" section should be identified using the techniques provided in Chapter IV for each treatment interval and then cluster perforated at a density of 4 5 shots/ft over a ft interval. This will decrease the chance of individual sets of perfs screening out early which results in uneven coverage of the reservoir, and will also reduce the chances of propagating multiple fractures. Prefracturing ball-out acid jobs should be continued since they have resulted in almost complete removal of near-wellbore tortuosity prior to pumping the fracture stimulation.

86 387 CHAPTER VIII DATA INTEGRATION Reservoir performance can be predicted from a geological, geophysical, petrophysical or reservoir engineering standpoint, however, all analyses must be integrated to get the clearest possible picture of key reservoir performance attributes. The primary goal of this study was to develop a cost-effective, integrated reservoir description for "targeted" 10-acre infill drilling and future recovery operations in a low permeability carbonate reservoir. Integration of geological and petrophysical studies with production and injection data analyses and pressure transient analyses has provided a rapid and effective method for developing a comprehensive reservoir description. This description can be used for reservoir flow simulation, performance prediction, infill well targeting, waterflood management and for optimizing well developments (patterns, completions and stimulations). Maps of historical production characteristics (contacted oil-in-place, estimated ultimate recovery and reservoir pressure) have been compared to maps generated from the geologic studies (rock type, permeability-thickness, and hydrocarbon pore volume) to identify the areas of the unit to be targeted for infill drilling. From our comparison of geological and petrophysical parameters with historical production performance, we believe that the producing characteristics of individual wells are entirely a function of localized reservoir quality. Whether this can be attributed to a particular depositional environment, rock fabric, or in our case rock type, is certainly open to debate since we could find no direct correlation between reservoir performance and the rock types defined in Chapter IV. 8.1 Identification of Interwell Reservoir Quality Trends We can see that the reservoir depletes and re-pressures as a function of reservoir quality throughout all areas of the unit. This is illustrated in Figs , where we have

87 388 mapped the results of our core-log modeling (on approximately sixty 10- and 20-acre infill wells) for oil flow capacity, k o h, and hydrocarbon pore volume, φhs o. Based on these results, we feel that the reservoir "sweet spots" within the NRU are in the northwest (Section 329) and southeast sections (Section 5) of the unit, with isolated high-quality reservoir also found on the east (Section 292) and southwest (Section 362) flanks of the unit. Figure 8.1 Estimated oil flow capacity, k o h, calculated from core-log model for the NRU 10- and 20-acre infill wells. Contour interval = 25.0 md-feet.

88 389 Figure 8.2 Estimated net hydrocarbon-feet, φhs o, calculated from core-log model for the NRU 10- and 20-acre infill wells. Contour interval = 2.0 feet. If we examine historical performance trends in the form of the results from our material balance decline type curve analyses performed on the original 40-acre development wells (Figs ), we see that the results of the geological-petrophysical modeling are confirmed. The highest contacted original in-place oil (OOIP) accumulations are in the northwestern and southeastern areas of the NRU. However, there are also isolated areas of prolific production in the same areas identified by Figs , above. In fact the entire east flank of the unit performed extremely well during the primary production period. These production trends coincide with the oil flow capacities calculated from the transient stem match on the decline type curve (Fig. 8.5) and the core-log flow capacity shown in Fig. 8.2.

89 390 Figure 8.3 "Contacted" original oil-in-place calculated from decline type curve analyses on the NRU original 40-acre development wells. Contour interval = 1.0 MMSTBO. The total OOIP for the NRU was calculated to be approximately 215 MMSTBO from material balance decline type curve analyses on individual 40-acre development wells.

90 Figure 8.4 Estimated ultimate recovery calculated from the NRU original 40-acre development wells. Contour interval = 70.0 MSTBO. 391

91 392 Figure 8.5 Estimated oil flow capacity, k o h, calculated from decline type curve analyses on the NRU original 40-acre development wells. Contour interval = 10.0 md-feet. During secondary production, represented by the 20-acre infill well production trends shown in Figs , we see that the northwest region of the unit continued to perform well, however, performance on the east flank was somewhat diminished. We note that the original discovery wells were on the east flank of the unit, and that these wells had much higher producing water cuts at the time of unitization. As we found out in from our interwell tracer studies (Chapter VI), it is also possible that due to the complex reservoir flow processes that are characteristic of the Clear Fork at the NRU, that oil from the east flank migrated in to the south-central portion of the unit. This might explain the increased secondary recovery in an area of the unit with relatively lower reservoir quality.

92 Figure 8.6 Estimated ultimate recovery calculated from the NRU 20-acre infill wells. Contour interval = 70.0 MSTBO. 393

93 394 Figure 8.7 Estimated oil flow capacity, k o h, calculated from decline type curve analyses on the NRU 20-acre infill wells. Contour interval = 10.0 md-feet. However, such a development is not confirmed by the 1988 average reservoir pressure map generated from pressure transient test data taken coincident with the start of the waterflood (Fig. 8.8). The reservoir pressure trend at that time indicated that the east flank of the unit was the most pressure depleted area, therefore, we would expect any migration of oil to be toward this pressure sink and not away from it. The central and south-central regions of the unit were not as pressure-depleted since they had relatively lower primary production totals and the reservoir quality in these areas is for the most part fairly poor.

94 395 Figure estimated average reservoir pressure from pressure transient tests. Contour interval = psia. The 1995 average reservoir pressure map (Fig. 8.9) indicates that the reservoir repressured itself just as it depleted based on the underlying geology. Fluid withdrawals are higher in the more prolific areas, therefore, the reservoir pressure is relatively lower. We note that the average pressure on the southwest flank of the field is indicative of a high fluid withdrawal rate. This confirms the selection of this area of the unit (above) as an additional reservoir "sweet spot" which should also be investigated for infill drilling opportunities.

95 396 Figure estimated average reservoir pressure from pressure transient tests. Contour interval = psia. Pressure depletion appears to have been a function of local reservoir quality, and hence, the underlying geologic footprint. The more prolific producing areas have a different dominant depositional environment (higher energy deposits) than do the lower quality areas. However, there are isolated patch reefs and debris aprons scattered across the unit. We know from whole core acquired in NRU 1510(WI) that patch reefs do exist in less prolific producing areas (i.e., central and southern) of the unit. This could account for the extremely favorable waterflood response in the south-central section of the unit, since the debris aprons and shoals around these reefs typically have good reservoir quality.

96 397 Using our idealized illustration of depositional environment from Chapter II, we see that the higher reservoir quality areas to the northwest and southeast coincide with the occurrence of reef environments with associated reef talus, reef debris aprons and fusulinid shoals, as shown once again in Figure 8.10, below. Island Complex Pond Island Beach NORTH Tidal Flat Restricted Restricted Lagoon Lagoon Algal Mat Open Open Lagoon Lagoon Bioherm Reef Talus Reef Shelf Shelf Debris Apron Fusulinid Shoal Figure 8.10 Idealized illustration of Clear Fork depositional environment Identification of Intrawell Reservoir Quality Trends Now that we have identified the areas of the unit where we need to concentrate our development efforts, we need to determine if individual well recovery predictions can be made using the results of our core-log modeling work. The core-log modeling results are combined with the results from our material balance decline type curve analyses and individual well initial potential rates (IP) in Tables , for selected 10-acre and 20- acre infill wells (i.e., wells for which we had open-hole well log data).

97 398 Table 8.1 Results for 10-acre infill well core-log modeling. net pay k o h φh φhs o ave. φ ave. S wi RT1 RT1-2 RT6 Well (feet) (feet) (feet) (feet) (v/v) (v/v) (frac.) (frac.) (frac.) Ave.: Table 8.2 Results for 10-acre infill well decline type curve analyses. 90-day IP EUR OOIP k o h Area Rec. Fac. Well (STB/day) (STB) (MSTB) (feet) (acres) (frac.) Ave.: Table 8.3 Results for 20-acre infill well core-log modeling. net pay k o h φh φhs o ave. φ ave. S wi RT1 RT1-2 RT6 Well (feet) (feet) (feet) (feet) (v/v) (v/v) (frac.) (frac.) (frac.)

98 399 Table 8.3 (Continued). net pay k o h φh φhs o ave. φ ave. S wi RT1 RT1-2 RT6 Well (feet) (feet) (feet) (feet) (v/v) (v/v) (frac.) (frac.) (frac.) Ave.: Table 8.4 Results for 20-acre infill well decline type curve analyses. 90-day IP EUR OOIP k o h Area Rec. Fac. Well (STB/day) (STB) (MSTB) (feet) (acres) (frac.)

99 400 Table 8.4 (Continued). 90-day IP EUR OOIP k o h Area Rec. Fac. Well (STB/day) (STB) (MSTB) (feet) (acres) (frac.) Ave.: Notes: frac. = fraction ave. = average v = volume Rec. Fac. = recovery factor Utilizing these results, we found that there is some direct correlation between net pay, oil flow capacity, hydrocarbon pore volume and rock type with estimated ultimate recovery, EUR. The most promising predictor variables are shown in Figs , below.

100 Figure 8.11 Correlation between infill well EUR and core-log model predicted net pay thickness. 401

101 Figure 8.12 Correlation between infill well EUR and core-log model predicted hydrocarbon pore volume. 402

102 Figure 8.13 Correlation between infill well EUR and core-log model predicted oil flow capacity. 403

103 404 Figure 8.14 Correlation between infill well EUR and core-log model predicted rock type 6 (anhydritic dolomite). We developed non-parametric 71 predictive models for the 10-acre and 20-acre wells separately, and also developed a combined model. Results were very good for the 10- acre well data, for which we had early estimates of EUR from initial rate declines on new wells, and only adequate for the 20-acre infill wells which are reaching their final stage of depletion. This is most likely due to the use of 10-acre well log and core data in

104 405 the construction of the core-log model. Since whole core data from the 20-acre wells was extremely questionable, the core-log model developed in Chapter IV was constructed using primarily 10-acre well data. The predictive model equations are given in Figs , below. The comparison between model-predicted EUR and actual EUR from initial rate declines on the 10-acre infill wells is shown in Fig We note that 10-acre well ultimate recoveries can be predicted quite accurately using the results of our geologicalpetrophysical modeling. Figure 8.15 Predictive equation for EUR using core-log model outputs as predictor variables 10-acre infill wells.

105 406 Figure 8.16 Predictive equation for EUR using core-log model outputs as predictor variables 20-acre infill wells. Figure 8.17 Predictive equation for EUR using core-log model outputs as predictor variables 10-acre and 20-acre infill wells.

106 407 Figure 8.18 Comparison between model-predicted EUR and actual EUR for 10-acre infill wells. Using the results of our geological, petrophysical and reservoir engineering analyses, we can now identify favorable infill drilling locations and predict individual well performance a priori using conventional well log data. In short, a comprehensive analysis, interpretation and prediction of well and field performance can be completed quickly, and at a minimal cost. These analyses can be used to directly improve our understanding of reservoir structure and performance behavior in complex formations.

107 Summary We have identified areas of the unit with additional reserves potential (accelerated or incremental), as well as those areas in which infill drilling is not likely to be economic. We believe that uniform infill drilling is neither prudent, nor warranted, given the stratigraphic compartmentalization and irregular permeability distributions in this reservoir. We will target areas of the field that have favorable geologic characteristics, as identified in Chapter II. We will also look for areas with good oil flow capacity, large hydrocarbon pore volume and a high volume percentage of rock types 1, 2 and 6 as identified by the core-log model. In addition, we will consider areas of the unit with favorable primary and secondary recovery characteristics as identified from material balance decline type curve analyses and areas of the unit that are at a relatively lower average reservoir pressure, since high fluid withdrawal is directly related to rock quality.

108 409 CHAPTER IX 10-ACRE INFILL WELL PERFORMANCE Eighteen 10-acre infill wells (14 producers and 4 injectors) were drilled in 1996 and 1997 in order to confirm the geologic and engineering work performed for this project. In order to verify our reservoir characterization studies, new wells were drilled in both high and low quality reservoir areas of the unit. Wells in the northern area (Section 329) were drilled in an area possessing high reservoir quality (higher energy depositional environment) and good interwell continuity. Wells in the southern infill area (Sections 326 and 327) were drilled in an area of the unit possessing lower quality reservoir (low energy environment with mud- and siltdominated rock). An additional well was drilled in the southwest corner of the unit in an area identified as having fairly good reservoir quality based on our pervious analyses. These 10-acre infill drilling locations are shaded on the unit map shown in Figure 9.1. We knew initially, that some of the wells drilled in the south-central area of the unit were not ideally located. However, this area was drilled in order to investigate a region of the unit that performed extremely well under water injection. We have found that additional infill wells should most likely only be drilled in the higher quality areas of the unit. From Chapter VIII, we know that these will be the northwest (Section 329), the east (Section 5) and the southwest (Section 362) areas of the NRU.

109 410 Figure 9.1 Initial 10-acre infill drilling locations ( ). Early stabilized production rates from the 14 producing wells were extremely encouraging. Total unit production was increased from approximately 2,750 STBO/day to 3,650 STBO/day, with a peak rate of over 4,000 STB/day. The initial oil and water rate increases are shown in Fig. 9.2.

110 411 Figure 9.2 Unit oil rate increase attributable to initial 10-acre infill wells. 9.1 Initial Production Rate Comparison The initial average production rates for the new 10-acre infill wells are compared against results obtained in previous development drilling campaigns in Figs The average three-month IP for the new wells in the northern infill area are two to three times greater than the IPs from the previous drilling and completion programs at the NRU. The wells in the lower reservoir quality southern infill area have 3-month stabilized IPs similar to those for the original 40-acre development wells, however, in both cases, the 10-acre wells decline more rapidly after the initial 90-day period. The improvement over 20-acre well performance could be related to targeting drilling locations in areas of high reservoir quality and improved completion and stimulation techniques. However, it is likely due to reservoir pressure changes that have occurred throughout the unit's productive history. The 20-acre wells were the poorest performers,

111 412 but were drilled at a time when the reservoir was pressure depleted. The pressure transient test and FT results summarized in Chapter VI indicate that the current reservoir pressure is at or above the original pressure in most Clear Fork intervals. Figure 9.3 First year initial oil rates for 40-, 20- and 10-acre wells in the northern infill area.

112 413 Figure 9.4 First year initial oil rates for 40-, 20- and 10-acre wells in the southern infill area. 9.2 Incremental versus Accelerated Reserves Early results indicate that more than 50 percent of the production from the new infill wells is accelerated production of existing reserves. On an individual well basis, most of the additional production in the northern infill area appears to be due to acceleration of existing reserves, while most of the additional production in the southern infill area appears to be incremental. These trends were predicted prior to drilling on the basis of differing depositional environments and obvious reservoir quality contrasts that define the two areas. The northern infill area is dominated by grainstone shoal facies with fairly good permeability and porosity characteristics. Lagoonal facies with good storage capacity (porosity), but relatively lower permeability and connectivity dominate the southern infill area.

113 414 We believe that reservoir continuity is far better than originally supposed. This indicates that reduction to 10-acre spacing in the high quality reservoir areas of the field may not be necessary for optimal reservoir depletion. An illustration of rate acceleration is shown for NRU 3604 (Fig. 9.5), in which we see that production from offset well NRU 3528 decreased as NRU 3604 was put on production. Figure 9.5 Production interference effect caused by NRU 3604 infill well. 9.3 Pressure Transient (Interval) Tests Short-term pressure drawdown tests were used to measure formation flow characteristics in the new producing wells. We recorded drawdown rather than buildup tests to avoid shutting in recently completed wells. These tests were recorded over individual completion intervals (i.e., Lower, Middle, or Upper Clear Fork), and were used to

114 415 estimate the completion efficiency and the relative contribution of each zone to total production. This information should be utilized to perform future injection well profile modifications. We have found that the hydraulic fracture jobs have been successful and are producing fractures with half-lengths on the order of 105 feet (pseudoradial skin factor = -5.1). A log-log plot summarizing the analysis results for the NRU 3532 Middle Clear Fork pressure drawdown test is shown in Fig All transient test analyses are summarized in Appendix P. Figure 9.6 Final data match on log-log plot for NRU 3532 (Middle Clear Fork) pressure drawdown test data (May 1996).

115 416 Based on the detailed visual description of slabbed whole core and core-log modeling work summarized in Chapter IV, we identified specific reservoir intervals in which to focus our completion and stimulation strategies. The discrete productive intervals that we focused on during completion include:! Lower Clear Fork +7,000-7,180 feet! Middle Clear Fork +6,770-6,900 feet! Upper Clear Fork +6,160-6,250 feet +6,350-6,500 feet The interval well test results indicate that the Middle Clear Fork interval is a much more significant contributor to total production than was previously thought. We note that this interval is typically under-supported by water injection. Each Clear Fork interval's approximate contribution to total oil production is shown in Table 9.1, below. Table 9.1 Relative production contribution of individual Clear Fork intervals. South North Interval Contribution to production (percent) Contribution to production (percent) Upper Clear Fork Middle Clear Fork Lower Clear Fork This information, together with newly acquired core and log data, will allow us to target our completion intervals much more effectively at the NRU. 9.4 Production and Injection Data Analysis Material balance decline type curve analyses performed on the new producing wells in January 1999 (Appendix L) indicated that the average per well recovery would be approximately 82.2 MSTB. The results are summarized in Table 9.2, below. The wells drilled in the higher quality reservoir regions had an average EUR of 98 MSTB, which is higher than the average estimated recovery for the 20-acre infill wells (83 MSTBO) drilled between 1987 and The wells drilled in the areas of relatively

116 417 lower reservoir quality have an average EUR of 64.6 MSTBO, which will be marginally economic. Stabilized three-month initial potential rates (IPs) for the wells in high quality areas averaged 75 STBO/day, while those in the low quality areas averaged 57 STBO/day. In addition, NRU 3319, the lone well drilled on the southwest flank of the unit, IP'd at 125 STBO/day and has an EUR of 125 MSTBO. These results validate our reservoir characterization studies, and future wells should only be targeted to the higher quality areas of the unit. Initial results for the 10-acre infill wells are summarized below. Table 9.2 Material balance decline type curve analysis results for 10-acre infill wells. Est. Sec. EUR Sec. Rec. Fact. Drainage Skin OOIP calc. area r e r ed x f Factor k o h Well (STB) (STB) (percent) (acres) (feet) (dim.) (feet) (dim.) (md-ft) E E E E E E E E E E E E E E total: 1.376E E+06 ave.: 983,059 82, Notes: 10-acre infill wells drilled φ C and S wi from core-log model results dim. = dimensionless sec. = secondary v = volume B o = RB/STB µ o = 2.0 cp r w = feet c t = 20x10-6 psi Producing Wells NRU 505 The type curve match indicates that NRU 505 will be an average 10-acre producer and is well stimulated. The oil rate and GOR (gas-oil ratio) profiles (Fig. 9.7) indicate that the well is receiving little or no early injection support. This well appears

117 418 to have adversely affected oil production in the east and west offset wells (Fig. 9.8), however, total fluid production for the area has increased with the additional well. If or when the well receives injection support, oil reserves could increase significantly. The current and average water cuts for this area are favorable. Figure 9.7 Initial rate profile for NRU 505.

118 419 Figure 9.8 Production interference effect due to addition of NRU 505. NRU 1509 The type curve match indicates that NRU 1509 will be a below-average 10- acre producer primarily because it is poorly stimulated (LCF frac job screened out). The oil rate and GOR profiles (Fig. 9.9) indicate that the well may be receiving limited injection support. This well appears to have adversely affected oil production in the east and west offset wells (Fig. 9.10), however, total fluid production for the area has remained fairly constant. If the LCF is re-stimulated, this well's performance could be improved and oil reserves could increase. The current and average water cuts for this area are extremely favorable.

119 420 Figure 9.9 Initial rate profile for NRU Figure 9.10 Production interference effect due to addition of NRU 1509.

120 421 NRU 1511 The type curve match indicates that NRU 1511 will be an exceptional 10- acre producer and is extremely well stimulated. The oil rate and GOR profiles (Fig. 9.11) indicate that the well may be producing from a previously uncontacted region. The GOR and water cut are low, but it does not appear that the well is receiving extensive injection support. This well appears to have adversely affected oil production in the east offset well (NRU 1506, Fig. 9.12), however, total fluid production for the area has increased significantly with the additional well. If or when the well receives injection support, oil reserves should be increased. Figure 9.11 Initial rate profile for NRU 1511.

121 422 Figure 9.12 Production interference effect due to addition of NRU NRU 2705 The type curve match indicates that NRU 2705 will be an average to below-average 10-acre producer and is not well stimulated. The oil rate and GOR profiles (Fig. 9.13) indicate that the well may be receiving limited injection support as both the total rates and GOR have remained fairly constant. A major parameter controlling secondary well performance is contacted reservoir pore volume. The major obstacle in this area is that due to relatively lower pore volumes, even if the well is receiving pressure support, the total producing rates will be rather low. If or when the well receives injection support, oil reserves may increase. The current and average water cuts for the area are average to above average.

122 423 Figure 9.13 Initial rate profile for NRU NRU 3017 The type curve match indicates that NRU 3017 will be an average to below average 10-acre producer and is not well stimulated. The oil rate and GOR profiles (Fig. 9.14) indicate that the well is receiving limited injection support as both the total rates and GOR have remained fairly constant. As for the case of NRU 2705, the major obstacle in this area is that due to relatively lower pore volumes, even if the well is receiving pressure support, the total producing rates will be rather low. If or when the well receives injection support, oil reserves should increase. The current and average water cuts for the area are extremely low indicating that injection support could significantly increase reserves, however, the reservoir quality may not be sufficient to supply the necessary injector-producer continuity.

123 424 Figure 9.14 Initial rate profile for NRU NRU 3018 The type curve match indicates that NRU 3018 will be a very good 10-acre producer and is well stimulated. The oil rate and GOR profiles (Fig. 9.15) indicate that the well is just beginning to see some limited injection support. If the well receives significant injection support, oil reserves may increase significantly. The well is located in an area with relatively favorable reservoir quality.

124 425 Figure 9.15 Initial rate profile for NRU NRU 3319 The type curve match indicates that NRU 3319 will be a very good 20-acre producer and is well stimulated. The well was drilled in an open location on the southwest flank of the unit. The oil rate, water cut and GOR profiles (Fig. 9.16) indicate that the well has yet to see injection support. If the well receives significant injection support, oil reserves should increase significantly.

125 426 Figure 9.16 Initial rate profile for NRU NRU 3532 The type curve match indicates that NRU 3532 will be an exceptional 10- acre producer and is fairly well stimulated. The oil rate and GOR profiles (Fig. 9.17) indicate that the well is receiving injection support early in its producing life. Oil reserves may be increased significantly over time since the well is in a region with above average reservoir quality and reservoir pore volume.

126 427 Figure 9.17 Initial rate profile for NRU NRU 3533 The type curve match indicates that NRU 3533 will be a very good 10-acre producer and is very well stimulated. The oil rate and GOR profiles (Fig. 9.18) indicate that the well is receiving moderate injection support or could be producing from a previously uncontacted region. This well has not significantly affected the performance of either the east or west offset (Fig. 9.19) and the total fluid production rate has remained fairly constant. If or when the well receives significant injection support, oil reserves should increase since the well is in a region with above average reservoir quality and reservoir pore volume.

127 428 Figure 9.18 Initial rate profile for NRU Figure 9.19 Production interference effect due to addition of NRU 3533.

128 429 NRU 3534 The type curve match indicates that NRU 3534 will be a below-average 10- acre producer although it is fairly well stimulated. The oil rate and GOR profiles (Fig. 9.20) indicate that the well has yet to see significant injection support, although the initial water rate was fairly high. The GOR has been steadily increasing since initial completion, and the well appears to be contacting a very limited reservoir area. If or when the well receives improved injection support, oil reserves could increase moderately since the well is in a region with above average reservoir quality and reservoir pore volume. Figure 9.20 Initial rate profile for NRU 3534.

129 430 NRU 3535 The type curve match indicates that NRU 3535 will be an exceptional 10- acre producer and is well stimulated. The oil rate and GOR profiles (Fig. 9.21) indicate that the well is beginning to receive fairly good injection support. This well has not significantly affected the performance of either the east or west offset (Fig. 9.22). The total fluid production rate for the pattern has declined over the past two years but appears to be increasing again at this point in time. Oil reserves should increase since the well is in a region with above average reservoir quality and reservoir pore volume. Figure 9.21 Initial rate profile for NRU 3535.

130 431 Figure 9.22 Production interference effect due to addition of NRU NRU 3537 The type curve match indicates that NRU 3537 will be a very good 10-acre producer but does not appear to be well stimulated. Problems encountered during fracture stimulation may have been caused by significant water movement (i.e., high pore pressures) that exists in this region, hence the rather high water cut. However, this also indicates that the well received strong initial water injection support, as evidenced by the low and stable initial GOR (Fig. 9.23). Oil reserves should increase with time since the well is in a region with above average reservoir quality and reservoir pore volume.

131 432 Figure 9.23 Initial rate profile for NRU NRU 3538 The type curve match indicates that NRU 3538 will be an average 10-acre producer but does not appear to be well stimulated. The significant water volume that exists in this region may have caused problems encountered during fracture stimulation. The oil rate and GOR profiles (Fig. 9.24) indicate that the well is beginning to receive fairly good injection support. Although the oil rate fell of rapidly after completion, the GOR is beginning to decrease and the oil rate is increasing. The addition of this well did not appear to have a significant affect on either of the offset producers (Fig. 9.25), and the total fluid withdrawal rate has been increased. Oil reserves may increase slightly with time since the well is in a region with above average reservoir quality and reservoir pore volume.

132 433 Figure 9.24 Initial rate profile for NRU Figure 9.25 Production interference effect due to addition of NRU 3538.

133 434 NRU 3604 The type curve match indicates that NRU 3604 will be an excellent 10-acre producer and that the well is moderately stimulated. The oil rate and GOR profiles (Fig. 9.26) indicate that the well appears to have been producing from a previously uncontacted region but has yet to receive significant injection support. The addition of this well did appear to have a significant affect on the west offset producer (NRU 3528, Fig. 9.27), however, total fluid withdrawal rate has been increased. Oil reserves may increase slightly with time since the well is in a region with above average reservoir quality and reservoir pore volume. Figure 9.26 Initial rate profile for NRU 3604.

134 435 Figure 9.27 Production interference effect due to addition of NRU Water Injection Wells NRU 1510 (WI) The initial type curve match indicates that NRU 1510 may be a poor 10-acre water injector and is not very well stimulated (small initial frac jobs). However, the addition of this well does not appear to have adversely affected water injection in the east and west offset wells (Fig. 9.28), and total water injection for the area is steadily increasing. The type curve matches do not show any of the near-offset injection wells to be in direct pressure communication via hydraulically-induced fractures.

135 436 Figure 9.28 Injection interference effect due to addition of NRU 1510(WI). NRU 1512 (WI) A type curve match could not be produced for NRU 1512 since it has not been possible to initiate injection in the well. Although type curve matches on the east and west offset injection wells do not indicate extensive fracture half-lengths, it is apparent that a problem exists. A 1996 injection profile/temperature survey indicated that all water injected into NRU 1593 is entering the Middle Clear Fork, therefore it is possible that a communication problem could exist between 6,800 feet and 7,000 feet. The way in which the injection rate profiles for NRU 1504 and NRU 1593 (Fig. 9.29) mirror one another also indicates a communication problem. The inability to maintain injection in NRU 1512 appears to be adversely affected by pressure communication with offset wells rather than by poor fracture stimulation.

136 437 Figure 9.29 Injection interference effect due to addition of NRU 1512(WI). NRU 3536 (WI) The type curve match indicates that NRU 3536 is very well stimulated, but that it has already felt the affect of a major flow boundary. This well appears to be in direct communication with the west offset injector (NRU 3511, Fig. 9.30), and this may be the reason why injectivity can not be maintained. A pressure falloff test performed on NRU 3511 (Appendix P) indicated rapid growth of hydraulic fractures due to long-term water injection. This was confirmed by decline type curve analysis (Appendix M). Injection profiles and Hall plots for NRU 3511 indicate normal stable injection, which contradicts the pressure transient and material balance analyses. Total water injection for the area decreased when injection was initiated in NRU 3536, therefore, we believe that the new well is communicating with one of its offsets.

137 438 Figure 9.30 Injection interference effect due to addition of NRU 3536(WI). NRU 3539 (WI) The type curve match indicates that NRU 3539 is very well stimulated and has been able to maintain fairly good injectivity. This well may also be in communication with NRU 3511 (Fig. 9.31), although the total injectivity for this group of wells has been maintained. We note once again that previous pressure falloff tests recorded on wells 3510 and 3511 indicated extensive fracturing and possible hydraulic communication, therefore these wells need to be monitored closely.

138 439 Figure 9.31 Injection interference effect due to addition of NRU 3539(WI). 9.5 Summary Infill drilling should be restricted to areas of the field that possess large volume percentages of rock types 1, 2 and 6, good effective oil permeability, large hydrocarbon pore volume and are located in areas with high fluid withdrawal rates. The core-log model results indicate that the quality of the reservoir rock in the 10-acre infill wells (based on net pay, oil flow capacity and hydrocarbon pore volume) is superior to that found in the 20-acre infill wells. This indicates that targeting infill wells is both a valid and economically sound method for field development. If the productive intervals within the reservoir can be better defined based on detailed core-log modeling, then initial production rates and ultimate recoveries can be improved through the optimization of completion and stimulation practices. We know that

139 440 individual well recoveries are primarily a function of local reservoir quality, however, recoveries are also directly related to the quality of the initial completion, as represented by each well's IP. Fig illustrates the relationship between IP and EUR for the 10- acre infill wells. Figure 9.32 Relationship between IP and EUR for 10-acre infill wells.

140 441 CHAPTER X SUMMARY AND CONCLUSIONS We have used a multi-disciplinary approach employing geology, petrophysics and engineering to conduct advanced reservoir characterization and management activities to design and implement an optimized infill drilling program at the North Robertson (Clear Fork) Unit in Gaines County, Texas. Specific reservoir surveillance activities were identified and tested. A geologically-targeted infill drilling program was implemented as a result of this work. A significant contribution of this work is to demonstrate the use of cost-effective reservoir characterization and management tools that will be helpful to both independent and major operators for the optimal development of heterogeneous, low permeability shallow-shelf carbonate (SSC) reservoirs. The techniques that are outlined for the formulation of an integrated reservoir description apply to all oil and gas reservoirs, but are specifically tailored for use in the heterogeneous, low permeability carbonate reservoirs of West Texas. The overall thrust of this project has been geologically targeted infill drilling. Blanket drilling in shallow-shelf carbonate (SSC) reservoirs is neither prudent nor warranted with the modern reservoir characterization tools and techniques available to operators. The key is reservoir characterization. Operators need to recognize its importance and how it can help in optimizing and maximizing recovery economics. We have learned much about this reservoir that may be put in to practice in the future. We have a better definition of what "pay" actually is from the geologic and petrophysical studies that were performed. We have tools in-hand to analyze the long term production and injection trends in order to identify the best areas of the field for development and workover opportunities. We also have a much better idea of what is required from a completion and stimulation standpoint. The major conclusions of this work are summarized below.

141 Geology and Petrophysics 1. The location of reservoir "sweet spots" is completely a function of the underlying geology. The reservoir depletes and re-pressures as a function of the underlying geology. This includes both the original depositional environment and any subsequent post-depositional diagenetic processes. 2. We can not find a direct relationship between the relative volume of the pay rock types and reservoir performance. This does not mean that these relationships do not exist, but simply that we can not identify individual rock types or estimate permeability with any degree of accuracy due to the extreme heterogeneities that exist within the Clear Fork section. 3. Depositional environments can not be directly predicted from conventional well log or core data, but only through visual and microscopic examination. Significant post-depositional diagenesis, which is typical for the Clear Fork section, usually masks any relationship between depositional environment, porosity and permeability. In addition, several depositional environments may overlap one another, further complicating any attempts to predict individual environments. 4. Core data (particularly whole core) is invaluable in defining pay within the Clear Fork at NRU, however, it is difficult to apply this information to noncored wells via well log data. Permeability estimation in heterogeneous, low permeability carbonate reservoirs is usually qualitative at best, but it is sufficient for our purposes, since we only have to identify the pay intervals in a broad sense as hydraulic fracture treatments are required for all intervals. 5. Porosity and water saturations calculated from well logs must be corrected for lithology as well as changing pore-scale attributes that affect the cementation factor and saturation exponent. The water resistivity must be calculated over small depth increments due to a large variation in water salinity across the Clear Fork caused by unequal water injection support.

142 Long Term Production and Injection Data Analysis 1. A geologically-driven core-log model was developed to help locate regions of the NRU with favorable reservoir quality. If geologic data are unavailable, the high quality areas of the reservoir may be identified by long-term production data analysis. 2. We propose the use of the Fetkovich-McCray type curve to estimate total and movable reservoir volumes, as well as the flow characteristics of the reservoir. Further, given a limited quantity of production or injection data, we have shown that we can accurately interpret and predict reservoir behavior. 3. The rate integral and rate integral-derivative functions allow for the analysis and interpretation of "noisy" field production and injection data. In addition, the integral functions provide better type curve matches and increase confidence in our interpretations. 4. The analysis techniques that we propose always yield excellent estimates of original and movable oil volumes and accurate estimates of reservoir flow characteristics, provided good early-time data are available. Using our methodology to analyze and interpret production and injection data is relatively straightforward and can provide the same information as conventional pressure transient analysis, without the associated cost of data acquisition, or loss of production/injection. 5. The field examples presented in this work verify that the new decline type curve is valid for the analysis and interpretation of injection data for fractured wells. Used together with pressure transient tests and injection profile data, this is probably the most effective tool for estimating injection efficiency, evaluating interwell communication, and estimating formation properties Reservoir Surveillance 1. A reservoir surveillance plan must be put in place in order to continuously monitor the daily performance of all production and injection wells. This can

143 444 be performed in a cost-effective manner utilizing only daily rate and pressure data. Periodic pressure transient tests can be economically performed on production and injection wells using surface pressure data acquisition with low resolution gauges on injection wells or an acoustic well-sounder on production wells. 2. The field examples presented in this work verify that the use of the Fetkovich- McCray decline type curve is valid for the analysis and interpretation of injection data for fractured wells. Used together with pressure transient tests and injection profile data, this is probably the most effective tool for estimating injection efficiency, evaluating interwell communication, and estimating formation properties. 3. Long-term water injection at or above the parting pressure of the formation has caused the propagation of extensive interwell fractures at NRU, some of which have caused direct hydraulic communication between offset injection wells. Communication between injection wells has reduced the injection capacity of the unit, and subsequently, the efficiency of the waterflood. 4. Before wells are drilled, it is absolutely essential that a complete reservoir surveillance study be performed on the infill area. Any problems relating to fracture propagation due to long-term water injection as identified by decline type curve analysis of long-term injection data or a Hall plot must be quantified. Instances of poor injector-producer conformance should be identified using injection and temperature profiles, and corrected if possible. 5. Individual layer pressures obtained during development drilling using Formation Pressure Test tools can be utilized to better define reservoir continuity, to confirm reservoir flow simulation results and to provide pore pressure estimates used in hydraulic fracture design and simulation. 6. By combining the analyses and interpretations from injection profile data, pressure transient data, long-term production and injection data and Thermal

144 445 Neutron Decay Log data, we can easily detect any deviation from "normal" reservoir behavior Rock Mechanical Properties Prediction 1. Static elastic moduli can be predicted fairly accurately from conventional well log responses. It would appear that there is no further need to record FWS logs for the determination of mechanical rock property data. 2. The static elastic moduli and stress profiles generated for fracture simulation have less of an affect on the final hydraulic fracture design than the perforating strategy, fluid rheology and fluid leakoff coefficient. 3. It is unclear whether additional studies would greatly enhance the predictive model or the fracture treatment design. We still prefer to use accurate values for Young's modulus, Poisson ratio and closure pressure in the initial design. These values have been significantly abused in past HF designs at the NRU Completion and Stimulation 1. Only the highest quality reservoir section of each staged frac treatment should be perforated and acidized. Ten to fifteen feet of the best interval should be cluster perforated at 4 to 5 shots/ft with a correctly sized entry hole (>0.25 inches for 20/40 sand). Limited-entry perforating should not be utilized. 2. Fracture treatment sizes and costs can be reduced without sacrificing completion efficiency. A conventional fluid system utilizing a Borate crosslinker should be utilized for the fracture treatment. For a designed 200-ft fracture half-length, the total job volume should be 25,000 to 30,000 gallons of clean fluid with a total proppant load of approximately 50,000 lbm of 20/40 Ottawa sand. The primary pays in adjacent Clear Fork intervals (Lower, Middle and Upper) can be adequately treated with a single frac treatment, and the job size should not be greatly increased when larger intervals are treated.

145 Based on recent tiltmeter surveys and net pressure modeling, we are currently obtaining 200 feet of propped half-length and feet of propped vertical growth with this treatment volume. The resulting long-term fracture conductivity will be sufficient due to the extremely low effective oil permeability in the Clear Fork Data Integration and 10-Acre Well Performance 1. We identified the regions in the unit with additional reserves potential (accelerated or incremental), as well as those areas in which infill drilling is not likely to be economic. 2. Infill drilling should be restricted to areas of the field that possess large volume percentages of rock types 1, 2 and 6, good effective oil permeability, large hydrocarbon pore volume and are located in areas with high fluid withdrawal rates. 3. If the productive intervals within the reservoir can be better defined based on detailed core-log modeling, then completion efficiency (initial potential and reserves recovery) can be increased and completion costs can be decreased. 4. The core-log modeling results indicate that the quality of the rock in the 10- acre infill wells (based on net pay, oil flow capacity and hydrocarbon pore volume) is superior to that found in the 20-acre infill wells. This indicates that targeting infill wells is both a valid and economically sound method for field development. 5. The 10-acre infill wells drilled in the high reservoir quality areas of the NRU will be economic wells, with average estimated ultimate recoveries of almost 100 MSTBO. The wells drilled in the areas with poorer rock quality and continuity will be marginal or uneconomic.

146 If flow simulation is to be performed, we believe that a geostatistical simulation model would be far superior to any deterministic model given the great uncertainty involved with permeability estimation. Since we possess significant historical production, injection and pressure transient data, reservoir "sweet spots" can be located without resorting to flow simulation. We note that a reservoir simulation model must be constructed for economic forecasts, however, its value will be directly proportional to the quality of the input data. While we think a "most probable" realization from a stochastic model will yield good results, we feel that past performance is what really matters in mature fields and reservoirs and these data types can be analyzed quickly and in a cost-effective manner Future Considerations A significant quantity of movable oil will remain in the reservoir at the end of the secondary recovery (waterflood) period. In most cases, economic or operational constraints negate the use of CO 2 injection or a WAG (water alternating with gas) process as a secondary recovery mechanism. Although injecting water is low-cost, operationally straight-forward and fairly effective, it is obvious that a process utilizing a miscible gas would be the optimum secondary depletion method for these low permeability, shallowshelf carbonate (SSC) reservoirs. Reservoirs of this type are irreparably damaged by long-term water injection, therefore, when the time comes to consider tertiary recovery processes, it is often difficult to implement these techniques since channels and fractures have been opened throughout the reservoir. At this late stage, the use of a miscible gas-flooding technique often becomes both an economic and operational nightmare. In the near future, the application of tertiary recovery (CO 2 ) techniques may be considered at the NRU. The application of tertiary recovery processes will be difficult due to the aforementioned problems caused by water injection.

147 448 Approximately 15 to 20 percent of the contacted oil-in-place will be recovered through primary and secondary (waterflood) production. Material balance decline type curve analyses have indicated that the contacted original oil-in-place is approximately 215 MMSTB. Assuming that some additional reserves will be contacted as the result of infill wells, we will consider a volumetric estimate of OOIP. Recent steady-state and unsteady-state laboratory core analyses indicate that approximately one-half of the total oil-in-place is movable (S wi ~ 30 percent, S or ~ 35 percent). For the 5,633 productive acres at the NRU, using an average net pay thickness of 150 feet and an average porosity of 7.5 percent, the volumetric OOIP will be approximately 280 MMSTB and the total movable oil is approximately 140 MMSTB. Using a 16 percent total estimated ultimate recovery for primary and secondary production, this means that at most only 32 percent of the movable oil (44.8 MMSTB) will be recovered without the application of tertiary recovery processes. Based on decline type curve analyses, we believe that primary and secondary recovery will actually be closer to 26 to 28 percent of movable oil. We might recover a significant volume of the remaining 95 MMSTBO of movable oil via CO 2 flooding. However, due to formation damage, fracturing and channeling along injector rows (parallel to regional fracture azimuth) caused by long-term water injection, it will likely be necessary to drill a significant amount of replacement wells. These wells will have to be located between current producer and injector rows, or between existing producers (risky) in order to avoid losing the CO 2 to the water or channeling it. Tertiary recovery processes may be marginally economic or uneconomic.

148 449 NOMENCLATURE Field Variables Formation and Fluid Parameters A = drainage or injection area, ft 2 B = formation volume factor, RB/STB B o B g FE FZI h = oil formation volume factor, RB/STB = gas formation volume factor, RB/MCF = fracturing fluid efficiency = V f /V i, fraction = flow zone indicator defined by Amaefule, m = formation thickness, ft I = resistivity ratio defined by Wyllie and Spangler 25 k = absolute permeability, md or cm 2 k a k air k e k GEOM k 90 k MAX k brine k g k o k w k prop k rg k ro k rw L = effective permeability to air, md = effective permeability to air, md = effective permeability, md = geometric mean of k 90 and k MAX, md = core-measured horizontal permeability (oblique to k MAX ), md = core-measured horizontal permeability, md = effective permeability to brine water, md = effective permeability to gas, md = effective permeability to oil, md = effective permeability to water, md = proppant permeability, md = relative gas permeability, fraction = relative oil permeability, fraction = relative water permeability, fraction = length, any units system

149 450 L e m ff m prop = average path length through a porous media defined by Wyllie and Spangler 25 = mass of one gallon of fracturing fluid, lbm = mass of proppant in one gallon of mean slurry, lbm MPTR = median pore throat radius, 10-6 m µ = fluid viscosity, cp µ g = gas viscosity, cp µ o = oil viscosity, cp N = original oil-in-place, STB N p N p,mov φ φ C φ D φ e φ He φ N φ X φ Z r e RE R i = cumulative oil production, STB = movable oil, STB = porosity, percent or fraction = lithology-corrected crossplot porosity, percent or fraction = porosity from the bulk density log, percent or fraction = effective porosity, fraction = helium porosity, percent or fraction = compensated neutron porosity (lime matrix), percent or fraction = density-neutron crossplot porosity, percent or fraction = pore volume to grain volume ratio, fraction = reservoir drainage radius, ft = recovery efficiency defined by Wardlaw, 30 percent = incremental pore entry radius defined by Burdine, 24 cm RG = reservoir grade defined by Jennings 39 R pt = average pore throat radius defined by Hagiwara, m RQI R o R p = reservoir quality index defined by Amaefule, m = resistivity of a 100-percent water sand containing formation water with resistivity R w, Ωm = producing gas-oil ratio, scf/stb

150 451 R s R t R ti r w r wa R w ρ b ρ ff ρ maa ρ slurry = solution gas-oil ratio, scf/stb = true formation resistivity, Ωm = true formation resistivity at irreducible conditions, Ωm = wellbore radius, ft = apparent wellbore radius (includes formation damage or stimulation), ft = formation water resistivity, Ωm = saturated bulk density, g/cc = density of fracturing fluid, lbm/gallon (ppg) = apparent matrix density, g/cc = density of equivalent slurry, lbm/gallon (ppg) S c S e S g S gr S gv S o S or S nwp S w S wi = critical saturation defined by Brooks and Corey, 29 fraction = effective saturation (= S w *) defined by Brooks and Corey, 29 fraction = gas saturation, fraction = residual gas saturation, fraction = surface area per unit grain volume, 1/10-6 m = oil saturation, fraction = residual oil saturation, fraction = non-wetting phase saturation, fraction = water saturation, fraction = irreducible water saturation, fraction STB = stock tank barrel, ft 3 T = temperature, o F T = tortuosity (length/length), fraction T e = tortuosity of a partially saturated medium, length U maa = volumetric capture cross-section, barns/cm 3 V b = bulk volume, cm 3 V bulk V dp = bulk volume, percent = Dykstra-Parsons 67 heterogeneity coefficient, fraction

151 452 V f V i V i V pore = volume of fracture, gallons = total injected fracture volume, gallons = incremental pore volume defined by Burdine, 24 fraction = pore volume, percent w = textural parameter defined by Coates and Dumanoir 48 w = propped fracture width, ft W i,mov W i W p W tot x f = total injectable water, STB = cumulative water injection, STB = cumulative water production, STB = total system volume, STB = fracture half-length, ft Pressure/Rate/Time Parameters b = Fetkovich/Arps decline curve exponent b pss = constant for the liquid flow equation as defined in Eq. 5.9 c g = gas compressibility, psi -1 c o = oil compressibility, psi -1 c rock = formation compressibility, psi -1 c t = total system compressibility, psi -1 c ti = initial total system compressibility, psi -1 c w = water compressibility, psi -1 D i = constant defined by Eq. 5.16, D -1 ε a ε r E g ob g pore K m = axial strain, length/length = radial strain, length/length = Young's modulus, psi = overburden pressure gradient, psi/ft = pore pressure gradient, psi/ft = bulk modulus of elasticity, psi = constant in the pseudosteady-state equation for liquid flow, as defined by Eq. 5.8, psi/stb

152 453 ν = Poisson ratio, fraction p = pressure, psia p = average reservoir pressure, psia (p) func p p p i = pressure functions for type curve matching, p, p or p', psi or psia = p i - p wf, total reservoir pressure drop, psi = p = p ws p wf, t=0, pressure drop for pressure buildup test, psi = p i = 1 t t p(τ) dτ or p = p ws p wf, t=0, integral pressure drop, 0 psi p c = capillary pressure, psi or dyne/cm 2 p d = displacement pressure, psi or dyne/cm 2 p i p tf p wf p ws p wsi p ws,1hr p wsi,1hr q q (q/ p) = initial reservoir pressure, psia = flowing surface tubing pressure, psig = flowing bottomhole pressure, psia = shut-in bottomhole pressure, psia = p wsi = 1 t t p ws (τ) dτ, shut-in bottomhole pressure integral, psia 0 = shut-in bottomhole pressure taken from semilog straight line at 1 hr, psia = shut-in bottomhole pressure integral taken from semilog straight line at 1 hr, psia = production rate, STB/day = oil flow rate or water injection rate, STB/day = pressure drop normalized rate function, STB/D/psi (q/ p) int = constant defined by Eq. 5.15, STB/D/psi (q/ p) i = pressure drop normalized rate integral function, STB/D/psi (q/ p) id = pressure drop normalized rate integral-derivative function, STB/D/psi σ cosθ = adhesive tension, dyne/cm σ a = axial stress, psi

153 454 σ x = in-situ (closure) stress, psi (S b /p c ) A = capillary pressure correlating parameter defined by Swanson, 33 psi -1 τ = dummy variable for integration t = time, days or hours t = N p /q, material balance time, days = W i /q wi, material balance time, days (water injection) (t) func t pss t cp t t c t s V p V s = time functions for type curve matching, t, t or t e, hr = time for the onset of pseudosteady-state, days = equivalent constant pressure time as defined by McCray, 82 days = shut-in time, hr = compressional travel time, µsec/ft = shear travel time, µsec/ft = compressional wave velocity, ft/sec or m/sec = shear wave velocity, ft/sec or m/sec Dimensionless Variables Real Domain α = Biot elastic constant, dimensionless a = empirical constant defined by Archie 26 A1, A2 = empirical constants in the effective permeability equation as defined by Timur 47 b Dpss = pseudosteady-state constant defined by Eq β = empirical pore geometry term for dimensionless pore radius distribution defined by Nakornthap and Evans, 38 dimensionless C 2 = capillary pressure curve shape factor defined by Thomeer 28 C A = reservoir shape factor c k = Kozeny constant for tortuosity determination defined by Carman, 15 dimensionless

154 455 c sh = shape factor for tortuosity determination defined by Carman, 15 dimensionless C v = coefficient of variation defined by Jensen 44 F p F R F s = Purcell lithology factor, 21 (~ tortuosity) = formation resistivity factor defined by Archie, 26 dimensionless = shape factor, dimensionless γ = Euler's Constant = G = pore geometrical factor defined by Thomeer 28 J(S w ) = "J-Function" defined by Leverett, 18 dimensionless λ = pore distribution factor defined by Brooks and Corey 29 m = cementation factor defined by Archie, 26 dimensionless (~2) η = number of pore throats/pore body as defined by Nakornthap and Evans, 38 dimensionless n = saturation exponent defined by Archie, 26 dimensionless (~2) π = circumference to diameter ratio = PTS = pore throat sorting parameter defined by Jennings, 39 dimensionless Q = quartile r 2 r 2 a r ed σ 2 s SG prop sv 2 = coefficient of multiple determination, fraction = adjusted coefficient of multiple determination, fraction = dimensionless drainage radius of reservoir = r e /x f, dimensionless radius (fractured well case) = population variance = radial flow skin factor for near-well damage or stimulation = specific gravity of proppant, dimensionless = sample variance S wd = dimensionless water saturation defined by Blasingame 43 X 2 i x D x wd = tortuosity factor defined by Burdine, 24 dimensionless = x/x f, dimensionless x-distance (fracture case) = x w /x f, dimensionless well location (fracture case)

155 456 x' wd y D = dummy variable of integration (fracture case) = y/x f, dimensionless y-distance (fracture case) Material Balance Decline Type Curve Analysis N pdd = dimensionless decline cumulative production function p D = kh p dimensionless pressure function for the constant flow qbµ rate case q D Bµ = q dimensionless rate function for the constant flow kh (p i p wf ) rate case q Dd = q D b Dpss, dimensionless decline rate function as defined by Fetkovich 78,81 q Ddi = dimensionless decline rate integral as defined by McCray 82 q Ddid = dimensionless decline rate integral derivative function as defined by McCray 82 t D = kt dimensionless time based on wellbore radius φµc t r 2 w t DLf = kt dimensionless time based on fracture half-length φµc t x2 f t DA = kt dimensionless time based on drainage area φµc t A t Dd = 2π t b DA = kt dimensionless decline time as defined by Dpss φµc t A Fetkovich. Pressure Transient Analysis C Df = dimensionless wellbore storage coefficient based on fracture halflength C fd = dimensionless fracture conductivity p wd = kh p, dimensionless wellbore pressure function for constant qbµ injection rate case, including wellbore storage and skin effects

156 457 p wd ' dp = t wd D = dp wd, logarithmic derivative of dimensionless wellbore dt D d(lnt D ) pressure function for the constant flow rate case, including wellbore storage and skin effects t D p wdi = t 1 p wd (τ)dτ, dimensionless wellbore pressure integral function, D 0 including wellbore storage and skin effects dp p wdi ' = t wdi D = dp wdi, logarithmic derivative of dimensionless wellbore dt D d(lnt D ) pressure integral function, including wellbore storage and skin effects t D = k t, dimensionless time based on the wellbore radius φµc t r 2 w t LfD = k t, dimensionless time based on the fracture half- φµc t x2 f length Dimensionless Variables Laplace Transform Domain = Laplace transform of dimensionless pressure for the constant flow rate p D q D u case = Laplace transform of dimensionless rate for the constant wellbore pressure case = Laplace space variable, dimensionless Special Functions I 0 (x) = modified Bessel function of the 1st kind, zero order I 1 (x) = modified Bessel function of the 1st kind, 1st order K 0 (x) = modified Bessel function of the 2nd kind, zero order K 1 (x) = modified Bessel function of the 2nd kind, 1st order

157 458 Special Subscripts ANHY = anhydrite cal = calculated cls = continuous line source solution cfracs = continuous fracture source solution cpb = constant pressure reservoir boundary D = dimensionless variable DOL = dolomite Dd = dimensionless decline variable dyn = dynamic mechanical rock property f = fractured i = integral function or initial value id = integral derivative inf = infinite-acting reservoir LS = limestone nfb = no-flow reservoir boundary nw = non-wetting nwp = non-wetting phase MP = match point o = oil pss = pseudosteady-state w = water wi = water injection id = integral-derivative function Operators/Constants: M = thousand MM = million e = exponential operator exp = exponential operator

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169 470 APPENDIX A CAPILLARY PRESSURE DATA FOR 10-ACRE INFILL WELLS Table A Hg-Air Capillary Pressure Results NRU #1509 (#1A 6,347.8 ft)

170 471 Table A.1 (Continued) Permeability to air 10.7 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 27.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#2A 6,348.8 ft)

171 472 Table A.2 (Continued) Permeability to air 1.25 md Porosity 7.20 percent Grain density g/cc (S b /p c ) A Displacement pressure 36.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

172 473 Table A Hg-Air Capillary Pressure Results NRU #1509 (#5A 6,352.9 ft)

173 474 Table A.3 (Continued) Permeability to air, md 0.60 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 55.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#6A 6,353.1 ft)

174 475 Table A.4 (Continued) Permeability to air 0.55 md Porosity 9.91 percent Grain density 2.85 g/cc (S b /p c ) A Displacement pressure 53.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir), 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#7A 6,353.9 ft)

175 476 Table A.5 (Continued) Permeability to air 0.03 md Porosity 4.75 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 4.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

176 477 Table A Hg-Air Capillary Pressure Results NRU #1509 (#8A 6,354.1 ft)

177 478 Table A.6 (Continued) Permeability to air 0.01 md Porosity 4.74 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#11A 7,020.9 ft)

178 479 Table A.7 (Continued) Permeability to air 0.29 md Porosity 6.00 percent Grain density g/cc (S b /p c ) A Displacement pressure 26.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#13A 7,050.8 ft)

179 480 Table A.8 (Continued) Permeability to air 0.52 md Porosity 5.74 percent Grain density g/cc (S b /p c ) A Displacement pressure 16.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

180 481 Table A Hg-Air Capillary Pressure Results NRU #1509 (#16A 7,083.7 ft)

181 482 Table A.9 (Continued) Permeability to air 0.10 md Porosity 5.41 percent Grain density g/cc (S b /p c ) A Displacement pressure 18.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#17A 7,084.6 ft)

182 483 Table A.10 (Continued) Permeability to air 0.16 md Porosity 2.68 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#19A 7,086.3 ft)

183 484 Table A.11 (Continued) Permeability to air 0.79 md Porosity 6.91 percent Grain density g/cc (S b /p c ) A Displacement pressure 9.5 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

184 485 Table A Hg-Air Capillary Pressure Results NRU #1509 (#23A 7,110.7 ft)

185 486 Table A.12 (Continued) Permeability to air 0.05 md Porosity 5.46 percent Grain density g/cc (S b /p c ) A Displacement pressure 3,000 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#24A 7,112.4 ft)

186 487 Table A.13 (Continued) Permeability to air 0.01 md Porosity, percent 6.34 percent Grain density g/cc (S b /p c ) A Displacement pressure 4,100 psi Threshold pressure, psi 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

187 488 Table A Hg-Air Capillary Pressure Results NRU #1509 (#25A 7,133.7 ft)

188 489 Table A.14 (Continued) Permeability to air 0.06 md Porosity 5.22 percent Grain density g/cc (S b /p c ) A Displacement pressure 24.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#26A 7,134.5 ft)

189 490 Table A.15 (Continued) Permeability to air 0.08 md Porosity 3.53 percent Grain density g/cc (S b /p c ) A Displacement pressure 20.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

190 491 Table A Hg-Air Capillary Pressure Results NRU #1509 (#28A 7,188.6 ft)

191 492 Permeability to air 0.15 md Porosity 7.61 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 22.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#29A 7,200.9 ft)

192 493 Table A.17 (Continued) Permeability to air 0.19 md Porosity 7.05 percent Grain density g/cc (S b /p c ) A Displacement pressure 28.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#30A 7,203.1 ft)

193 494 Table A.18 (Continued) Permeability to air 0.01 md Porosity 3.74 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,450 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

194 495 Table A Hg-Air Capillary Pressure Results NRU #1509 (#31A 7,203.9 ft)

195 496 Table A.19 (Continued) Permeability to air 0.07 md Porosity 4.97 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,300 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#33A 7,213.1 ft)

196 497 Table A.20 (Continued) Permeability to air 0.07 md Porosity 8.00 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

197 498 Table A Hg-Air Capillary Pressure Results NRU #1509 (#34A 7,219.1 ft)

198 499 Table A.21 (Continued) Permeability to air 1.01 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 20.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1509 (#35A 7,220.1 ft)

199 500 Table A.22 (Continued) Permeability to air 0.02 md Porosity 7.69 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

200 501 Table A Hg-Air Capillary Pressure Results NRU #1510 (#1D 6,353.5 ft)

201 502 Table A.23 (Continued) Permeability to air md Porosity 3.50 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,300 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#2D 6,801.9 ft)

202 503 Table A.24 (Continued) Permeability to air 0.83 md Porosity 8.72 percent Grain density g/cc (S b /p c ) A psi Displacement pressure 38.0 psi Threshold pressure 30.0 Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#3D 6,852.1 ft)

203 504 Table A.25 (Continued) Permeability to air 2.38 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 34.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

204 505 Table A Hg-Air Capillary Pressure Results NRU #1510 (#4D 7,049.7 ft)

205 506 Table A.26 (Continued) Permeability to air 0.08 md Porosity 4.72 percent Grain density g/cc (S b /p c ) A Displacement pressure 30.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#5D 7,074.1 ft)

206 507 Table A.27 (Continued) Permeability to air 0.01 md Porosity 4.71 percent Grain density g/cc (S b /p c ) A Displacement pressure 40.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

207 508 Table A Hg-Air Capillary Pressure Results NRU #1510 (#6D 7,093.2 ft)

208 509 Table A.28 (Continued) Permeability to air 0.01 md Porosity 4.73 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#8D 7,094.5 ft)

209 510 Table A.29 (Continued) Permeability to air 0.01 md Porosity 4.44 percent Grain density g/cc (S b /p c ) A Displacement pressure 3,800 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

210 511 Table A Hg-Air Capillary Pressure Results NRU #1510 (#9D 7,125.6 ft)

211 512 Table A.30 (Continued) Permeability to air 12.2 md Porosity 8.42 percent Grain density g/cc (S b /p c ) A Displacement pressure 10.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#13D 7,159.4 ft)

212 513 Table A.31 (Continued) Permeability to air 0.01 md Porosity 3.77 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,100 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

213 514 Table A Hg-Air Capillary Pressure Results NRU #1510 (#14D 7,160.6 ft)

214 515 Table A.32 (Continued) Permeability to air 0.34 md Porosity 4.68 percent Grain density g/cc (S b /p c ) A Displacement pressure 26.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#10D 7,163.8 ft)

215 516 Table A.33 (Continued) Permeability to air 0.31 md Porosity 5.92 percent Grain density g/cc (S b /p c ) A Displacement pressure 25.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

216 517 Table A Hg-Air Capillary Pressure Results NRU #1510 (#16D 7,167.0 ft)

217 518 Table A.34 (Continued) Permeability to air 0.01 md Porosity 5.33 percent Grain density g/cc (S b /p c ) A Displacement pressure 70.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #1510 (#17D 7,168.3 ft)

218 519 Table A.35 (Continued) Permeability to air 0.01 md Porosity 4.43 percent Grain density g/cc (S b /p c ) A Displacement pressure 90.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

219 520 Table A Hg-Air Capillary Pressure Results NRU #1510 (#19D 7,176.4 ft)

220 521 Table A.36 (Continued) Permeability to air 0.26 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 75.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#1C 7,050.9 ft)

221 522 Table A.37 (Continued) Permeability to air 0.01 md Porosity 4.47 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

222 523 Table A Hg-Air Capillary Pressure Results NRU #3319 (#2C 7,053.7 ft)

223 524 Table A.38 (Continued) Permeability to air 0.01 md Porosity 4.31 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#3C 7,054.4 ft)

224 525 Table A.39 (Continued) Permeability to air 0.42 md Porosity 6.30 percent Grain density g/cc (S b /p c ) A Displacement pressure 55.0 psi Threshold pressure 14.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#4C 7,055.7 ft)

225 526 Table A.40 (Continued) Permeability to air 0.01 md Porosity 3.58 percent Grain density g/cc (S b /p c ) A Displacement pressure 65.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

226 Table A Hg-Air Capillary Pressure Results NRU #3319 (#5C 7,056.8 ft)

227 528 Table A.41 (Continued) Permeability to air 0.01 md Porosity 4.69 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,200 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#6C 7,059.9 ft)

228 529 Table A.42 (Continued) Permeability to air 0.01 md Porosity 4.05 percent Grain density g/cc (S b /p c ) A Displacement pressure 2,500 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

229 530 Table A Hg-Air Capillary Pressure Results NRU #3319 (#7C 7,060.7 ft)

230 531 Table A.43 (Continued) Permeability to air 0.01 md Porosity 4.20 percent Grain density g/cc (S b /p c ) A Displacement pressure 3,000 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#8C 7,061.9 ft)

231 532 Table A.44 (Continued) Permeability to air 0.01 md Porosity 4.57 percent Grain density g/cc (S b /p c ) A Displacement pressure 2,800 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

232 533 Table A Hg-Air Capillary Pressure Results NRU #3319 (#9C 7,063.8 ft)

233 534 Table A.45 (Continued) Permeability to air 0.01 md Porosity 4.65 percent Grain density g/cc (S b /p c ) A Displacement pressure 1,900 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#10C 7,066.4 ft)

234 535 Table A.46 (Continued) Permeability to air 0.11 md Porosity 7.49 percent Grain density g/cc (S b /p c ) A Displacement pressure 40.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

235 536 Table A Hg-Air Capillary Pressure Results NRU #3319 (#12C 7,068.6 ft)

236 537 Table A.47 (Continued) Permeability to air 0.90 md Porosity percent Grain density g/cc (S b /p c ) A psi Displacement pressure 25.0 psi Threshold pressure 2.0 Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#13C 7,069.3 ft)

237 538 Table A.48 (Continued) Permeability to air 0.30 md Porosity 6.33 percent Grain density g/cc (S b /p c ) A Displacement pressure 50.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

238 539 Table A Hg-Air Capillary Pressure Results NRU #3319 (#14C 7,070.6 ft)

239 540 Table A.49 (Continued) Permeability to air 0.01 md Porosity 6.16 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#15C 7,072.5 ft)

240 541 Table A.50 (Continued) Permeability to air 0.01 md Porosity 4.81 percent Grain density g/cc (S b /p c ) A Displacement pressure 2,700 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

241 542 Table A Hg-Air Capillary Pressure Results NRU #3319 (#16C 7,073.9 ft)

242 543 Table A.51 (Continued) Permeability to air 0.01 md Porosity 7.31 percent Grain density g/cc (S b /p c ) A Displacement pressure 2,000 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#17C 7,076.5 ft)

243 544 Table A.52 (Continued) Permeability to air 0.01 md Porosity 5.10 percent Grain density g/cc (S b /p c ) A Displacement pressure 43.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#19C 7,148.5 ft)

244 545 Table A.53 (Continued)

245 546 Table A.53 (Continued) Permeability to air 0.01 md Porosity 5.58 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#20C 7,149.2 ft)

246 547 Table A.54 (Continued) Permeability to air 0.75 md Porosity 6.33 percent Grain density g/cc (S b /p c ) A Displacement pressure 20.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#22C 7,150.8 ft)

247 548 Table A.55 (Continued)

248 549 Table A.55 (Continued) Permeability to air 0.04 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 2,000 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#23C 7,152.1 ft)

249 550 Table A.56 (Continued) Permeability to air 0.02 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#25C 7,154.1 ft)

250 551 Table A.57 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 8.2 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

251 552 Table A Hg-Air Capillary Pressure Results NRU #3319 (#26C 7,154.8 ft)

252 553 Table A.58 (Continued) Permeability to air 1.34 md Porosity 5.44 percent Grain density g/cc (S b /p c ) A Displacement pressure 14.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#28C 7,156.3 ft)

253 554 Table A.59 (Continued) Permeability to air 0.01 md Porosity 5.59 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 75.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#29C 7,157.6 ft)

254 555 Table A.60 (Continued) Permeability to air 0.04 md Porosity 8.16 percent Grain density, g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

255 Table A Hg-Air Capillary Pressure Results NRU #3319 (#30C 7,158.4 ft)

256 557 Table A.61 (Continued) Permeability to air 5.81 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 18.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#31C 7,160.7 ft)

257 558 Table A.62 (Continued) Permeability to air 70.5 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 18.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#32C 7,161.5 ft)

258 559 Table A.63 (Continued) Permeability to air 0.23 md Porosity 9.14 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

259 560 Table A Hg-Air Capillary Pressure Results NRU #3319 (#34C 7,163.2 ft)

260 561 Table A.64 (Continued) Permeability to air, 0.17 md Porosity, 8.04 percent Grain density, g/cc (S b /p c ) A Displacement pressure, 80.0 psi Threshold pressure, 3.0 psi Median pore throat radius, µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#35C 7,164.2 ft)

261 562 Table A.65 (Continued) Permeability to air 0.02 md Porosity 6.34 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

262 563 Table A Hg-Air Capillary Pressure Results NRU #3319 (#38C 7,167.9 ft)

263 564 Table A.66 (Continued) Permeability to air 0.01 md Porosity 5.07 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3319 (#39C 7,185.0 ft)

264 565 Table A.67 (Continued) Permeability to air 1.03 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 70.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#5B 6,321.6 ft)

265 566 Table A.68 (Continued) Permeability to air 0.15 md Porosity 2.51 percent Grain density g/cc (S b /p c ) A Displacement pressure 40.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

266 567 Table A Hg-Air Capillary Pressure Results NRU #3533 (#7B 6,401.9 ft)

267 568 Table A.69 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 20.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#9B 6,403.9 ft)

268 569 Table A.70 (Continued) Permeability to air 0.11 md Porosity 5.81 percent Grain density g/cc (S b /p c ) A Displacement pressure 50.0 psi Threshold pressure 6.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#11B 6,405.1 ft)

269 570 Table A.71 (Continued) Permeability to air 0.19 md Porosity 6.66 percent Grain density g/cc (S b /p c ) A Displacement pressure 55.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

270 571 Table A Hg-Air Capillary Pressure Results NRU #3533 (#12B 6,406.1 ft)

271 572 Table A.72 (Continued) Permeability to air 0.85 md Porosity 6.49 percent Grain density g/cc (S b /p c ) A Displacement pressure 35.0 psi Threshold pressure 4.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#13B 6,407.1 ft)

272 573 Table A.73 (Continued) Permeability to air 0.03 md Porosity 3.25 percent Grain density g/cc (S b /p c ) A Displacement pressure 28.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#14B 6,414.8 ft)

273 574 Table A.74 (Continued) Permeability to air 0.05 md Porosity 5.00 percent Grain density g/cc (S b /p c ) A Displacement pressure psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

274 575 Table A Hg-Air Capillary Pressure Results NRU #3533 (#15B 6,418.1 ft)

275 576 Table A.75 (Continued) Permeability to air 12.0 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 17.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#16B 6,418.9 ft)

276 577 Table A.76 (Continued) Permeability to air 21.4 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 13.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#17B 6,419.1 ft)

277 578 Table A.77 (Continued) Permeability to air, 0.87 md Porosity, 4.04 percent Grain density, g/cc (S b /p c ) A Displacement pressure, 11.0 psi Threshold pressure, 2.0 psi Median pore throat radius, µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

278 579 Table A Hg-Air Capillary Pressure Results NRU #3533 (#18B 6,422.9 ft)

279 580 Table A.78 (Continued) Permeability to air 8.57 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 17.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#19B 6,424.9 ft)

280 581 Table A.79 (Continued) Permeability to air 0.19 md Porosity 7.02 percent Grain density g/cc (S b /p c ) A Displacement pressure 23.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

281 582 Table A Hg-Air Capillary Pressure Results NRU #3533 (#20B 6,427.1 ft)

282 583 Table A.80 (Continued) Permeability to air, md Porosity, percent Grain density, g/cc (S b /p c ) A Displacement pressure, 8.5 psi Threshold pressure, 4.0 psi Median pore throat radius, µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#21B 6,427.9 ft)

283 584 Table A.81 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 14.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

284 585 Table A Hg-Air Capillary Pressure Results NRU #3533 (#22B 6,428.1 ft)

285 586 Table A.82 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 12.0 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#23B 6,803.8 ft)

286 587 Table A.83 (Continued) Permeability to air 0.01 md Porosity 4.92 percent Grain density g/cc (S b /p c ) A Displacement pressure 2,000 psi Threshold pressure 3.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

287 588 Table A Hg-Air Capillary Pressure Results NRU #3533 (#24B 6,951.7 ft)

288 589 Table A.84 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 15.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#25B 6,952.1 ft)

289 590 Table A.85 (Continued) Permeability to air md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 13.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#26B 6,952.9 ft)

290 591 Table A.86 (Continued) Permeability to air 10.0 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 18.0 psi Threshold pressure 4.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

291 592 Table A Hg-Air Capillary Pressure Results NRU #3533 (#27B 6,954.6 ft)

292 593 Table A.87 (Continued) Permeability to air 2.43 md Porosity 8.27 percent Grain density g/cc (S b /p c ) A Displacement pressure 33.0 psi Threshold pressure 16.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#30B 7,180.6 ft)

293 594 Table A.88 (Continued) Permeability to air 8.45 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 21.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#31B 7,181.4 ft)

294 595 Table A.89 (Continued) Permeability to air 50.8 md Porosity percent Grain density g/cc (S b /p c ) A Displacement pressure 13.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

295 596 Table A Hg-Air Capillary Pressure Results NRU #3533 (#32B 7,184.3 ft)

296 597 Table A.90 (Continued) Permeability to air 0.01 md Porosity 6.12 percent Grain density g/cc (S b /p c ) A Displacement pressure 50.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm Table A Hg-Air Capillary Pressure Results NRU #3533 (#33B 7,187.3 ft)

297 598 Table A.91 (Continued) Permeability to air 1.86 md Porosity 6.43 percent Grain density g/cc (S b /p c ) A Displacement pressure 8.0 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

298 599 Table A Hg-Air Capillary Pressure Results NRU #3533 (#34B 7,191.2 ft)

299 600 Table A.92 (Continued) Permeability to air 7.12 md Porosity 9.03 percent Grain density g/cc (S b /p c ) A Displacement pressure 5.5 psi Threshold pressure 2.0 psi Median pore throat radius µm ρ water psi/ft ρ hydrocarbon psi/ft σ cosθ (Lab) 368 dyne/cm σ cosθ (Reservoir) 26 dyne/cm

300 601 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #1509 (#3A 6,349.9 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 2.78 md Porosity percent Water density g/cc Oil density g/cc Core length 4.84 cm Pore volume cc S wi 32.6 percent S or 14.4 percent

301 602 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #1509 (#4A 6,350.2 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air md Porosity 8.87 percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 6.90 percent S or 33.9 percent

302 603 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #1509 (#14A 7,068.4 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 1.09 md Porosity 5.87 percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 33.7 percent S or 27.1 percent

303 604 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3533 (#6B 6,400.9 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 4.61 md Porosity percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 39.8 percent S or 23.2 percent

304 605 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3533 (#8B 6,402.2 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air md Porosity 7.28 percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 21.3 percent S or 24.3 percent

305 606 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3533 (#10B 6,404.1 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 2.06 md Porosity 8.14 percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 5.30 percent S or 34.1 percent

306 607 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3533 (#28B 7,135.9 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 2.78 md Porosity 7.38 percent Water density g/cc Oil density g/cc Core length 5.00 cm Pore volume cc S wi 20.1 percent S or 29.1 percent

307 608 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3319 (#11C 7,067.8 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air fractured Porosity percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 13.2 percent S or 31.5 percent

308 609 Table A Water-Oil and Oil-Water Centrifuge Capillary Pressure Results NRU #3319 (#33C 7,162.5 ft). Water-Oil Data (Bottom Radius = cm) Produced Hassler-Brunner Hassler-Brunner RPM Oil S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Oil-Water Data (Bottom Radius = 9.38 cm) Produced Hassler-Brunner Hassler-Brunner RPM Water S w p c S w p c (rev/min) (cc) (percent) (psi) (percent) (psi) Permeability to air 3.57 md Porosity percent Water density g/cc Oil density g/cc Core length cm Pore volume cc S wi 17.0 percent S or 39.6 percent

309 610 APPENDIX B RELATIVE PERMEABILITY DATA 10-ACRE INFILL WELLS Table B.1 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1509, Core 1A (6,347.8 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air md Porosity 13.8 percent Initial water saturation, S wi 23.7 percent Oil permeability at S wi 2.60 md Net confining stress 5,450 psi Pore volume cc Differential pressure for test psi Initial oil volume 8.97 cc Back pressure 0.0 psi Initial water volume 2.79 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

310 611 Table B.2 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1509, Core 3A (6,349.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 3.57 md Porosity 9.88 percent Initial water saturation, S wi 12.6 percent Oil permeability at S wi 1.09 md Net confining stress 4,850 psi Pore volume 5.41 cc Differential pressure for test psi Initial oil volume, 4.73 cc Back pressure 0.0 psi Initial water volume 0.68 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi k o /k g test resulted in pore plugging

311 612 Table B.3 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 6B (6,400.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 4.34 md Porosity 10.1 percent Initial water saturation, S wi 9.2 percent Oil permeability at S wi 0.74 md Net confining stress 5,200 psi Pore volume cc Differential pressure for test psi Initial oil volume 8.08 cc Back pressure 0.0 psi Initial water volume 0.82 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

312 613 Table B.4 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 7B (6,401.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 19.8 md Porosity 13.5 percent Initial water saturation, S wi 17.3 percent Oil permeability at S wi 4.57 md Net confining stress 5,000 psi Pore volume cc Differential pressure for test psi Initial oil volume 9.77 cc Back pressure 0.0 psi Initial water volume 2.04 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

313 614 Table B.5 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 18B (6,422.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio , Permeability to air 4.76 md Porosity 10.7 percent Initial water saturation, S wi 29.4 percent Oil permeability at S wi 1.86 md Net confining stress 4,825 psi Pore volume cc Differential pressure for test psi Initial oil volume 6.61 cc Back pressure 0.0 psi Initial water volume 2.75 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

314 615 Table B.6 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 21B (6,427.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 34.3 md Porosity 13.7 percent Initial water saturation, S wi 12.3 percent Oil permeability at S wi 17.1 md Net confining stress 4,950 psi Pore volume cc Differential pressure for test 75.0 psi Initial oil volume cc Back pressure 0.0 psi Initial water volume 1.42 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

315 616 Table B.7 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 22B (6,428.1 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 16.3 md Porosity 10.7 percent Initial water saturation, S wi 24.8 percent Oil permeability at S wi 7.75 md Net confining stress 4,700 psi Pore volume cc Differential pressure for test 75.0 psi Initial oil volume 6.84 cc Back pressure 0.0 psi Initial water volume 2.25 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

316 617 Table B.8 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 24B (6,951.7 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 16.2 md Porosity 11.8 percent Initial water saturation, S wi 24.4 percent Oil permeability at S wi 10.7 md Net confining stress 5,675 psi Pore volume cc Differential pressure for test psi Initial oil volume 7.78 cc Back pressure 0.0 psi Initial water volume 2.51 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

317 618 Table B.9 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 25B (6,952.1 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio , , Permeability to air 29.1 md Porosity 11.6 percent Initial water saturation, S wi 26.4 percent Oil permeability at S wi 16.3 md Net confining stress 5,600 psi Pore volume cc Differential pressure for test psi Initial oil volume 7.58 cc Back pressure 0.0 psi Initial water volume 2.72 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

318 619 Table B.10 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 26B (6,952.9 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 4.31 md Porosity 15.4 percent Initial water saturation, S wi 27.6 percent Oil permeability at S wi 3.63 md Net confining stress 5,625 psi Pore volume cc Differential pressure for test psi Initial oil volume 9.85 cc Back pressure 0.0 psi Initial water volume 3.75 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

319 620 Table B.11 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 34B ( ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 13.1 md Porosity 9.3 percent Initial water saturation, S wi 22.3 percent Oil permeability at S wi 10.8 md Net confining stress 5,700 psi Pore volume cc Differential pressure for test psi Initial oil volume 6.07 cc Back pressure 0.0 psi Initial water volume 1.74 cc Temperature 72.0 o F Core length 7.40 cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

320 621 Table B.12 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1510, Core 3D (6,852.1 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 2.45 md Porosity 11.5 percent Initial water saturation, S wi 23.0 percent Oil permeability at S wi md Net confining stress 5,600 psi Pore volume cc Differential pressure for test psi Initial oil volume 7.95 cc Back pressure 0.0 psi Initial water volume 2.37 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

321 622 Table B.13 Native-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1510, Core 9D (7,125.6 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio , Permeability to air 2.94 md Porosity 6.88 percent Initial water saturation, S wi 32.3 percent Oil permeability at S wi md Net confining stress 6,400 psi Pore volume 5.95 cc Differential pressure for test psi Initial oil volume 4.03 cc Back pressure 0.0 psi Initial water volume 1.92 cc Temperature 72.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi 2 Endpoint k o /k g data

322 623 Table B.14 Clean-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1509, Core 1A (6,347.8 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 16.5 md Porosity 14.4 percent Initial water saturation, S wi 53.5 percent Oil permeability at S wi 5.10 md Net confining stress 5,450 psi Pore volume cc Differential pressure for test psi Initial oil volume 5.46 cc Back pressure psi Initial water volume 6.29 cc Temperature 73.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

323 624 Table B.15 Clean-State Unsteady-State Gas-Oil Relative Permeability Results NRU #3533, Core 22B (6,428.1 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 16.3 md Porosity 10.7 percent Initial water saturation, S wi 65.3 percent Oil permeability at S wi 11.6 md Net confining stress 5,000 psi Pore volume cc Differential pressure for test psi Initial oil volume 3.15 cc Back pressure psi Initial water volume 5.94 cc Temperature 77.0 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

324 625 Table B.16 Clean-State Unsteady-State Gas-Oil Relative Permeability Results NRU #1510, Core 3D (6,852.1 ft). Gas Saturation, (percent pore vol.) Relative Permeability to Gas, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Gas-Oil Relative Permeability Ratio Permeability to air 2.45 md Porosity 11.8 percent Initial water saturation, S wi 61.5 percent Oil permeability at S wi 1.53 md Net confining stress 5,600 psi Pore volume cc Differential pressure for test psi Initial oil volume 3.90 cc Back pressure psi Initial water volume 6.24 cc Temperature 74.5 o F Core length cm Gas viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

325 626 Table B.17 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 18B (6,422.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio , Permeability to air 4.76 md Porosity 10.7 percent Initial water saturation, S wi 29.4 percent Oil permeability at S wi 1.86 md Net confining stress 4,825 psi Pore volume cc Differential pressure for test 1,500 psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

326 627 Table B.18 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 22B (6,428.1 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio , Permeability to air 16.3 md Porosity 10.7 percent Initial water saturation, S wi 24.8 percent Oil permeability at S wi 7.75 md Net confining stress 4,700 psi Pore volume cc Differential pressure for test psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

327 628 Table B.19 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 24B (6,951.7 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 16.2 md Porosity 11.8 percent Initial water saturation, S wi 24.4 percent Oil permeability at S wi 10.7 md Net confining stress 5,675 psi Pore volume cc Differential pressure for test psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

328 629 Table B.20 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 25B (6,952.1 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio , Permeability to air 29.1 md Porosity 11.6 percent Initial water saturation, S wi 26.4 percent Oil permeability at S wi 16.3 md Net confining stress 5,600 psi Pore volume cc Differential pressure for test psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

329 630 Table B.21 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 26B (6,952.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 4.31 md Porosity 15.4 percent Initial water saturation, S wi 27.6 percent Oil permeability at S wi 3.63 md Net confining stress 5,625 psi Pore volume cc Differential pressure for test 1,000.0 psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

330 631 Table B.22 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 34B (7,191.2 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio , Permeability to air 13.1 md Porosity 9.3 percent Initial water saturation, S wi 22.3 percent Oil permeability at S wi 10.8 md Net confining stress 5,600 psi Pore volume cc Differential pressure for test psi Initial oil volume 6.07 cc Back pressure 0.0 psi Initial water volume 1.74 cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

331 632 Table B.23 Native-State Unsteady-State Water-Oil and Oil-Water Relative Permeability Results NRU #1510, Core 3D (6,852.1 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 2.45 md Porosity 11.5 percent Initial water saturation, S wi 23.0 percent Oil permeability at S wi md Net confining stress 5,600 psi Pore volume cc Differential pressure for test 1,800.0 psi Initial oil volume cc Back pressure 0.0 psi Initial water volume cc Temperature o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

332 633 Table B.24 Clean-State Unsteady-State Water-Oil Relative Permeability Results NRU #3533, Core 7B (6,401.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 20.2 md Porosity 13.6 percent Initial water saturation, S wi 33.1 percent Oil permeability at S wi 14.7 md Net confining stress 5,000 psi Pore volume cc Differential pressure for test psi Initial oil volume 7.91 cc Back pressure psi Initial water volume 3.92 cc Temperature 77.0 o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

333 634 Table B.25 Clean-State Unsteady-State Water-Oil Relative Permeability Results NRU #3533, Core 24B (6,951.7 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 15.8 md Porosity 11.7 percent Initial water saturation, S wi 44.6 percent Oil permeability at S wi 12.3 md Net confining stress 5,675 psi Pore volume cc Differential pressure for test psi Initial oil volume 5.64 cc Back pressure 0.0 psi Initial water volume 4.54 cc Temperature 73.5 o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

334 635 Table B.26 Clean-State Unsteady-State Water-Oil Relative Permeability Results NRU #3533, Core 26B (6,952.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 4.86 md Porosity 15.2 percent Initial water saturation, S wi 42.6 percent Oil permeability at S wi 4.77 md Net confining stress 5,625 psi Pore volume cc Differential pressure for test psi Initial oil volume 7.69 cc Back pressure 0.0 psi Initial water volume 5.71 cc Temperature 75.0 o F Core length cm Water viscosity cp Core area cm 2 Oil viscosity cp 1 Relative to oil permeability at S wi

335 636 Table B.27 Native-State Steady-State Water-Oil and Oil-Water Relative Permeability Results NRU #1509, Core 1A (6,347.8 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio (reverse) Permeability to air 16.5 md Porosity 14.4 percent Initial water saturation, S wi 23.7 percent Oil permeability at S wi 2.25 md Net confining stress 1,500 psi Pore volume cc Back pressure 0.0 psi Initial oil volume cc Temperature o F Initial water volume cc Water viscosity cp Core length cm Oil viscosity cp Core area cm 2 1 Relative to oil permeability at S wi

336 637 Table B.28 Native-State Steady-State Water-Oil and Oil-Water Relative Permeability Results NRU #3533, Core 21B (6,427.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 31.9 md Porosity 14.2 percent Initial water saturation, S wi 12.3 percent Oil permeability at S wi 11.5 md Net confining stress 1,500 psi Pore volume cc Back pressure 0.0 psi Initial oil volume cc Temperature o F Initial water volume cc Water viscosity cp Core length cm Oil viscosity cp Core area cm 2 1 Relative to oil permeability at S wi

337 638 Table B.29 Clean-State Steady-State Water-Oil and Oil-Water Relative Permeability Results NRU #1509, Core 1A (6,347.8 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 16.5 md Porosity 14.4 percent Initial water saturation, S wi 15.0 percent Oil permeability at S wi 5.06 md Net confining stress 1,500 psi Pore volume cc Back pressure 0.0 psi Initial oil volume cc Temperature 72.0 o F Initial water volume cc Water viscosity cp Core length cm Oil viscosity cp Core area cm 2 1 Relative to oil permeability at S wi

338 639 Table B.30 Clean-State Steady-State Water-Oil Relative Permeability Results NRU #3533, Core 18B (6,422.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 5.33 md Porosity 11.2 percent Initial water saturation, S wi 29.2 percent Oil permeability at S wi 2.12 md Net confining stress 1,500 psi Pore volume cc Back pressure 0.0 psi Initial oil volume cc Temperature 72.0 o F Initial water volume cc Water viscosity cp Core length cm Oil viscosity cp Core area cm 2 1 Relative to oil permeability at S wi Table B.31 Clean-State Steady-State Water-Oil Relative Permeability Results NRU #3533, Core 34B (7,191.2 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Oil, 1 (fraction) Water-Oil Relative Permeability Ratio Permeability to air 17.8 md Porosity 9.6 percent Initial water saturation, S wi 26.6 percent Oil permeability at S wi 10.7 md Net confining stress 1,500 psi Pore volume cc Back pressure 0.0 psi Initial oil volume cc Temperature 72.0 o F Initial water volume cc Water viscosity cp Core length cm Oil viscosity cp Core area cm 2 1 Relative to oil permeability at S wi

339 640 Table B.32 Clean-State Steady-State Water-Gas Relative Permeability Results NRU #1509, Core 3A (6,349.9 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Gas, 1 (fraction) Water-Gas Relative Permeability Ratio Permeability to air 2.78 md Porosity 10.3 percent Initial water saturation, S wi 24.7 percent Gas permeability at S wi 2.15 md Net confining stress 1,500 psi Pore volume cc Back pressure psi Initial gas volume cc Temperature 72.0 o F Initial water volume cc Water viscosity cp Core length cm Gas viscosity cp Core area cm 2 1 Relative to gas permeability at S wi Table B.33 Clean-State Steady-State Water-Gas Relative Permeability Results NRU #3533, Core 25B (6,952.1 ft). Water Saturation, (percent pore vol.) Relative Permeability to Water, 1 (fraction) Relative Permeability to Gas, 1 (fraction) Water-Gas Relative Permeability Ratio Permeability to air 30.5 md Porosity 12.1 percent Initial water saturation, S wi 22.1 percent Gas permeability at S wi 24.9 md Net confining stress 1,500 psi Pore volume cc Back pressure psi Initial gas volume cc Temperature 72.0 o F Initial water volume cc Water viscosity cp Core length cm Gas viscosity cp Core area cm 2 1 Relative to gas permeability at S wi

340 641 APPENDIX C COMPUTED AXIAL TOMOGRAPHY SCAN SUMMARY, FORMATION RESISTIVITY, ROCK COMPRESSIBILITY AND MINI-PERMEAMETER DATA FOR 10-ACRE INFILL WELLS C.1 Fracture Detection, Lithologic Characterization and Core Screening of Heterogeneous Carbonate Cores from the North Robertson (Clear Fork) Unit Using X-Ray Computerized Tomography 133 X-ray computer-assisted or computerized tomography (CT) scanning was used to screen carbonate core samples to be used for special core analyses. These core plugs were from four ten-acre infill wells at the North Robertson (Clear Fork) Unit (NRU) in Gaines County, Texas. The primary criterion for rejecting a core was the presence of significant quantities of anhydrite, which has little or no porosity and permeability, and could drastically affect the flowing behavior. Of the 127 core samples examined from the Upper, Middle and Lower Clear Fork Formation, approximately 67 percent were rejected. In addition, application of CT scanning identified several cores containing significant natural fractures. Most fractures appeared to have been filled and sealed with anhydrite or another cementing material. Because of the scanning resolution, no fractures less than 0.5 mm wide could be detected. CT scanning also identified several core samples containing fossils and/or fossil fragments. Finally, we noted that the significant changes in material density within core samples illustrated the heterogeneous nature of the Clear Fork carbonates. C.1.1 Principles of X-Ray Attenuation Computer-assisted tomography (CT) scanning is a nondestructive x-ray technique that produces an image of the internal structure of a cross-sectional "slice" through an object by reconstruction of a matrix of x-ray attenuation coefficients. An imaged slice can be divided into an n by n matrix of volume elements or voxels. The attenuation of x-

341 642 ray photons, G o, passing through any single voxel having a linear attenuation coefficient, ν L, reduces the number of transmitted photons to G according to Beer s law: G = G 0 exp( ν L x)....(c.1) Where: G 0 = incident x-ray intensity or number of x-ray photons entering the scanned object, G = resultant x-ray intensity or the number of x-ray photons passing through the scanned object and measured by the detector, and x = the voxel dimension in the direction of the incident x-ray. As shown by Eq. C.1, the quantity of photons passing through the scanned material depends on the material s linear attenuation coefficient. The mass attenuation coefficient is a function of both electron density and the atomic number and can be determined from: ν L = σ(e) ρ E + yz3.8 E (C.2) Where: σ(e) = Klein-Nishina coefficient (independent of the type of radiation energy) ρ E = electron density of material Z = effective atomic number of material E = photon energy y = constant = 9.8 x 10-4 The first term in Eq. C.2 represents a phenomenon called Compton Scattering, which is predominant at x-ray energies above 100 kv and depends significantly on electron density. The second term in Eq. C.2 accounts for photoelectric absorption, which is more important at x-ray energies well below 100 kv and is most affected by the atomic number of the material. For this study, all core samples were scanned at an x-ray energy of 120 kv. Because x-ray attenuation coefficients are related to the material density, the CT image shows the density at each location within the scanned object. The magnitude and lack of sensitivity of the linear attenuation coefficient to small changes in material density

342 643 makes it impractical to present results in terms of ν L, so CT images are often presented in an internationally standardized scale called the Hounsfield unit or, more commonly, the CT number. The measured linear absorption coefficient is typically normalized to that of water: N CT =1,000 ν Li ν Lw ν Lw....(C.3) Where ν Li is the linear attenuation coefficient of the scanned material and ν Lw is the coefficient for water. Normalized CT numbers for air and water are 1,000 H and 0 H, respectively. Reservoir sandstone rocks are typically in the range of 1,200 H to 1,400 H, while carbonates range from 1,600 H to 2,000 H for limestone, and 2,200H to 2,400 H for dolostone. The CT number for anhydrite will be above 2,400 H. Beer s law, which is given by Eq. C.1, describes the attenuation of x-ray energy and assumes a narrow x-ray beam and monochromatic radiation source. However, because of the polychromatic character of x-ray energy, lower energy x-rays are usually preferentially absorbed by the scanned material. Consequently, beams passing through the central region of a circular cross section have a relatively higher energy than those passing through the periphery and the computed x-ray attenuation coefficients of the central region are less than that of the outer region. This shifts the relative energy distribution of photons traversing the sample to higher energies than originally present in the incident beam. This phenomena, which is called beam hardening or "cupping," is typically manifested by an increased apparent density and a brighter image of the material around the perimeter. Beam hardening can be reduced somewhat by immersing the core sample in a clean, homogeneous and well-sorted sand. C.1.2 Description and Instrumentation of CT Scanner The CT scanner located at the Texas A&M University Imaging Center is a thirdgeneration type of medical scanner that has been modified for petrophysical applications. The scanner, which is controlled from a remote computer console, is located in a room constructed with lead-lined walls and lead-impregnated observation

343 644 windows. The CT scanner mainframe computer system is located in the same room with the radiation sources. Samples are placed in a plexiglass core holder designed to accommodate core samples up to 76.2 mm (3 in.) in diameter and 1,219.2 mm (48 in.) long. Sequential crosssectional slices are taken at different angles as the sample moves slowly through the center of the scanner yoke on which the x-ray source and multiple detectors are mounted around the periphery. Power is pulsed to the x-ray tube, creating a fan of beams that traverses a thin slice of the sample. Depending on the density of the samples, the tube voltage can be varied from 80 to 120 kv. In addition, slice thickness can range from 1 to 8 mm (0.04 in. to 0.3 in.). Between each power pulse, the yoke moves and the next x-ray fan beam traverses the same slice from a slightly different angle. As the yoke is rotated 6.3 radians (360 o ), hundreds of thousands of different x-ray projections are sent to the computer system for mathematical processing. Although most core scanning is done with the samples placed horizontally, the yoke can be also be rotated several degrees in the vertical plane, thus allowing flow experiments on an inclined plane. CT attenuation data are typically presented in an internationally standardized scale, the Hounsfield unit, which is defined by air at -1,000 H and water at 0 H. Thus, each Hounsfield unit represents a 0.1 percent change in density. For CT measurements on reservoir rock, it is more convenient to calibrate with a SiO 2 standard, such as fused quartz. Thus, a change of +1 H is equivalent to a fractional density change of g/cc for sandstone. When combined with the CT number for pure distilled water, the resulting calibration curve is a straight line. The calibration curve used by the scanner was supplemented with clean sandstone samples and extended with clean lime samples.

344 645 C.1.3 Materials and Methodology Description of Core Samples 127 core samples were taken from wells 1509, 1510, 3319, and 3533 in Sections 326, 327, 329, and 362 of the NRU. Sample depths in the Upper, Middle and Lower Clear Fork ranged from 6,169 ft to 7,220 ft. Each core sample was 38.1 mm (1.5 in.) in diameter and 76.2 mm (3 in.) long. To maintain the native-state wettability, the cores were immersed in a mixture of lease crude oil and 20/40 Ottawa sand and sealed in borosilicate glass sample bottles. The cores remained sealed in these bottles during the scanning process. The presence of the sand reduced the beam hardening effects. Measurement of Anhydrite CT Number Anhydrite is a very dense material having little or no porosity and essentially no permeability. Handbooks of material properties indicate the density of pure anhydrite ranges from 2.92 to 3.00 g/cc, however, no published values of anhydrite CT number are available. Therefore, a 76.2-mm (3-in.) diameter core sample (test core) containing large pieces of anhydrite was scanned to determine the value for anhydrite. CT numbers generated from the scanned slices range from about 2,550 H for less pure anhyrite to almost 3,500 H for pure anhydrite. Fig. C.1 shows a scanned image of one of the slices. Included with the image are the maximum and minimum CT number and a color scale showing the distribution of CT numbers for materials within the slice. The lighter material was identified as anhydrite, while the darker rock is dolomite. Therefore, for this study, anhydrite was identified for any material having a CT number greater than 2550 H.

345 646 Test Core - Slice No. 1 Anhydrite > 2,550 H CT Number Frequency Distribution Figure C.1 CT scan of test core sample used to calibrate Hounsfield scale. Scanning Technique All scans were performed using a peak voltage of 120 kv and a current of 80 ma applied to the x-ray tube. The total scan or exposure time for each slice was maintained at 8 seconds. Each slice was 5 mm wide, so 15 slices were taken for each sample. The first slice was taken at the bottom of the bottle (or core). Each individual core sample was marked with an index number, and the top and bottom of the sample can be identified by the orientation of the index number on the plug. Slice A would be at the bottom of the core plug, and each successive image was taken moving up the plug. When the core sample was not resting on the bottom of the bottle, fewer slices were taken. C.1.4 Examples of Scanned Images Some representative examples of the NRU core plug CT scans are shown in Figs. C.2 - C.8, below.

346 647 Possible Open Fracture (not anhydrite-filled) NRU 1509 Core No. 28, Slice A Well Depth = ft Min. CT No. = 2090 Max. CT No. = 2532 Avg. CT No. = 2347 CT Number Frequency Distribution Figure C.2 Sample with distinct fracture (not anhydrite-filled) NRU Massive Anhydrite Blocking Much of Core Flow Path NRU 3533 Core No. 15, Slice E Well Depth = ft Min. CT No. = 2092 Max. CT No. = 3159 Avg. CT No. = 2664 CT Number Frequency Distribution Figure C.3 Massive and continuous anhydrite shown blocking most of core's flow path rejected sample from NRU 3533.

347 648 Anhydrite-filled Fractures and/or Fingers NRU 3319 Core No. 12, Slice H Well Depth = ft Min. CT No. = 1988 Max. CT No. = 3049 Avg. CT No. = 2379 CT Number Frequency Distribution Figure C.4 Fingers and/or fractures filled with anhydrite NRU Small and Discontinuous Anhydrite Nodules NRU 1509 Core No. 3, Slice D Well Depth = ft Min. CT No. = 2157 Max. CT No. = 2604 Avg. CT No. = 2322 CT Number Frequency Distribution Figure C.5 Small and discontinuous anhydrite "nodules" NRU 1509.

348 649 Low Average CT Number - Lime or Limey Dolostone NRU 3319 Core No. 39, Slice No. 9 Well Depth = ft Min. CT No. = 1772 Max. CT No. = 2399 Avg. CT No. = 1917 CT Number Frequency Distribution Figure C.6 Sample with low CT number suggesting presence of limestone NRU Fossils Indicated by Low Density (dark) Areas NRU 1509 Core No. 33, Slice H Well Depth = ft Min. CT No. = 845 Max. CT No. = 2794 Avg. CT No. = 2321 CT Number Frequency Distribution Figure C.7 Sample with distinct fossil fragments (dark areas) NRU 1509.

349 650 Example of Silty/Shaly Streaks Within Core NRU 1510 Core No. 6, Slice E Well Depth = ft Min. CT No. = 2115 Max. CT No. = 2516 Avg. CT No. = 2272 CT Number Frequency Distribution Figure C.8 Sample with streaks of silt and/or shale NRU 1510.