Location and Geology 1. Central Study Area (CSA) of SACROC Unit of Permian Carbonates in Kelly-Snyder Field, West Texas 2.

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1 Reservoir Characterization in the Sacroc Unit of the Kelly-Snyder Field, Horseshoe Atoll, Permian Basin, Texas, via Probabilistic Clustering and Prediction Procedures Using Limited Well Log and Core Data Eric Eslinger, Eric Geoscience, Inc. and The College of Saint Rose, Albany, NY ; Reinaldo Gonzalez, Advanced Resources International, Houston, TX Reservoir quality (RQ) and flow units were defined using well log and whole core data from 24 wells in the Kelly- Snyder Field, a complex carbonate oil field (SACROC Unit) in the Permian Basin. Twenty-two of the wells were from a small "central study" area, including one that had whole core through most of the ~ 900 ft limestone interval of the Pennsylvanian-Permian aged Canyon and Cisco Formations. Two additional cored wells outside of the study area were also used. The goal of the study was to provide input into a 3D geostatistical reservoir model. Major problems were: 1. limited wells with core porosity and permeability; 2. the fact that most wells inside and outside of the study area had only neutron (NPHI) and gamma ray (GR) logs, and 3. the unavailability of core description information. The overall problem was attacked using a probabilistic clustering procedure which permits estimation of "missing" well logs where only NPHI and GR logs exist, and also estimation of porosity and permeability profiles in non-cored wells. Porosity and permeability profiles were generated for all wells; credibility was established using a series of "hold-out" tests. Flow unit reservoir quality was defined and its continuity evaluated as a function of reservoir geometry. A vertical cyclicity (defined by interpreted "sharp" 3rd to 4th order sequence boundaries) was observed that is semipervasive throughout the study area. The differences in porosity and permeability between the "good" RQ flow units (11-13 % porosity) and the "poor" RQ flow units (<5% porosity), plus the sharpness of the boundaries between them and their lateral continuity indicate that substantial vertical compartmentalization exists that could impact on-going secondary and tertiary recovery efforts.

2 Location and Geology 1. Central Study Area (CSA) of SACROC Unit of Permian Carbonates in Kelly-Snyder Field, West Texas 2. ~ 800 ft of Pennsylvanian-Permian Cisco and Canyon Formations 3. reef carbonate limestone ( Canyon Reef ) part of Horseshoe Atoll Goal provide stratigraphic & flow unit reservoir quality (RQ) input for 3D geostatistical model Method use clustering analysis to define strata of similar RQ

3 The Challenge 1. missing log curves 2. limited core 3. no core description available Data Available wells with wireline logs in the CSA a. 10 of these wells had full suite of logs (RHOB, NPHI, GR, DT) b. 12 wells had only NPHI and GR logs 2. 3 modern cored wells, 1 in CSA and 2 outside CSA; core porosity and permeability available on one-foot spacing for all 3 wells

4 What We Did 1. using clustering analysis, divided the geologic section of interest into rock types (~ electrofacies ~ flow units ) 2. interpreted the lithology of the facies units (using an internal ModeAssign routine) 3. examined the degree to which the facies units might be correlated among the wells 4. estimated values for missing data (specifically, RHOB, DT, porosity, and permeability). 5. related the flow units and flow unit packages to previous studies concerning sequence seismic stratigraphy

5 SACROC Location Map (with location of Kelly-Snyder Field) Hockley Lubbock Crosby Dickens Wellman Field Terry Lynn Garza Kent S. Brownfield Field Horseshoe Atoll 500' Salt Creek Field Cogdell Field Gaines Adair Field Mungerville Fld Dawson 500' Oceanic Field Good Fld Vealmoor Field Borden Diamond M Fld Von Roeder Fld Hobo Fld Howard Kelly-Snyder Fld Scurry SACROC Unit Sharon Ridge Unit S. Von Roeder Fld Reinecke Field E. Vealmoor Fld 0 20 Miles Midland Basin Oklahoma Texas [map from Raines, 2004, Heterogeneity at the Pennsylvanian SACROC Unit, Scurry County, TexasT exas,, who modified it from Vest (1970)]

6 SACROC 3-D 3 D Structure Central Study Area [map from Raines, 2004, Heterogeneity in the Pennsylvanian SACROC Unit, Scurry County, TexasT exas ]

7 Orange Square = Location of the Central Study Area in the Sacroc North Platform Well is the Sacroc North Platform and the study area modern cored well limited by the orange square. in The the three cored CSA; wells 2 other are shown as red circles. modern cored wells are & C D FEET PETRA 2/28/ :08:44 AM

8 South Gross Stratigraphy North Subsea Depth Total Length of Cross-Section is miles Wolfcamp Shale Cisco Canyon Oil / Water Contact HS=9000 SACROC Unit Horizontal Scale = Ft. Vertical Scale = Vertical Exaggeration = 72.0x [from Raines, et al., A Review of, 2001] -4600

9 2-3 Core Permeability (log 10) vs Core Porosity all 3 cored wells 0 25

10 Clustering Method: (software = GAMLS) Analyst selects wells, depth ranges, clustering variables, # of modes desired, and initialization method Samples are assigned probabilistically to n-dimensional clusters with similar properties Cluster ~ mode ~ electrofacies ~ rock type ~ flow unit Distribution of each mode for each variable is modeled as a Gaussian distribution For each variable, the sum of all modes is modeled to approximate true distribution Modeling is done in v-space, where v = number of variables used in clustering Maximum likelihood solution is attained

11 Clustering analysis setup for C1A 12 wells, 10 modes; variables used: RHOB, NPHI, GR, DT; no missing log data 3 modern cored wells: (in the heart of the study area) and and (about 8000 ft to the NW & NNW of the central study area)

12 Cluster Run C1A RHOB vs NPHI crossplot for cored Well lithology interpretations for each mode the ellipses are 4-dimesional ellipsoids projected onto the RHOB- NPHI plane, and are drawn at ~ 2 SD from the mean of each ellipsoid; samples are color coded according to their crisp (highest) mode assignment

13 3D plot (RHOB, NPHI, GR) of all wells included in cluster run C1A

14 Cored wells (left) and (right) with logs, corpor, coreperm, and results of clustering analysis C1A C1A: 11 wells, 10 modes; RHOB, NPHI, GR, DT; no missing log data

15 Cluster Run C1A ModeAssign (automatic lithology assignment routine) a mode is a group of samples having similar multi-dimensional character; we consider a mode to be equivalent to a lithofacies or a flow unit; in this run, 4 variables (well logs) were used so the classification was done in 4 dimensions all lithology assignments are subject to user-input logic criteria and can be overruled given core data or other info; in this cluster run, all modes in the Cisco and Canyon sections were interpreted to be limestones; based on apparent porosity from the mean NPHI values, the limestone modes have been ordered from top to bottom in order of decreasing reservoir quality (RQ); mode 10 (colored red) has the highest apparent porosity of 13% (mean NPHI = 0.13)

16 Cluster Run C1A mean and standard deviation for selected variables (well log & core data) for each mode (lithology) in Well 5. Well_37_11 M10_LsBest M9_Slt M6_Sh1 M4_Sh2 M3_Ls1 M5_Ls2 M8_Ls3 M1_Ls4 M7_Ls5 M2_Ls6 * BULK_DENS (Raw) * ± ± ± ± ± ± ± ± ± CALI (Raw) ± ± ± ± ± ± ± ± ± DEEP_LAT (Li) ± ± ± ± ± ± ± ± ± * DT_LOG (Raw) * ± ± ± ± ± ± ± ± ± * GR (Raw) * ± ± ± ± ± ± ± ± ± * NPHI (Raw) * ± ± ± ± ± ± ± ± ± SHALL_LAT (Raw) ± ± ± ± ± ± ± ± ± AI_GAMLS (Raw) ± ± ± ± ± ± ± ± ± K0 (Li) ± ± ± ± ± ± ± ± ± K90 (Li) ± ± ± ± ± ± ± ± ± KV (Li) ± ± ± ± ± ± ± ± ± CORPOR (Mu) ± ± ± ± ± ± ± ± ± core perm core por Cluster Run C1A enlargement (from above) of the 3 modes with the best reservoir quality NOTE: the perm values listed are logarithm (base 10) of core perm 5. Well_37_11 M10_LsBest M3_Ls1 M5_Ls2 % ± ± ± DT (Raw) ± ± ± * BULK_DENS (Raw) * ± ± ± CALI (Raw) ± ± ± DEEP_LAT (Li) ± ± ± * GR (Raw) * ± ± ± * NPHI (Raw) * ± ± ± AI_GAMLS (Raw) ± ± ± K0 (Li) ± ± ± K90 (Li) ± ± ± KV (Li) ± ± ± CORPOR (Mu) ± ± ±

17 Results of Predicting Porosity and Permeability Using Models Built from Clustering Analyses: Results of two different prediction models: each model was used to predict por & perm in hold-out tests wherein the real core poor & perm were held out ; the results are shown below for predicted porosity (blue) and predicted permeability (green) along with the core data (red); these models were used to predict porosity and permeability in all of the non-cored wells Cored well Porosity tracks Permeability tracks Cored well Porosity tracks Permeability tracks 6500 ft 6500 ft 6500 ft 6750 ft 6750 ft 6750 ft 7000 ft 7000 ft 7000 ft

18 Example of prediction of porosity, permeability, and RHOB in a non-cored well (33-15, cluster run C1A) Track 1: probabilistic ( fuzzy ) lithology Track 2: most probable ( crisp ) lithology Track 3: predicted permeability Track 4: predicted porosity Track 5: RHOB (blue), NPHI (green), and AI = Acoustic Impedance (orange) This well had no RHOB or DT log; both were predicted and the AI was computed from the predicted RHOB and DT

19 2-2 Permeability (log 10 ) Cluster C1A non-cored Well ClusterC1A - Well (no no core core data, and RHOB no RHOB & DT logs or DT available) logs M10_LsBest: R 2 = M9_Slt: R 2 = M6_Sh1: R 2 = M4_Sh2: R 2 = M3_Ls1: R 2 = M5_Ls2: R 2 = M1_Ls4: R 2 = M7_Ls5: R 2 = M2_Ls6: R 2 = Predicted permeability (log 10 ) versus predicted porosity for each of the facies Porosity Predicted permeability (log 10 ) vs predicted porosity Modes are color coded to the crisp mode assignment, and the lithology of each mode has been interpreted This well had no RHOB or DT log; both were predicted and the AI was computed from the predicted RHOB and DT

20 The clustering and prediction procedure generates: 1. RHOB profiles and DT profiles (where those logs do not exist) 2. porosity and permeability predictions (for non-cored wells) Also, the following: 3. modeled distribution of beds (number, thicknesses..) 4. statistics (mean & standard deviation) for predicted porosity and permeability for each mode (~ flow unit) Therefore, the following can be interpreted: 5. bedding style and continuity (via interwell correlation) and thus the number and position of likely flow barriers 6. a ranking of the reservoir quality (RQ) of flow units based on the statistics (mean and stnd dvn) of Por & Perm for each unit Also, for upscaling purposes, the bedding style (1-D geometry) can be simplified by either dropping modes or by using a bed thickness filter

21 C1A, non-cored well bed and bed thickness data for limestone flow units (no bed thickness filtering) 1. Well_33_15 M10_LsBest M3_Ls1 M5_Ls2 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 17.4% 11.8% Well_33_15 M8_Ls3 M1_Ls4 M7_Ls5 M2_Ls6 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 13.1% 3.3% 19.3% All Wells M10_LsBest M3_Ls1 M5_Ls2 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 9.4% 15.0% All Wells M8_Ls3 M1_Ls4 M7_Ls5 M2_Ls6 Beds Thickness (ft) Avg Thick (ft) % Avg Prob C1A, all 12 wells bed and bed thickness data for limestone flow units (no bed thickness filtering) % 17.4% 2.3% 16.5%

22 5. Well_37_11 M10_LsBest M3_Ls1 M5_Ls2 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 8.2% 19.1% Well_37_11 M8_Ls3 M1_Ls4 M7_Ls5 M2_Ls6 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 23.9% 0.0% 18.8% All Wells M10_LsBest M3_Ls1 M5_Ls2 Beds Thickness (ft) Avg Thick (ft) % Avg Prob % 8.6% 14.5% All Wells M8_Ls3 M1_Ls4 M7_Ls5 M2_Ls6 Beds Thickness (ft) Avg Thick (ft) % Avg Prob C1A, non-cored well bed and bed thickness data for limestone flow units (bed thickness filter applied minimum thickness = 10 feet) C1A, all 12 wells bed and bed thickness data for limestone flow units (bed thickness filter applied minimum thickness = 10 feet) % 17.3% 1.4% 18.5%

23 C1A, non-cored well mean & standard deviation of predicted porosity and predicted permeability (log 10) for each of the 7 limestone facies the predicted porosity and permeability values were generated in cluster run C9 the flow units were defined in cluster run C1A 1. Well_33_15 M10_LsBest M3_Ls1 M5_Ls2 C9_KO_P (Raw) C9_POR_P (Raw) ± ± ± ± ± ± Well_33_15 M8_Ls3 M1_Ls4 C9_KO_P (Raw) C9_POR_P (Raw) ± ± ± ± Well_33_15 M7_Ls5 M2_Ls6 C9_KO_P (Raw) C9_POR_P (Raw) ± ± ± ±

24 reducing 10 lithology units (modes) to modes 4 modes 10 modes 4 modes 3 cored wells, C1A tracks with 10 modes are original C1A results tracks with 4 modes are after dropping 6 modes and reiterating the probability assignments to convergence modes 4 modes

25 C1A, after reducing 10 lithology units (modes) to 4 units for all 12 wells

26 Upscaling : Effect of using a Bed Thickness Filter (this & next 3 views) C1A crisp modes displayed (bed thickness filter not applied) 201 beds in Well

27 C1A crisp modes displayed (beds thickness filter applied; minimum bed thickness = 5 ft) 66 beds in Well

28 C1A crisp modes displayed (beds thickness filter applied; minimum bed thickness = 10 ft) 39 beds in Well

29 C1A crisp modes displayed (beds thickness filter applied; minimum bed thickness = 20 ft) 24 beds in Well

30 C1A C1B C1C C1D C1E Which realization should be used? 5 realizations for well in each column, bright red is highest por & perm, and purple is lowest; the blues and purples are barriers to vertical flow although each realization if from a different clustering run, the results are very similar to one another; this illustrates the robustness of the clustering process for defining flow units

31 C3 36-D C3 36-D C3 36-D C3 36-D C3 36-D C3 36-D A A B B C C C3 36-D C3 36-D C3 36-D C3 36-D C3 36-D C3 36-D A A B B C C Cross-section lines through the Central Study Area

32 Cross- Section C-C low por & low perm flow barriers Cumulative Mode Probability (CMP) plots for 7 wells along a N-S cross section; two prominent barriers to vertical flow are indicated (green arrows) these barriers separate the section into 3 zones: A, B, C

33 Cross-Section A-A Cumulative Mode Probability (CMP) plots for 6 wells along an E-W cross section Approximate position of zones A, B, & C are indicated at the position of Well 37-5

34 Relationship of lithologic units & major barriers to seismic sequences The wells here are hung on the base of a low RQ bed (dark blue) that we interpret to be the base of sequence boundary (SB) top C1 identified by Waite (1993); this (see lowest yellow line) represents a flooding surface the position of Waite s SB top C4 is also interpreted between the Canyon C & the Canyon B2 top Strawn (?) Canyon C & Cisco top C4 SB Canyon B2 Canyon B1 top C1 SB Canyon A ft A B C 6500 ft 6750 ft 7000 ft

35 N Z E An example of how data was used in the 3D reservoir modeling: left = porosity, right = permeability Development and Application of an Integrated Clustering/Geostatistical Approach for 3D Reservoir Characterization, SACROC Unit, Permian Basin R.J. González, SPE, and S.R. Reeves, SPE, Advanced Resources International, Inc., E. Eslinger, Eric Geoscience, Inc. and The College of Saint Rose, and G. García, Kinder Morgan CO2 Company., SPE PP; SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, U.A.E., October 2007.

36 Conclusions 1. An electrofacies (flow units) stratigraphy was generated using a multi-well probabilistic clustering procedure using RHOB, NPHI, GR, and DT well logs as clustering variables. 2. Profiles for RHOB and DT were generated for wells with no RHOB and DT logs. Also, using clustering runs that included core porosity and permeability from cored wells, profiles for porosity and permeability were generated for non-cored wells. 3. In the clustering run that was used for many analyses (cluster C1A), 10 modes were used; the 3 modes (flow units) with best RQ had mean porosity of 12, 11, and 10 %. 4. A vertical cyclicity (defined by interpreted "sharp" 3rd to 4th order sequence boundaries) was observed that is semi-pervasive throughout the study area. The differences in porosity and permeability between the "good" RQ flow units (11-13 % porosity) and the "poor" RQ flow units (<5% porosity), plus the sharpness of the boundaries between them and their lateral continuity indicate that substantial vertical compartmentalization exists that could impact on-going secondary and tertiary recovery efforts. 5. This procedure (defining flow units via clustering) appears to be applicable for any type of reservoir rock and is very useful when there is much missing data (e.g., missing well log data or core data). 6. Each clustering run is a realization. Although many different realizations were generated, the results were similar. This demonstrates the robustness of the procedure in terms of obtaining a similar flow unit stratigraphy using different clustering run setup inputs. 7. The number of electrofacies (modes) used is made by the analyst. The ten modes defined in cluster run C1A (for instance) was an arbitrary number. Different clustering runs (resulting in different realizations) could be made using more or fewer modes. Alternatively, given a clustering run with ten modes, the resulting stratigraphy can be simplified by deleting selected modes. Another method for simplifying is to perform an upscaling procedure via a beds thickness filter that eliminates thin beds. The results of both of these procedures was demonstrated.

37 References Eslinger, E., Procedures for Lithology Characterization and Probabilistic Upscaling (Curve "Blocking") Using Petrophysical and Core Data, AAPG National Meeting (poster session), Long Beach, CA, April 3, González, R. J., 2007, Reeves, S. R., Eslinger, E., and G. García, 2007, Development and Application of an Integrated Clustering/Geostatistical Approach for 3D Reservoir Characterization, SACROC Unit, Permian Basin., SPE PP; SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, U.A.E., October. Raines, M. A., 2004, Heterogeneity in the Pennsylvanian SACROC Unit, Scurry County, Texas, in Carbonate Reservoir Characterization and Simulation: From Facies to Flow Units, AAPG HEDBERG CONFERENCE, March 15-18, 2004, El Paso, Texas. Raines, M., Dobitz, J.K., and Wehner, S.C., 2001, A Review of the Pennsylvanian SACROC Unit, 2001, paper presented at West Texas Geological Society Fall Symposium, Midland, TX, Oct Waite, L.E., 1993, Upper Pennsylvanian Seismic Sequences and Facies of the Eastern and Southern Horseshoe Atoll, Midland Basin, West Texas, in Carbonate Sequence Stratigraphy, AAPG Memoir 57, Robert G. Loucks and J. Frederick Sarg (eds.), pp , The American Association of Petroleum Geologists, Tulsa, OK.