Formation Evaluation of an Unconventional Shale Reservoir: Application to the North Slope Alaska

Transcription

1 1 Formation Evaluation of an Unconventional Shale Reservoir: Application to the North Slope Alaska A REPORT SUBMITTED TO THE DEPARTMENT OF ENERGY RESOURCE ENGINEERING OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN PETROLEUM ENGINEERING By MINH TUAN TRAN June 2014

2 2 I certify that I have read this report and that in my opinion it is fully adequate, in scope and in quality, as partial fulfillment of the degree of Master of Science in Petroleum Engineering. Professor Tapan Mukerji (Principal Advisor)

3 3 I. Abstract: Organic-rich shale (ORS) has become an increasingly important hydrocarbon resource around the globe due to rapid depletion of conventional reservoirs. Successful exploration and production schemes for ORS should base on reliable identification of major organic components (kerogen in particular) and their hydrocarbon-generating potential. There is a growing need to identify organic content in terms of quantity (Total Organic Carbon TOC) and quality (kerogen type, thermal maturity) in promising shale formations through indirect seismic data, which is usually the only available source of information in most exploration phases. The objective of this study is to delineate different seismic lithofacies in North Slope Alaska (NSA) region in terms of elastic/seismic and petrophysical properties based on core and logging data. A seismic lithofacies is not necessarily a single rock or formation but rather a collection of geologically similar rocks that span a comparable range of petrophysical and seismic properties (Avseth et al., 2005). A seismic lithofacies shares characteristic sedimentologic and rock physics properties, thus serving as a major force in controlling reservoir geometry and porosity distribution (Avseth et al., 2005). In this study, background geology, standard triple combo logging suites, petrophysical and geochemical analysis of core plugs are basic inputs to obtain facies definition, which is the very first step of a more comprehensive statistical rock physics evaluation workflow. Key wells with the most complete dataset in the area of interest are two vertical wells drilled by Great Bear Petroleum LLC, Merak-1 and Alcor-1. Rock physics templates (RPTs) of seismic parameters (Acoustic Impedance AI versus P-wave over S-wave ratio Vp/Vs) are constructed for each facies to facilitate assessments of pore fluid distribution and lithology variation. Another goal is to create useful correlations between source rock attributes (TOC, Hydrogen Index HI) and petrophysical properties (bulk density/porosity, GR, sonic velocities) of major NSA lithofacies. A petrophysical model proposed by Alfred and Vernik in 2012, which has been successfully tested in Bakken shale, will be tested in the area of interest to take into account kerogen porosity. These correlations, together with facies-specific RPTs, assist in mapping organic richness and reservoir properties from seismic-derived attributes. The third goal is to explore elastic anisotropy of NSA shale in both core plugs and logging measurements. This provides a preliminary insight into possible sources of shale anisotropy in NSA, thus enhancing the prospect of applying seismic anisotropy attributes (Amplitude-versus- Offset data for example) to explore source rock potentials in NSA.

4 4 II. Acknowledgement First of all I would like to sincerely thank my advisor Prof. Tapan Mukerji for his support and encouragement on this work and throughout my graduate study. Without him, this work would not have been possible. I am looking forward to future opportunities cooperating with him in both academic pursuit and professional work. I would like to thank Great Bear Petroleum LLC for providing the financial support and the comprehensive dataset of Merak-1 and Alcor-1. All the sponsors of Stanford Center for Reservoir Forecasting (SCRF) and Basin and Petroleum System Modeling (BPSM) groups are acknowledged. I would like to thank Allegra Scheirer, Ken Peters, Les Magoon, and my classmates for their valuable inputs and guidance. I would also like to thank all my friends for their support and time to make my life at Stanford much more joyful. Finally I would like to thank all my parents and sister; for their timely encouragement and limitless care. Most importantly I would like to thank my dear wife Van Bui for her irreplaceable companion and long distant love in the past 6 years.

5 5 III. Contents I. Abstract:... 3 II. Acknowledgement... 4 III. Contents List of tables: List of figures:... 6 IV. Introduction V. Geological setting VI. Dataset Description VII. Methodology VIII. Seismic Lithofacies Delineation: Logging Analysis: Core data analysis Rock physics template: IX. Application of existing petrophysical models X. Preliminary shale anisotropy characterization XI. Conclusion and Future Work XII. References XIII. Appendix... 55

6 6 1. List of tables: Table 1: Average source rock properties of major shale formations in NSA (Peters et al. 2006) Table 2: Merak-1 core plug set for ultrasonic measurement. WF=Weatherford. In current literature, Shublik is subdivided into four smaller units (A, B, C and D) to emulate its complex, highly heterogeneous nature Table 3: Alcor-1 core plug set for ultrasonic measurement. Kingak is not available for coring in Alcor. Due to pre-existing fracture, it is difficult to obtain horizontal and 45-degree plugs Table 4: XRD analysis of Alcor-1 core plugs, covering HRZ and Shublik intervals. Illite is the main clay component in both shale units. Minerals that are of significant amount are quartz, carbonate and illite Table 5: Simplified composition for HRZ, Kingak and Shublik to use as inputs of soft sediment model Table 6: Elastic moduli of different minerals (Table 2.1, Avseth et al., 2005). NSA kerogen elastic properties are extremely limited so typical values of kerogen modulus and density at similar maturity level from other shale plays are taken (Vernik 1994) Table 7: Original plug porosity and kerogen-modified porosity based on Alfred and Vernik's model to account for kerogen porosity List of figures: Figure 1: Diagrams showing the proportion of undiscovered, technically recoverable oil and gas resources of Alaska by regions, including onshore and offshore (Bird, 2001) Figure 2: Generalized stratigraphic column for North Slope Alaska, emphasizing potential petroleum source rocks, their relative ages and thickness across a cross-section. GRZ=high GR zone. The Lower Cretaceous unconformity (LCU) lies right under Pebble shale unit (Bird 2001) Figure 3: Map showing major tectonic features of Northern Alaska. ANWR=Arctic National Wildlife Refuge; NPRA=National Petroleum Reserve-Alaska, PB=Prudhoe Bay (Bird, 2001) Figure 4: Ternary diagram shows shale classification of Hue/HRZ and Shublik (blue triangles) based on limited XRD analysis in Alcor-1 (Allix et al. 2010). Other notable shale plays are also presented in the diagram Figure 5: Rock-Evaluation pyrolysis S2 peak (mg hydrocarbon/g TOC) versus TOC of 408 thermally immature and early-mature samples shows that the quantity and quality of organic matter in the Shublik Formation exceed the other three source rocks. Slopes of radiating lines equal Hydrogen Index (100*S2/TOC) that distinguish organic matter types (Peters et al., 2006) Figure 6: Focus area is located between the NPRA and ANWR. The area of interest shows locations of two vertical wells of interest: Alcor-1 and Merak-1. The blue dashed line indicates the area of available 3-D seismic data. Yellow blocks show Great Bear leases in NSA. Two wells Alcor-1 and Merak-1 are 1.5 miles apart and located along the Trans-Alaskan Pipeline (green dashed line) Figure 7: Diagram showing how three core plugs of different directions are taken out of core slab of Merak-1 well at depth Note that pyrite (brownish in the upper right corner) as well as fractures on the surface of the slab are intentionally avoided. Bedding is clear at this depth so 3 core plugs are taken. V=vertical/bedding-normal, H=horizontal/bedding-parallel, 45=45-degree-to-bedding Figure 8: Diagram showing quantitative seismic interpretation workflow with integration of geochemical data. In this study, we focus on the parts of the workflow that related to the construction of a reliable elastic and geochemical training dataset of each pre-defined lithofacies

7 Figure 9: Diagram showing typical logging tracks used for qualitative delineation of NSA lithofacies. From left to right for Alcor-1 well: GR (API unit), Compensated Bulk Density (gm/cc), P and S wave velocity (m/s) and Vp/Vs Figure 10: Diagram showing picks for top and bottom depth of each shale of interest. Matlab is used to color-code each facies and index their numerical values. From top to bottom: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). This color code is used throughout this study Figure 11: P and S wave velocities (m/s) versus bulk density (gm/cc) of different shale lithofacies in two wells: Merak at the top and Alcor at the bottom. Graphs are of similar scale for comparison Figure 12: P and S wave velocities (m/sec) versus GR (API unit) of different NSA shale lithofacies in two wells: Merak-1 at the top and Alcor-1 at the bottom. Graphs are of similar scale for comparison Figure 13: Crossplot of P-wave Velocity (m/sec) versus Density (gm/cc) in Merak-1, color-coded by GR showing reasonable trends in Kingak, HRZ and Pebble shale. Hot color indicates higher GR while cold color indicates lower GR. GR is a good indicator for Kingak shale trend since high GR and low GR points stack nicely along the velocity-density trend Figure 14: Crossplot of S-wave Velocity versus Density in Merak-1, color-coded by GR showing reasonable trend in Kingak and HRZ. GR is a good indicator for Kingak shale since Vs-density trend show separate clusters for high GR and low GR points Figure 15: Vs versus Vp from dipole sonic log of two wells Merak and Alcor. Blue dashed lines represent constant Vp/Vs ratio. Plots are of similar scale for comparison Figure 16: Relationship between compressional and shear velocity for bedding-normal (0 0 ) for Bakken, Woodford and Bossier shale from dipole sonic logs. Dashed lines also indicate constant Vp/Vs ratio (Vernik and Milovach, 2011). Reduced velocity ratio is observed in organic-rich shale compared to its inorganic counterpart Figure 17: S 2 peak (mg HC/g rock) versus TOC (wt %) of core plugs in Geomark dataset. Black lines indicate different Level of Maturity LOM as defined by Passey et al Figure 18: Cross-validation of TOC logs created by Passey method (blue lines) and geochemical core data (pink dots) for different lithofacies in two wells. From left to right: Merak Hue, Merak Kingak, Merak Shublik, Alcor Hue/HRZ, Alcor Shublik Figure 19: Vp/Vs ratio (log-derived) versus Dry and Wet (As-received or AR) bulk density (gm/cc) of core plugs in Alcor-1 well. Only Kingak shows slight velocity ratio increase as shale gets more compacted (bulk density increases) Figure 20: Cross-validation of density values between core and log measurements. The diagonal 45- degree slope line indicates consistency of HRZ and Shublik samples while Kingak samples need further calibration. Shublik, Hue and HRZ show good consistency as most of the data points fall onto the diagonal 45 degree line while Kingak shows greater value of core density compared to logging results. 33 Figure 21: P and S-wave velocities (m/sec) versus bulk density (gm/cc). Log value is denoted as circle, asreceived core as diamond and dry core as star. Saturation of as-received core does not change bulk density much because NSA shale has low porosity. Kingak log values of density are lower than core values possibly due to sampling bias of core plugs towards pyrite-free and unfractured intervals Figure 22: Density (gm/cc) and P-wave velocity (m/sec) versus Tmax (degree C). Density shows its little dependence on maturity due to its weak correlation within each lithofacies Figure 23: P and S-wave velocity (feet/sec) versus HI. Each data cluster is well separated. The correlation is weaker compared to velocity-density correlation

8 Figure 24: P and S wave velocity (m/sec) versus TOC (weight percentage). No correlation is recognized even though the clusters are relatively well separated Figure 25: P-wave velocity (Vp in feet/sec) versus HI of other shale plays. Vp is inversely proportional to HI. Within a single formation, the correlation between Vp and HI is reasonable and the scatter is greatly reduced (Prasad et al., 2002a) Figure 26: P and S wave velocity versus log-derived TOC values for Merak-1. TOC and acoustic velocities show a strong directly proportional correlation in Shublik Figure 27: P and S wave velocity versus log-derived TOC values for Alcor-1. TOC and acoustic velocities show a strong directly proportional correlation in Shublik Figure 28: Crossplot of AI versus Vp/Vs of Hue/HRZ, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR/clay content increases, both velocity ratio and AI tend to decrease. The colorbar indicates GR magnitude. Cluster of points in the red circle (upper left corner) are at the same interval that logging equipment switch happens and may need to be removed to align with the trend Figure 29: A rock physics template (RPT) of Hue/HRZ presented as cross-plots of Vp/Vs versus AI includes a rock physics model locally constrained by depth (i.e., pressure), mineralogy, critical porosity and fluid properties. The template includes porosity trends for different fluid saturation (from fully watersaturated Sw=1 to fully gas-saturated Sw=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (2) increasing shaliness, (3) increasing gas saturation Figure 30: Crossplot of Vp/Vs versus AI of Pebble shale unit. Density is not a driving force behind this trend Figure 31: A rock physics template (RPT) of Kingak presented as cross-plots of Vp/Vs versus AI. The template includes porosity trends for different fluid saturation (from fully water-saturated Sw=1 to fully gas-saturated Sw=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (2) increasing shaliness, (3) increasing gas saturation. The trend of increasing shaliness is shown in Figure Figure 32: A rock physics template (RPT) of Shublik presented as cross-plots of Vp/Vs versus AI. The template includes porosity trends for different fluid saturation (from fully water-saturated S w=1 to fully gas-saturated S w=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (3) increasing gas saturation. The trend of increasing shaliness is not clear as shown in Figure Figure 33: A rock physics template (RPT) of NSA presented as cross-plots of Vp/Vs versus AI. Colorbar indicates different magnitudes of bulk density. Shale porosity of soft sediment model (using average values of composition of all NSA ORS lithofacies) is drawn for reference Figure 34: A rock physics template (RPT) of NSA presented as cross-plots of Vp/Vs versus AI. Colorbar indicates different magnitudes of GR. Shale porosity of soft sediment model (using average values of composition of all NSA ORS lithofacies) is drawn for reference Figure 35: The combined domain of pore system. Organic domain contains solid organic matter (kerogen), organic porosity (filled with hydrocarbon Sw k). Non-kerogen domain contains solid inorganic matter (host rock or matrix) and inorganic porosity (filled with water Sw nk). k-kerogen, nk-non-kerogen 8

9 matrix, fk-fluid in organic domain, nfk-fluid in non-organic domain, b-bulk property. V k is the volume of the organic domain (kerogen matrix and its porosity) and V nk is the volume of the inorganic domain (host rock matrix and its porosity) (Alfred and Vernik 2012) Figure 36: The combined domain system with allocations of volumes. K is volume fraction of kerogen in the solid part of the domain (Alfred and Vernik 2012) Figure 37: Correlation between kerogen density and thermal maturity through studying core data of various shale plays (Alfred and Vernik 2012) Figure 38: Apply the modified porosity to account for pore spaces in kerogen, a much better correlation between velocity and porosity is observed (R 2 =0.8). Prasad s relationship obtained from other shale plays is superimposed for comparison in the P-wave chart. Velocities in feet/sec, PHI in percentage. My correlation formulas are given in the box Figure 39: Correlation between P-wave velocity (feet/sec) and Kerogen-modified porosity in other shale plays. Velocity correlates very well with kerogen volumetric content if we assume that about 40% of the kerogen acts as pore space to soften the rock. The correlation coefficient between velocity and modified porosity is now significant (R 2 =0.7) and does not depend on formation (Prasad et al., 2009) Figure 40: Cross-dipole acoustic tool measure velocities of two different directions of shear wave polarization. Percentage of difference is plotted in the right with values range from 5-10 percent difference Figure 41: Experiment set-up. The right picture shows the oscilloscope. The left picture shows the transducer and the core holder. Molasses is used to improve the acoustic coupling between transducers and core sample Figure 42: P and S-wave velocities versus Angle to Bedding of Shublik core plugs at different depth and orientations. 0 degree means parallel to the bedding. 90 degree means normal to the bedding Figure 43: Map of northern Alaska showing exploratory drilling density, pipeline infrastructure, and land ownership. North of the Brooks Range, federal ownership includes NPRA, ANWR and the offshore beyond the state-federal three-mile boundary. Ownership of nonfederal lands is divided between the state and Native American organizations. TAPS=Trans-Alaska Pipeline System (Ken Bird 2001) Figure 44: Formation tops of all rock units in Merak-1. True Vertical Depth TVD is used in log analysis as it corresponds to the depth in my vertical type well. Rocks of interest are Hue, HRZ, Pebble, Kingak and Shublik. True Vertical Depth TVD is comparable to logging depth since both wells are vertical Figure 45: Formation tops of all rock units in Alcor-1. Rocks of interest are Hue, HRZ, Pebble, Kingak and Shublik. True Vertical Depth TVD is comparable to logging depth since both wells are vertical Figure 46: Diagram showing typical logging tracks used for qualitative delineation of NSA lithofacies. From left to right for Alcor-1 well: GR (API unit), Compensated Bulk Density (gm/cc), P and S wave velocity (m/s) and Vp/Vs. Diagram showing picks for top and bottom depth of each shale of interest. Matlab is used to color-code each facies and index their numerical values. From top to bottom: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). This color code is used throughout this study Figure 47: Young s modulus and Bulk Modulus versus Poisson Ratio in Merak-1. Each lithofacies clusters show a distinctive trend between bulk modulus and Poisson ratio. Shublik separate itself from other clusters. In this figure, color code is: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik)

10 10 Figure 48: Young s modulus and Bulk Modulus versus Poisson Ratio in Alcor-1. In this figure, color code is: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). Several Hue shale data points have Poisson Ratio value of 0.5, which need to be removed Figure 49: Well-to-well cross correlation based on TOC and GR logs (Ken Bird 2012). Two wells of interest are 1.5 miles apart and have shown excellent correlation in terms of petrophysical properties and source rock character Figure 50: Organic mudstone classification (Gamero-Diaz et al. 2012) Figure 51: Crossplot of AI versus Vp/Vs of Kingak, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude Figure 52: Crossplot of AI versus Vp/Vs of Pebble, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude Figure 53: Crossplot of AI versus Vp/Vs of Shublik, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude IV. Introduction Alaska, one of the least explored regions in the United States, is estimated to contain approximately 40% of total U.S. undiscovered, technically recoverable oil and natural gas resources, the bulk of its resources coming from Northern Alaska with more than 30 billion barrels of oil and nearly 200 trillion cubic feet of natural gas (Figure 1, Bird 2001). Shale oil is gaining abundant attention because of increasingly depleted conventional reservoirs and more advanced technology to develop this resource. Exploration is mostly at an immature stage except the region near the coastline located between the National Petroleum Reserve Alaska (NPRA) and the Arctic National Wildlife Refuge (ANWR) known as Prudhoe Bay. Fewer wells have been drilled outside of Prudhoe Bay region (Figure 43), thus only sparse information for proper formation evaluation and lithofacies classification is available. Traditionally, formation evaluation and production planning of shale formations pose challenging problems due to their complex lithology, rapid areal

11 11 and vertical variation of petrophysical properties. A key issue for future exploration of the NSA is the lateral variability of source rock away from known hydrocarbon accumulations (Keller et al., 1999). Figure 1: Diagrams showing the proportion of undiscovered, technically recoverable oil and gas resources of Alaska by regions, including onshore and offshore (Bird, 2001). This study attempts to characterize petrophysical, geochemical and elastic properties of major NSA shale lithofacies and build a reliable training dataset (P and S-wave velocities, bulk density) for rock classification purposes. Previous rock classification techniques introduced in organic shale formations are strongly dependent on a large number of core measurements to reasonably capture shale heterogeneity, which are both time-consuming and expensive. Gupta et al. (2012) conducted rock classification in the Woodford shale based on 300 core samples from six different wells using measurements of TOC, porosity and clay/quartz concentration. Due to sparse core information in my study area, well log is a viable candidate for rock classification as it provides relatively high vertical sampling resolution, continuous interval properties and realtime, more economical alternative. Cross-validation and proper calibration of log-derived properties with limited core data are regularly performed throughout the study. Existing shale petrophysical models, calibrated and constrained to NSA geology, yield various outputs which are then verified by my training dataset to observe their applicability. In addition, integration of available geochemical data (TOC, HI and Thermal Maturity R0) into the training dataset is performed by building reliable connections between elastic properties and geochemical parameters

12 12 of different shale lithofacies. The ultimate objective, which is outside the scope of this work, is to facilitate the use of seismic signatures to evaluate source rock potential in newly explored shale formations. Several factors may contribute to the elastic anisotropic behavior of shale, which can be classified as either intrinsic anisotropy or induced anisotropy. Intrinsic anisotropy is commonly due to the inherent layering in the formations based on the distribution and orientation of clay particles, kerogen matters and pore spaces in micro-scale (Tutuncu, 2010). Level of maturation of kerogen in shale also plays a central role in overall mechanical and elastic properties as expulsion of oil and gases introduces microcracks and fractures changing the texture of shale (Tutuncu 2010). Shale also exhibits heterogeneous anisotropy: in high-porosity shale, porosity is a primary factor controlling wave propagation speed whereas in low-porosity shale, bedding angle and hydrocarbon maturity/quantity are principal forces. On the other hand, induced anisotropy is influenced by anisotropic in-situ principal stress condition, which often results in differential closure of microcracks in subsurface formations. Cracks that are aligned perpendicular to the major principal stress have a higher tendency of being closed than cracks aligned in other directions (Tutuncu, 2010).

13 13 V. Geological setting Four major source rock units have been stratigraphically identified in NSA, named as Hue, Pebble, Kingak and Shublik (Figure 2). The High Radioactive Zone (HRZ) at the bottom of Hue shale will be later separated from the Hue shale because of its different petrophysical signature. The most important and relevant geological features (Figure 3), depositional history and source rock characters will be discussed here. Figure 2: Generalized stratigraphic column for North Slope Alaska, emphasizing potential petroleum source rocks, their relative ages and thickness across a cross-section. GRZ=high GR zone. The Lower Cretaceous unconformity (LCU) lies right under Pebble shale unit (Bird 2001).

14 14 Figure 3: Map showing major tectonic features of Northern Alaska. ANWR=Arctic National Wildlife Refuge; NPRA=National Petroleum Reserve-Alaska, PB=Prudhoe Bay (Bird, 2001). The Triassic Shublik formation is relatively thin (less than 300 feet), regionally extensive and lithologically heterogeneous consisting of limestone, sandstone, siltstone, phosphatic nodular shale and calcareous shale (Parrish 1987). Shublik facies south of the Barrow Arch, part of the Ellesmerian sequence, is of particular economic interest because it is the principal source of oil and gas generation in the North Slope region, accounting for more than 90% of the recoverable crude oil and 82% of the recoverable hydrocarbon gases (Bird, 2001). It is organically enriched (TOC ranges from 0.5 to 13.1%), ranging from a strongly oil-prone Type I kerogen to a more gasprone Type III kerogen (Robinson et al. 1996). The lower part of Shublik Formation is part of a transgressive systems tract dominated by laminated marls and shales deposited under suboxic to anoxic conditions (Peters et al. 2006). This organic-rich facies of the Shublik Formation was deposited as black limestone, marl and mudstone on a subsiding marine shelf characterized by upwelling and anoxic conditions (Parrish, 1987; Parrish et al., 2001). The upper, regressive Shublik contains bioturbated shale having mainly gas-prone or inert organic matter caused by bioturbation and more oxic conditions during diagenesis (Robinson et al, 1996).

15 15 The Jurassic-Lower Cretaceous Kingak shale comprises the bulk of the Beaufortian sequence that was deposited during rift opening of the Arctic Ocean basin (Hubbard et al., 1990). Kingak shale on the southern passive rift flank is a mud-dominated succession of prograding shelf deposits characterized by multiple transgressive-regressive sequence sets (Houseknecht and Bird, 2004). Kingak shale contains a mixture of marine and terrigenous organic matter deposited in a marine siliciclastic setting (Peters et al., 2006). The lower part of Kingak is typically the most organic-rich interval with the average TOC of more than 5%. Uplift and erosion of the rift margin produced the regional Lower Cretaceous Unconformity (LCU). This unconformity progressively truncates all older units northward onto the Barrow Arch. It plays an important role in many of the largest oil fields in northern Alaska: development of enhanced porosity in sub-unconformity reservoirs, provision of a migration pathway for hydrocarbon, juxtaposition of over-lying marine mudstone source and seal rocks, such as Pebble shale unit and HRZ of the Hue Shale (Bird 2001). The Pebble shale was deposited during a south-to-north marine transgression in response to subsidence of the rift margin (Wang et al. 2014). It is characterized by a small but distinctive proportion of pebbles and well-rounded frosted sand grains scattered through the shale (Collins, 1961; Molenaar et al., 1987). Pebble shale differs in its organic characteristics: being oil-prone in some areas and gas-prone in others. Despite its relatively high TOC range ( wt. %), petroleum-generative potential of the Pebble shale unit varies because of differences in primary productivity, clastic dilution, and preservation (Keller and Macquaker, 2001; Keller et al., 2002). The Hue shale is the distal-deltaic condensed section of the Brookian sequence and was deposited in a deep water basin plain environment (Peters et al., 2006). Its thickness ranges from less than 50 feet thick in the west (western NPRA) to more than 600 feet thick in the east (ANWR), showing a reflection of the west-to-east pro-gradational filling of the Colville basin (Wang et al. 2014). The upper part of the Hue shale is thicker but has considerably less generative potential (lower TOC and HI) than the lower part because of more proximal deposition and greater clastic dilution. The lowermost part of the Hue shale is easily marked on well logs by a characteristic high Gamma Ray (GR) signature, widely known as gamma-ray zone (GRZ) or highly radioactive zone (HRZ). This organic rich interval has a range of TOC from 1.9 to 3.9 wt. % (Keller et al., 1999). In the ternary diagram commonly used for shale classification, shale can be divided into argillaceous shale (rich in clay minerals), calcareous shale (rich in calcite) and siliceous shale (rich

16 16 in biogenic and detrital quartz/feldspar). Based on limited XRD analysis and geological background, Hue is classified as siliceous mudstone while Shublik is classified as siliceous marlstone. Other shale classification schemes based on bulk mineralogy are also included for reference (Appendix Figure 50). SHUBLIK HUE/HRZ Figure 4: Ternary diagram shows shale classification of Hue/HRZ and Shublik (blue triangles) based on limited XRD analysis in Alcor-1 (Allix et al. 2010). Other notable shale plays are also presented in the diagram. Peters et al. use well logs of more than 60 wells in NSA and Rock-Evaluation pyrolysis analyses to map the present-day thickness of each source rock and the quantity (TOC), quality (HI), and thermal maturity (Ro, Tmax) of the organic matter (2006). Plots of S2 peak versus TOC are useful to compare the petroleum-generative potential of different source rocks (Langford and Blanc-Vallenron, 1990). Slopes of lines radiating from the origin are directly related to HI (100*S2/TOC, mg HC/g TOC). HI values of greater than 600, , , and less

17 17 than 50 mg HC/ g TOC distinguish organic matter type I (very oil prone), II (oil prone), II/III (oil and gas prone), III (gas prone), and IV (inert), respectively (Peters et al., 2006). Type I to type IV denotes decreasing source rock potential and value. Figure 5: Rock-Evaluation pyrolysis S2 peak (mg hydrocarbon/g TOC) versus TOC of 408 thermally immature and early-mature samples shows that the quantity and quality of organic matter in the Shublik Formation exceed the other three source rocks. Slopes of radiating lines equal Hydrogen Index (100*S2/TOC) that distinguish organic matter types (Peters et al., 2006). Based on Figure 5, the quantity (TOC and S2 peak) and quality (HI) of organic matter in the Shublik shale (oil-prone type I or II) commonly exceed those of the other three source rocks, which usually fall into oil and gas prone type II/III. Peters et al have performed mass balance calculations to determine the extent of fractional conversion of kerogen to petroleum (f) and the original TOC (TOCo) of source rocks prior to thermal maturation, which controls directly the ultimate yield of petroleum in the area. Table 1 provides a summary of their results. Values given are typical average, but not by any means comprehensive in the whole area of interest: Formation TOCo (wt %) HIo (mg HC/g TOC) Kerogen type Thickness (feet) Shublik 2% to >4% Type I/II Kingak 5% 400 Type II/III 1400 Pebble 2-4% Type IV Hue <2% to 4-5% Type II/III Table 1: Average source rock properties of major shale formations in NSA (Peters et al. 2006)

18 18 VI. Dataset Description Data is provided by Great Bear Petroleum LLC. There are two comprehensive log suites including GR tool (Spectral GR is also available), Density, Neutron, Resistivity (with different radius of investigation) and Sonic logs of two vertical wells, Alcor-1 and Merak-1, located along the Trans-Alaskan pipeline system in Great Bear leases (Figure 6). The sonic log includes transit time (or slowness) of both compressional P and shear S waves (both directions of polarization of shear wave SH and SV are also available). Two wells of interest are 1.5 miles apart and have shown excellent correlation in terms of petrophysical properties and source rock character. Wellto-well correlation based on GR and TOC logs is completed by Ken Bird (Appendix Figure 49). Formation tops of each lithofacies of interest are given in Great Bear completion reports based on mud logs and bit cutting lithology (Appendix Figure 44 and Figure 45). Core petrography are also available in normal and ultraviolet light. Vertical Seismic Profiling (VSP) and 3-D seismic are also available for future study. Available core analysis from Corelab includes: porosity, helium permeability, oil/gas saturation, X-ray Diffraction (XRD), computed tomography scans (CT scans). Figure 6: Focus area is located between the NPRA and ANWR. The area of interest shows locations of two vertical wells of interest: Alcor-1 and Merak-1. The blue dashed line indicates the area of available 3-D seismic data. Yellow blocks show Great Bear leases in NSA. Two wells Alcor-1 and Merak-1 are 1.5 miles apart and located along the Trans-Alaskan Pipeline (green dashed line).

19 19 In addition, Geomark and Weatherford labs conduct geochemical tests on selected core and cuttings subsets. Cutting measurements are not included in this study due to uncertainty in depth determination and possible mud contamination. Geochemical core data includes depth of core samples, Leco TOC, S1 peak (free oil/gas without thermal cracking), S2 peak (hydrocarbon during second programmed heating stage), S3 peak (CO2 during thermal cracking of kerogen), Tmax (temperature of maximum pyrolytic degradation), Ro (vitrinite reflectance or thermal maturity indicator), HI, Oxygen Index (OI) and Productivity Index (PI) (McCarthy et al., 2011). I also took my own set of core plugs (Table 2 and Table 3) to measure porosity and elastic properties (P and S wave velocities) using ultrasonic measurement devices in Stanford Rock Physics Lab. Depths are carefully chosen to be representative of each lithofacies (picked at homogenous and unfractured sections) and cover a wide range of porosity, TOC and lithology based on logging signature. Visible fractures and undesirable lithology (pyrite, bioturbation) are intentionally avoided to ensure consistency with theoretical explanations. At each depth, if bedding direction is clear, core plugs of three different directions (bedding-normal or vertical, beddingparallel or horizontal, 45-degree-to-bedding) are taken, assuming that their depths are sufficiently close to represent similar lithology and texture (Figure 7). In Alcor-1, it is more difficult to obtain whole cylinder plugs in all directions due to pre-existing fracture propagation so 45-degree-tobedding and horizontal plugs are sometimes not available. Table 2: Merak-1 core plug set for ultrasonic measurement. WF=Weatherford. In current literature, Shublik is subdivided into four smaller units (A, B, C and D) to emulate its complex, highly heterogeneous nature.

20 20 Table 3: Alcor-1 core plug set for ultrasonic measurement. Kingak is not available for coring in Alcor. Due to pre-existing fracture, it is difficult to obtain horizontal and 45-degree plugs. Figure 7: Diagram showing how three core plugs of different directions are taken out of core slab of Merak-1 well at depth Note that pyrite (brownish in the upper right corner) as well as fractures on the surface of the slab are intentionally avoided. Bedding is clear at this depth so 3 core plugs are taken. V=vertical/bedding-normal, H=horizontal/bedding-parallel, 45=45-degreeto-bedding.

21 21 VII. Methodology Quantitative seismic interpretation (QSI, Figure 8) demonstrates how rock physics can be applied to predict reservoir parameters, such as lithology, pore fluid and source rock character from seismically derived attributes (Avseth et al., 2005). Based on available logs, cores and geology, we can identify major seismic lithofacies by observing cluster separation in exploratory crossplots of different properties. Rock physics helps convert geologic and wireline logging information into elastic properties (P and S-wave velocities and bulk density). An additional dimension of the desired elastic dataset, geochemical parameters, is integrated into the workflow by establishing useful correlations between elastic and source rock properties. Outside of this study s scope, the training dataset can be expanded to cover what-if situations (varying physical conditions not encountered in key wells) by correlated Monte Carlo simulation and fluid substitution recipe. After performing proper scale calibration of inverted seismic data in the area of interest, we can use this dataset to classify lithology and source rock character to detect best producing intervals and areas. A full quantitative seismic interpretation is not part of this work. In this study, we focus on the parts of the workflow that related to the construction of a reliable elastic and geochemical training dataset of each pre-defined lithofacies. I extracted well log data (density, GR, resistivity and sonic wave velocities) for exploratory crossplots and quantitative assessment. Each lithofacies has a corresponding depth interval (formation tops and bases) in the completion report. Based on these depth markers, we can delineate and build a log-based training data of each facies. Preliminary quality checks are performed to remove anomalous log readings due to equipment errors. Calibration of logging data based on available core data is also performed (see example in Figure 20). Neutron log cannot be used in radioactive shale intervals as cross-validation shows erroneously higher values of neutron porosity compared to core values. A challenge of this study is the lack of petrophysical and geochemical data in a same core plug subset because two different labs conducting their experiments at different times. Therefore, I have to use existing correlations in the literature to expand the available dataset. The use of crossplot between relevant log-derived properties to separate lithofacies proves to be a fast, simple and field-applicable process. Intrinsic variability of rock properties within a single lithofacies presents the biggest challenge of QSI: when does an observed attribute change indicate a significant change across facies rather than a minor fluctuation within a facies? (Avseth et al., 2005).

22 Figure 8: Diagram showing quantitative seismic interpretation workflow with integration of geochemical data. In this study, we focus on the parts of the workflow that related to the construction of a reliable elastic and geochemical training dataset of each pre-defined lithofacies. 22

23 23 VIII. Seismic Lithofacies Delineation: 1. Logging Analysis: Popular logging tracks are plotted to verify several key signatures of each lithofacies (Figure 9). Density of Hue/HRZ is relatively constant throughout the interval. However, HRZ has significantly higher GR and lower sonic velocities than its overlying Hue Shale because of smaller clastic dilution (more clay content) and less proximal deposition. A spike at 8700 feet in the density log of Merak well is due to equipment switch after setting 9-5/8 casing. Hue and HRZ will therefore be separated into two separate lithofacies (Figure 10). Pebble shale has a wide range of density due to its varying inclusion of pebble and well-rounded sand grain in its fine-grained matrix. In terms of radioactivity level and acoustic properties, Kingak shale is a relatively homogeneous interval. Nevertheless, Kingak s density varies considerably due to its depositional history: a mud-dominated succession of prograding shelf deposits characterized by multiple transgressive-regressive sequences (Wang et al., 2014). Shublik formation has abrupt high-gr bands interbedded in between lower-gr intervals. Spikes in both GR and density track indicate different amounts of clay and carbonate presence, respectively, throughout Shublik interval. It also has much higher velocities of both P and S waves compared to other facies since its matrix has greater amount of carbonate. Alcor-1 well logs show similar features and is included in the Appendix for reference (Figure 46).

24 24 Figure 9: Diagram showing typical logging tracks used for qualitative delineation of NSA lithofacies. From left to right for Alcor-1 well: GR (API unit), Compensated Bulk Density (gm/cc), P and S wave velocity (m/s) and Vp/Vs. Figure 10: Diagram showing picks for top and bottom depth of each shale of interest. Matlab is used to color-code each facies and index their numerical values. From top to bottom: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). This color code is used throughout this study.

25 25 Crossplots of P and S-wave velocities versus bulk density show some separation between different shale units (Figure 11). Shublik cluster shows slightly higher density and significantly higher velocities than the other lithofacies. Hue cluster does have minor separation thanks to higher P and S wave velocities and a more limited range of density. HRZ cluster forms the low-velocity end of Hue cluster and possesses a wider range of bulk density. Pebble and Kingak shale are difficult to distinguish from each other due to a significant overlap of these two clusters. While Hue and HRZ display considerable fluctuation of acoustic velocity magnitude within a small range of density, Pebble and Kingak s shear velocities show relative independence of bulk density, especially in Merak-1 well. Despite small distance between Merak and Alcor wells, well-to-well lateral variability is fully demonstrated as Pebble and Kingak s data cluster in Alcor-1 lack lowdensity members. Organic matter has much lighter density (around 1.2 gm/cc) compared to other major lithology in the matrix (clay, quartz and calcite). Due to low porosity observed in core plugs of all lithofacies, bulk density differences are primarily controlled by organic matter or TOC content. Therefore, the span of density range may imply the variation of organic content within each facies and across lateral distance between two wells. Another factor that may affect bulk density reading is trace amounts of pyrite (heavy mineral with density of 5 gm/cc) in the matrix. Figure 11: P and S wave velocities (m/s) versus bulk density (gm/cc) of different shale lithofacies in two wells: Merak at the top and Alcor at the bottom. Graphs are of similar scale for comparison.

26 26 Crossplots of velocity and GR demonstrate a different trend (Figure 12). Despite repeated overlap of Kingak and Pebble units, velocity-gr crossplots do show a better separation of other clusters. Due to high carbonate content (resulting in higher velocities), Shublik cluster stands out in high-velocity region. Varying concentration of clay mineral illite, which is related to potassium (one of three components contributing to GR reading), explains why Shublik covers such a wide range of GR values. Non-constant clay mineral composition indicates that there were major changes with respect to detrital input during the deposition history of Shublik shale. Figure 12: P and S wave velocities (m/sec) versus GR (API unit) of different NSA shale lithofacies in two wells: Merak-1 at the top and Alcor-1 at the bottom. Graphs are of similar scale for comparison. The traditional GR measures total radioactivity as the sum of three radioactive elements: thorium, potassium and uranium. Uranium (in ppm unit) has been usually found to have stronger correlation with TOC compared to total GR (Mann et al., 1985). Therefore, another approach is to use spectral GR, which provides relative contributions of each component to total GR. An anoxic depositional environment (the lower part of Shublik for example) could provide a more ideal setting for the fixation and preservation of uranium on organic matter. Post-depositional processes are also responsible for uranium content. In carbonate-rich sediment (like Shublik), a partial

27 27 exchange of carbonate and organic carbon, which is the uptake of carbon from the oxidation of organic matter during early diagenetic cementation may have taken place (Mann et al., 1985). Merak-1 s spectral GR log does not fully cover all the sections of interest. In a limited interval of Hue shale that has spectral GR, uranium content associated with organic matter is the principal cause of higher GR intensity compared to overlying inorganic layers. GR is usually an indicator of clay content since clay minerals emit larger amount of gamma radiation than other rock-forming minerals such as quartz and carbonate. Considering all lithofacies in Merak-1, GR does not have a strong influence on velocity-density relationship because data points of different clay content are not clearly separated (bottom right Figure 13 and Figure 14). Within a single lithofacies, only in Kingak shale is GR a significant driving force in velocity-density trend. In Kingak, velocity-bulk density trend is clear: as density increases, velocity also increases. In addition, clusters of high GR data points separate clearly from clusters of lower GR data points. As GR increases, P-wave velocity and density decrease accordingly. Figure 13: Crossplot of P-wave Velocity (m/sec) versus Density (gm/cc) in Merak-1, color-coded by GR showing reasonable trends in Kingak, HRZ and Pebble shale. Hot color indicates higher GR while cold color indicates lower GR. GR is a good indicator for Kingak shale trend since high GR and low GR points stack nicely along the velocity-density trend.

28 28 Figure 14: Crossplot of S-wave Velocity versus Density in Merak-1, color-coded by GR showing reasonable trend in Kingak and HRZ. GR is a good indicator for Kingak shale since Vs-density trend show separate clusters for high GR and low GR points. Another useful crossplot is Vp versus Vs (Figure 15). Shublik and Hue are readily separated from other clusters. Pebble, Kingak and HRZ clusters are well overlapped. Dashed blue lines represent lines of constant Vp/Vs ratio, which have been suggested by Vernik and Milovac to be a good indicator of organic-rich shale (2011). Several published datasets compiled by Vernik and in-house core and log data from Bossier, Woodford and Bakken shale plays fall within a relatively narrow Vp/Vs range regardless of wide range of saturation, porosity, or effective stress. These parameters seem to be secondary in controlling the reduced velocity ratio typical of organic shales as compared to their inorganic counterpart (Vernik and Milovac, 2011). In NSA, the spread of velocity ratio spans between values of 1.6 and 2.4, significantly wider compared to other shale plays (Figure 16). The organic-richer Shublik has the narrowest spread and lower average value of Vp/Vs ratio compared to other lithofacies, which supports the inverse correlation suggested by Yan et al between TOC content and Vp/Vs ratio.

29 29 Figure 15: Vs versus Vp from dipole sonic log of two wells Merak and Alcor. Blue dashed lines represent constant Vp/Vs ratio. Plots are of similar scale for comparison. Figure 16: Relationship between compressional and shear velocity for bedding-normal (0 0 ) for Bakken, Woodford and Bossier shale from dipole sonic logs. Dashed lines also indicate constant Vp/Vs ratio (Vernik and Milovach, 2011). Reduced velocity ratio is observed in organic-rich shale compared to its inorganic counterpart.

30 30 Computation of TOC from available logs, in this case resistivity log, is necessary to supplement the limited geochemical data. In addition to low resolution, resistivity measurements in logging devices are strongly maturity dependent. Oil generation results in an increase in resistivity while expelled gas (products of oil cracking at higher maturity) decreases resistivity (Mann et al., 1985). Low resistivity therefore can indicate both immature and over-mature oil source rocks as well as gas-only source rock. Hence, resistivity alone is not sufficient for TOC calculation. A widely popular method to calculate TOC from logs in the industry is Passey method (or Delta Log R technique). The method involves overlaying of a properly scaled porosity log (or transit time log) on a resistivity curve (ideally from a deep reading tool). The separation between two tracks results from two effects: the transit time curve responds to the presence of low-density, low-velocity kerogen and the resistivity curve responds to the formation fluid in pore spaces (Passey et al., 1990). Generation and expulsion of hydrocarbon from source rock contribute to the increasing resistivity in organic-rich intervals because of the replacement of electrically conductive pore water with non-conductive hydrocarbon. In this study, superposition of a deep resistivity and a sonic transit time logs on a pre-defined scale (50 µsec/feet to one resistivity cycle in log scale) shows good separation in source rock intervals (Hue, Shublik, Kingak) and decent overlap in inorganic intervals. I pick the Miluveach sandstone (a non-source inorganic rock) to be the baseline interval as the two curves run parallel and well overlap in this interval. Miluveach s values of baseline resistivity (R_baseline) and baseline transit time ( t_baseline), as well as resistivity and transit time of layers of interest are inputs to calculate TOC: R logr = log ( ) ( t t_baseline) R baseline TOC = logr LOM LOM is the level of maturity and is determined separately for each source rock. For type II and III source rock, I use the crossplot of S2 peak versus TOC of core plugs to find out the LOM value of Hue/HRZ in Merak-1 to be 8.5, Hue/HRZ in Alcor-1 to be 9.5, Kingak and Shublik in both wells to be 12 (Figure 17).

31 31 Figure 17: S 2 peak (mg HC/g rock) versus TOC (wt %) of core plugs in Geomark dataset. Black lines indicate different Level of Maturity LOM as defined by Passey et al Spikes in the TOC logs might be attributed to anomaly in the deep resistivity log. Crossvalidation with geochemical core data in Figure 18 shows a reasonable agreement in organic-rich intervals in Merak-1 well (especially Shublik and Hue). Only a small portion of Kingak is matched since we do not have enough core measurements of this thick interval. In Alcor-1, Shublik is also sampled sparsely so this method could not guarantee the match for the whole interval. Figure 18: Cross-validation of TOC logs created by Passey method (blue lines) and geochemical core data (pink dots) for different lithofacies in two wells. From left to right: Merak Hue, Merak Kingak, Merak Shublik, Alcor Hue/HRZ, Alcor Shublik.

32 32 2. Core data analysis This study lacks a complete set of core plugs with both geochemical, acoustic and petrophysical data. Due to time constraint to carry out all experiments with all available core plugs, I decide to correlate data of different scales (well log versus core plug). Preliminary quality check shows that bulk density of log and core at similar depth are of reasonable agreement (Figure 20). In Figure 19, acoustic velocities are log-derived values at the identical depths core plugs are taken while bulk density is core plug value. Only Kingak shows slight Vp/Vs ratio increase as shale gets more compacted (bulk density increases). Due to great Kingak thickness, depth burial and shale compaction has a much more influential role in controlling this trend compared to thinner and more heterogeneous Shublik and HRZ. Figure 19: Vp/Vs ratio (log-derived) versus Dry and Wet (As-received or AR) bulk density (gm/cc) of core plugs in Alcor-1 well. Only Kingak shows slight velocity ratio increase as shale gets more compacted (bulk density increases). P-wave and S-wave velocities (extracted from sonic logs at corresponding depths) are plotted against different bulk density (log, dry core plug and as-received core plug) (Figure 21). Log values of bulk density of Shublik and HRZ show very good consistency with core measurements so no correction is necessary (Figure 20). However, other factors may obscure the value of bulk density log such as varying heavy pyrite concentration and natural fracture system. Kingak log values of density are lower than core values possibly due to sampling bias of core plugs towards pyrite-free and unfractured intervals. Presence of heavy minerals, like pyrite (less than 10% in XRD analysis), could be ignored for the sake of simplicity.

33 33 Figure 20: Cross-validation of density values between core and log measurements. The diagonal 45-degree slope line indicates consistency of HRZ and Shublik samples while Kingak samples need further calibration. Shublik, Hue and HRZ show good consistency as most of the data points fall onto the diagonal 45 degree line while Kingak shows greater value of core density compared to logging results. Figure 21: P and S-wave velocities (m/sec) versus bulk density (gm/cc). Log value is denoted as circle, as-received core as diamond and dry core as star. Saturation of as-received core does not change bulk density much because NSA shale has low porosity. Kingak log values of density are lower than core values possibly due to sampling bias of core plugs towards pyrite-free and unfractured intervals. The feasibility of conducting petroleum source rock evaluation from well-log data is examined by establishing useful correlations between log-derived or seismic-related attributes and

34 34 source rock parameters. A full assessment of source rock potential means a complete characterization in terms of richness, kerogen type and maturity. Log-derived density values are presented versus the thermal maturity indicator Tmax in Figure 22, which shows that bulk density is not maturity dependent. Figure 22: Density (gm/cc) and P-wave velocity (m/sec) versus Tmax (degree C). Density shows its little dependence on maturity due to its weak correlation within each lithofacies. Figure 23: P and S-wave velocity (feet/sec) versus HI. Each data cluster is well separated. The correlation is weaker compared to velocity-density correlation.

35 35 Figure 24: P and S wave velocity (m/sec) versus TOC (weight percentage). No correlation is recognized even though the clusters are relatively well separated. Crossplots of Vp, Vs and TOC, HI show good separation between different lithofacies (Figure 23 and Figure 24). A simple correlation between geochemical and petrophysical parameters is not easy to deduce since log response in shale intervals is complex and affected by not only the organics but also mineralogical and pore fluid properties of the rock (Mann and Muller, 1988). Looking closer at a single lithofacies, the correlation is stronger but it is not as profound as velocity-density relationship. Acoustic analysis in other notable shale plays (Bakken, Bazhenov and Niobrabra) is compiled by Vernik (Figure 25, Vernik and Nur, 1994; Vernik and Landis, 1996; Vernik and Liu, 1997), showing that Vp increases as HI decreases, except in high porosity shale where Vp is better correlated with porosity (or density). Figure 25: P-wave velocity (Vp in feet/sec) versus HI of other shale plays. Vp is inversely proportional to HI. Within a single formation, the correlation between Vp and HI is reasonable and the scatter is greatly reduced (Prasad et al., 2002a).

36 36 A statistically well-defined evaluation requires a comprehensive geochemical analysis of extensive core sets, which is time consuming and expensive. Bit cuttings do not always reflect the correct lithology due to caving and contamination by organic mud additives (Mann et al., 1985). Therefore, wireline log data, which offers continuous profile of stratigraphic sections of interest with relatively high resolution, proves to be the best alternative. This is where the TOC logs I established earlier come in handy. In Shublik, TOC and acoustic velocities show a strong directly proportional correlation. Hue and HRZ clusters are significantly overlapping, as do Pebble and Kingak (Figure 26 and Figure 27). Figure 26: P and S wave velocity versus log-derived TOC values for Merak-1. TOC and acoustic velocities show a strong directly proportional correlation in Shublik. Figure 27: P and S wave velocity versus log-derived TOC values for Alcor-1. TOC and acoustic velocities show a strong directly proportional correlation in Shublik.

37 37 3. Rock physics template: Rock physics model allows to link seismic properties to geologic properties. Expanding on the earlier rock physics diagnostics, I create rock physics templates (RPTs) of two selective seismic parameters: Acoustic Impedance (AI, which is the product of bulk density and P-wave velocity) and Vp/Vs ratio, for each lithofacies in NSA. Geologic trends (pressure variation, pore fluid, sorting, and cementation) also play a role in constraining rock physics models. If we can predict the expected change in seismic response (or seismic-derived attributes such as AI or Vp/Vs) as a function of depositional environment or burial depth, we will increase our ability to predict hydrocarbons in ORS (Avseth et al., 2005). This RPT approach enables me to perform rock physics analysis not only on well-log data but also on elastic inversion results of seismic data. RPT facilitates prediction of porosity/density as well as discrimination of different pore fluid and pressure scenarios in the area of interest. XRD mineralogy is available in HRZ and Shublik in Alcor-1 (Table 4). To simplify the matrix composition, I only consider minerals that are of significant amount and critical inputs in existing rock physics models in the literature (quartz, clay and carbonate). Note that pyrite is also prevalent in HRZ core plugs (around 10% volume percentage) but will be ignored for the sake of simplicity. Illite is the main clay component in both shale units. Kingak composition is assumed based on existing literature. Table 4: XRD analysis of Alcor-1 core plugs, covering HRZ and Shublik intervals. Illite is the main clay component in both shale units. Minerals that are of significant amount are quartz, carbonate and illite. Table 5 presents the simplified lithology of HRZ and Shublik to use in the rock physics soft sediment template. The soft sediment model uses Hertz-Mindlin contact theory (Mindlin,

38 ) to calculate high-porosity end members at critical porosity and the modified lower Hashin- Shtrikman (Hashin and Shtrikman, 1963) to interpolate back to low-porosity end members. The zero-porosity end member is a pure mineral mix of quartz, clay and calcite, assuming that other minerals only appear as trace amounts in the matrix composition. The Matlab code, written by Gary Mavko, needs several inputs (effective pressure, volume composition) to calculate shale elastic properties (acoustic velocities at different saturations, bulk density). Pressure data is not available in type wells so I assume standard lithostatic and pore pressure gradient (1 and psi/feet respectively) for calculation of effective pressure. Therefore, the effective pressure gradient is psi/feet. Other inputs of the soft sediment model are mineral and fluid bulk/shear modulus (Table 6) and critical porosity (0.7 for shale). Clay (Illite) Calcite Quartz Kerogen HRZ Kingak Shublik Table 5: Simplified composition for HRZ, Kingak and Shublik to use as inputs of soft sediment model. Clay mineral Bulk Modulus K (GPa) Shear Modulus µ (GPa) Density Rho (kg/m 3 ) Quartz Illite Calcite Kerogen Table 6: Elastic moduli of different minerals (Table 2.1, Avseth et al., 2005). NSA kerogen elastic properties are extremely limited so typical values of kerogen modulus and density at similar maturity level from other shale plays are taken (Vernik 1994). This model calculates shale elastic properties and yields a Vp/Vs versus P-wave impedance trend superimposed onto my log-derived data points. The soft sediment model examines expected changes of these seismic attributes with regard to change in pore fluid, pressure, clay content and mineralogy (blue arrows in Figure 29). This step also serves as a checkpoint to ensure log quality consistency. The crossplot of AI versus Vp/Vs of my dataset (Figure 28), reveals the trend of RPT-

39 39 related property change due to shaliness/clay content in Hue/HRZ (marked by blue arrow 2 in Figure 29). The trend s sub-branches (blue arrow 3 in Figure 29) represent expected change during pore fluid substitution as gas displaces water in pore spaces (Sw varies from 0 to 1). Fluid substitution recipe has to be used with caution because shale lithology (clay minerals) defy the assumptions of Gassmann s formula. The effects of organic content and hydrocarbon-filled pore space will deviate the clusters of each lithofacies away from the main trend lines. The soft sediment model does a decent job to match bulk density of low-porosity (or high-density) members. Despite the inclusion of low-density kerogen in the model, low-density members (blue points) are not wellpositioned as they fall into a higher density zone. This is likely because the soft sediment model does not account for effective pressure anomaly along the interval. Also, the Hertz-Mindlin elastic contact theory, which is based on the behavior of elastic sphere pack subject to a confining pressure, is more applicable to sand than to shale. Another explanation is that logging device directly measures a layer of low-density organic material at those depths corresponding to dark blue data points in these RPTs below. Figure 28: Crossplot of AI versus Vp/Vs of Hue/HRZ, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR/clay content increases, both velocity ratio and AI tend to decrease. The colorbar indicates GR magnitude. Cluster of points in the red circle (upper left corner) are at the same interval that logging equipment switch happens and may need to be removed to align with the trend.

40 40 (2) (1) (3) Figure 29: A rock physics template (RPT) of Hue/HRZ presented as cross-plots of Vp/Vs versus AI includes a rock physics model locally constrained by depth (i.e., pressure), mineralogy, critical porosity and fluid properties. The template includes porosity trends for different fluid saturation (from fully water-saturated Sw=1 to fully gas-saturated Sw=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (2) increasing shaliness, (3) increasing gas saturation. To match bulk density of high porosity (or low bulk density) members, the model needs further modifications of its inputs (shear reduction factor, coordination number in Hertz-Mindlin model, kerogen composition and properties). Pebble crossplot of AI versus Vp/Vs does not show much density dependence but is included for reference (Figure 30). Figure 31 shows Kingak RPT, in which density proves to be the principal driving force of Vp/Vs-AI trend as clusters of various density magnitude clearly separate from each other. Figure 33 shows that the soft sediment model works well in Shublik to predict bulk density as the range of bulk density matches accurately density values of data points. In Shublik RPT, high density members are falling in lower density range because I do not include high-density pyrite in the model. The model is limited to two fluids interchangeable substitution (in this case water and gas). The predicted saturation of the soft sediment model shows slight over-estimation of gas saturation compared to wet core plugs (at corresponding depths of log data points). This is most likely due to an inadequate fluid preservation process of core plugs or the omission of oil in the fluid substitution recipe in the soft-sand model.

41 41 Figure 30: Crossplot of Vp/Vs versus AI of Pebble shale unit. Density is not a driving force behind this trend. (2) (1) (3) Figure 31: A rock physics template (RPT) of Kingak presented as cross-plots of Vp/Vs versus AI. The template includes porosity trends for different fluid saturation (from fully water-saturated Sw=1 to fully gas-saturated Sw=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (2) increasing shaliness, (3) increasing gas saturation. The trend of increasing shaliness is shown in Figure 52.

42 42 (1) (3) Figure 32: A rock physics template (RPT) of Shublik presented as cross-plots of Vp/Vs versus AI. The template includes porosity trends for different fluid saturation (from fully water-saturated S w=1 to fully gas-saturated S w=0) assuming uniform saturation. Color bar indicates the range of bulk density. Input parameters are highlighted in the right. Blue arrows indicate various conceptual geologic trend: (1) decreasing porosity (or increasing bulk density), (3) increasing gas saturation. The trend of increasing shaliness is not clear as shown in Figure 53. There are several challenges in modeling ORS composition and porosity effects on velocities. Porosity is not easily determined from either core plugs or log data due to complication in lithology and ambiguity in measurement accuracy, such as neutron tools in the log suite or ultralow permeability plugs. Therefore, bulk density is used instead of porosity in the RPTs. Additionally, fluid effects on acoustic properties are more problematic because shale lithology defies the main assumptions of Gassmann theory (widely used for clean sandstone rocks) due to rock (clay minerals) and fluid interaction. Grouping all lithofacies into one RPT, Figure 33 and Figure 34 shows density demonstrates a more profound importance than GR in influencing Vp/Vs versus AI trend.

43 43 Figure 33: A rock physics template (RPT) of NSA presented as cross-plots of Vp/Vs versus AI. Colorbar indicates different magnitudes of bulk density. Shale porosity of soft sediment model (using average values of composition of all NSA ORS lithofacies) is drawn for reference. Figure 34: A rock physics template (RPT) of NSA presented as cross-plots of Vp/Vs versus AI. Colorbar indicates different magnitudes of GR. Shale porosity of soft sediment model (using average values of composition of all NSA ORS lithofacies) is drawn for reference.

44 44 IX. Application of existing petrophysical models Several field-specific shale petrophysical models have already been successfully tested in other shale plays across the States. A physically consistent solution based on partitioning the system into kerogen and non-kerogen domains (suggested by nano-scale images) with their associated porosities is proposed by Alfred and Vernik in The model assumes that premature organic source rock is originally and fully water saturated. Kerogen, which consists mostly of carbon and hydrogen, is the portion of the naturally occurring organic matter that is insoluble to organic solvents. Due to thermal maturity and alteration, kerogen gets cooked leading to the densification of kerogen and creation of maturation-induced pore space filled with hydrocarbons (Alfred et al. 2012). This model assumes that hydrocarbon phase occupies the kerogen-related porosity while water occupies the non-kerogen matrix porosity. The combining investigated volume domain is shown in Figure 35: Figure 35: The combined domain of pore system. Organic domain contains solid organic matter (kerogen), organic porosity (filled with hydrocarbon Sw k). Non-kerogen domain contains solid inorganic matter (host rock or matrix) and inorganic porosity (filled with water Sw nk). k-kerogen, nk-non-kerogen matrix, fk-fluid in organic domain, nfk-fluid in non-organic domain, b-bulk property. V k is the volume of the organic domain (kerogen matrix and its porosity) and V nk is the volume of the inorganic domain (host rock matrix and its porosity) (Alfred and Vernik 2012). I use Vernik s model to calculate kerogen-modified porosity of core plugs to account for kerogen porosity. Solid part of the domain includes kerogen and host rock. Kerogen volume fraction in the solid, called K in Figure Figure 36, is a key input of this model. It is calculated by

45 45 using log-derived TOC (weight percentage), organic carbon percentage Ck (use the value of 84 as suggested by Vernik), kerogen density ρk (use correlation in Figure 37), non-kerogen matrix density ρnk (based on XRD analysis) in the formula below: K = TOC ϱ m C k ϱ k = TOC ϱ nk TOC (ϱ nk ϱ k ) + C k ϱ k (Alfred and Vernik 2012). Figure 36: The combined domain system with allocations of volumes. K is volume fraction of kerogen in the solid part of the domain (Alfred and Vernik 2012). Kerogen density is calculated by using their proposed correlation with the Vitrinite Reflectance (or thermal maturity indicator) Ro. The more mature (more carbon concentration) the system is, the more kerogen gets converted to hydrocarbons and hence the kerogen becomes denser (Alfred et al. 2012). Then the volume percentage of kerogen porosity in the total rock domain (or the volume difference between kerogen-modified porosity and matrix porosity) ᴠk is calculated from K and porosity. ᴠ k = K (1 Φ)

46 46 Figure 37: Correlation between kerogen density and thermal maturity through studying core data of various shale plays (Alfred and Vernik 2012). Since XRD analysis is only available for Alcor-1 well, Table 7 shows the modified porosity compared to original plug porosity for only Alcor samples. The additional kerogen-related porosity is 6% for HRZ and 3.5% for Shublik, on the average. Depth (feet) TOC (Wt %) Matrix porosity Kerogen-modified porosity Formation HRZ Shublik Table 7: Original plug porosity and kerogen-modified porosity based on Alfred and Vernik's model to account for kerogen porosity. Prasad et al. have proposed a correlation between P-wave velocity and modified porosity using this model for other notable shale plays (Bakken, Bazhenov, Niobrabra, Woodford) (Figure 39). Regardless of formation, the correlation becomes stronger when they correct the porosity to include kerogen-related pore space. Using a similar power trend line, the correlation coefficient of Velocity-Modified-Porosity relationship (Figure 38) is much more improved (R 2 =0.8) compared

47 47 to the original trend between velocity and core plug porosity (R 2 =0.4). This suggests a possibility of applying existing petrophysical models in cross-field applications. Vp=4400*(PHI/100) Vs=2300*(PHI/100) Figure 38: Apply the modified porosity to account for pore spaces in kerogen, a much better correlation between velocity and porosity is observed (R 2 =0.8). Prasad s relationship obtained from other shale plays is superimposed for comparison in the P-wave chart. Velocities in feet/sec, PHI in percentage. My correlation formulas are given in the box. Figure 39: Correlation between P-wave velocity (feet/sec) and Kerogen-modified porosity in other shale plays. Velocity correlates very well with kerogen volumetric content if we assume that about 40% of the kerogen acts as pore space to soften the rock. The correlation coefficient between velocity and modified porosity is now significant (R 2 =0.7) and does not depend on formation (Prasad et al., 2009).

48 48 X. Preliminary shale anisotropy characterization Anisotropic behavior of shale is observed in the dipole sonic tool running in Merak-1 well where velocities of two directions of shear wave polarization are measured as Vsxx and Vsyy. Cross-dipole shear-wave acoustic tool provides a direct measurement of macroscopic formation anisotropy. The percent anisotropy is computed: Percentage = Vsxx Vsyy (Vsxx+Vsyy)/2 Figure 40: Cross-dipole acoustic tool measure velocities of two different directions of shear wave polarization. Percentage of difference is plotted in the right with values range from 5-10 percent difference. A subset collection of core plugs in Shublik is chosen to proceed with petrophysical and elastic measurements. Due to the time-consuming nature of both tests for shale, only bench top equipment (Figure 41) is used for quick anisotropy measurement under low stress conditions (usually atmospheric pressure). The difference between laboratory measured velocities and sonic logs could be due to several reasons. Sonic log measures in situ conditions (fluid-saturated) while ultrasonic velocities are measured in dry condition (due to the inability to preserve original fluids during coring). Core plugs are dried up in a vacuumed container before ultrasonic velocity measurements. Other sources of discrepancies include sampling bias towards homogenous lithology (unfractured intervals) and size bias (smaller size of core plugs compared to logging coverage). Following a consistent way of picking P and S-wave arrival time in the oscilloscope

49 Vs (m/s) Vp (m/s) 49 signal, I plot the ultrasonic velocities of samples of different orientations to observe elastic anisotropy under atmospheric pressure (Figure 42). Figure 41: Experiment set-up. The right picture shows the oscilloscope. The left picture shows the transducer and the core holder. Molasses is used to improve the acoustic coupling between transducers and core sample Shublik A ft Shublik D Shublik D Alcor ft Angle to bedding Shublik A ft Shublik D Shublik D Alcor ft Angle to bedding Figure 42: P and S-wave velocities versus Angle to Bedding of Shublik core plugs at different depth and orientations. 0 degree means parallel to the bedding. 90 degree means normal to the bedding.

50 50 A consistent observation is velocity decreases as the angle to bedding increase from 0 degree (parallel to bedding) to 90 degree (normal to bedding). This anisotropic response is probably related to the fine, bedding-parallel lamination of organic matter and preferred orientation of clay particles (Vernik and Nur, 1992). This intrinsic anisotropy may be further enhanced in thermally mature shale by bedding-parallel microcracks induced by the processes of hydrocarbon generation. A geochemical test on these samples needs to be done to observe any correlation between thermal maturity and anisotropy. A more complete picture of microcrack effects will be better revealed if the effect of confining pressure on P and S wave velocities is experimentally available.

51 51 XI. Conclusion and Future Work Major shale lithofacies in North Alaska System can be qualitatively delineated in terms of elastic and petrophysical properties using simple crossplots except Kingak and Pebble. GR proves to be a better candidate than bulk density to qualitatively separate seismic lithofacies. Cross-plots between elastic properties and TOC or HI show good separation among different shale but little useful correlation is obtained. Weak inverse correlation between Vp/Vs and TOC is observed in NSA lithofacies. Organic material is not the sole driving force controlling velocity-density trend as mineralogy and fluid properties also play a part. Clay content plays a key role in the velocitydensity trend of Kingak assuming that it is directly related to GR. Existing shale petrophysical model can be applied if it is properly calibrated to specific regional geology of NSA. The soft sediment model is applied to produce NSA rock physics templates and obtains decent match in bulk density, especially for high density members. These templates show how various geological trends (pressure, saturation, clay content, mineralogy) affect seismic-related attributes (acoustic impedance and velocity ratio Vp/Vs). A training dataset of elastic properties (P and S wave velocities, bulk density) has been built to advance in the statistical rock physics workflow. There is a need to account for different physical scenarios across the field that might not be present at the well locations. A possible solution is to use correlated Monte Carlo to expand the training dataset to account for natural variability within dataset, or in other words, include cases beyond the wellbores. Future work also involves completion of necessary experiments to fill up the core dataset, which will calibrate the quality of the training dataset and be used to deduce more reliable correlations. North Slope Alaska shale anisotropy is apparent both in sonic log and core measurement. Source of anisotropy will be clearer after conducting velocity versus confining pressure test on core plugs. Limited bench top tests on core plugs have shown that velocity decreases as angle to bedding increases from 0 degree (parallel to bedding) to 90 degree (normal to bedding). The prospective of identifying potential source rocks and developing completion scenarios using wireline logs or seismic data depend on the ability to remove the intrinsic anisotropy from induced anisotropy (Vernik 1993)..

52 52 XII. References Alfred, D., Vernik, L., 2012, Cartagena, Colombia, A new petrophysical model for organic shales: SPWLA 53 rd Annual Logging Symposium. Allix, P., A. Burnham, M. Herron, and R. Kleinberg, 2010, Gas Shale, Oil Shale, and Oil-Bearing Shale: Similarities and Differences: AAPG Search and Discovery Aranibar, A., Saneifar, M., Heidari, Z., 2013, Denver, Colorado, Petrophysical rock typing in organic-rich source rocks using well logs: SPE , presented at the Unconventional Resources Technology Conference. Avseth, P., Mukerji, T., Mavko, G., 2005, Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk: Cambridge University Press. Bird, K.J., 1985, The framework geology of the North Slope of Alaska as related to oil-source rock correlation, in L.B. Magoon and G.E. Claypool, eds., Alaska North Slope Oil/Rock Correlation Study. AAPG Studies in Geology #20, p Bird, K.J., 2001, Alaska: A twenty-first-century petroleum province, in M.W. Downey, J.C. Threet, and W.A. Morgan, eds., Petroleum Provinces of the Twenty-first Century: AAPG Memoir 74, p Collins, F.R., 1961, Core tests and test wells, Barrow area, Alaska: U.S. Geological Survey Professional Paper 305-K, Gamero-Diaz, H., Miller, C., and Lewis, R., 2012, A classification scheme for organic mudstones based on bulk mineralogy: AAPG Southwest Section meeting. Hashin, Z., and Shtrikman, S., 1963, A variational approach to the elastic behavior of multiphase materials. J. Mech. Phys. Solids, 11, Houseknecht, D.W., and K.J. Bird, 2004, Sequence stratigraphy of the Kingak Shale (Jurassic Lower Cretaceous), National Petroleum Reserve in Alaska: AAPG Bulletin, v. 88, p Hubbard, R.J., S.P. Edrich, and R.P. Rattey, 1990, Geological evolution and hydrocarbon habitat of the Arctic Alaska microplate, in J. Brooks, ed., Classic Petroleum Provinces: London, Geological Society Special Publication, v. 50, p Keller, M. A., 2002, Petroleum source potential of the Beaufortian succession of the NPRA and Colville Delta area, NSA, based on sonic and resistivity logs: AAPG Bulletin, v. 86, p

53 53 Keller, M.A., and Macquaker J.H.S., 2001, High resolution analysis of petroleum source potential and lithofacies of Lower Cretaceous mudstone core pebble shale unit and GRZ of Hue Shale, Mikkelsen Bay State #1 well, NSA, in D.W.Houseknecht, ed., NPRA Core Workshop: Petroleum plays and systems in the National Petroleum Reserve-Alaska: SPEM Core Workshop 21, p Keller, M.A., K.J. Bird, and K.R. Evans, 1999, Petroleum source rock evaluation based on sonic and resistivity logs, in The Oil and Gas Resource Potential of the 1002 Area, Arctic National Wildlife Refuge, Alaska, by ANWR Assessment team, U.S. Geological Survey Open-File Report 98-34, 62 p. Langford, F.F., and M.-M. Blanc-Valleron, 1990, Interpreting Rock-Eval pyrolysis data using graphs of pyrolyzable hydrocarbons vs. TOC: AAPG Bulletin, v. 74, p Liu, X., Vernik, L., Nur. A., 1995, Petrophysical properties of the Monterey formation and fracture detection from the sonic log: SEG Annual Meeting, 8-13 October, Houston, Texas. Mann, U., Leythaeuser, D., Muller, P.J., 1985, Relation between source rock properties and wireline log parameters: An example from Lower Jurassic Posidonia Shale, NW-Germany: Advances in Organic Geochemistry. v. 10, p Mindlin, R.D., 1949, Compliance of elastic bodies in contact. J. Appl. Mech., 16, Molenaar, C.M., Bird, K.J., and Kirk, A.R., 1987, Cretaceous and Tertiary stratigraphy of northeastern Alaska, in I. Tailleur, and P. Weimer, eds., Alskan North Slope Geology: Bakersfield, California,, Pacific Section, Society of Economic Palentologists and Mineralogists and Alaska Geological Society, p Parrish, J.T., 1987, Lithology, geochemistry, and depositional environment of the Triassic Shublik Formation, northern Alaska, in I. Tailleur and P. Weimer, eds., Alaskan North Slope Geology: Bakersfield and Anchorage, the Pacific Section, SEPM and the Alaska Geological Society, p Parrish, J.T., M.T. Whalen, and E.J. Hulm, 2001, Shublik Formation lithofacies, environments, and sequence stratigraphy, Arctic Alaska, U.S.A., in Houseknecht, D.W., ed., Petroleum Plays and Systems in the National Petroleum Reserve Alaska: SEPM (Society for Sedimentary Geology) Core Workshop No. 21, p Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J., and Stroud J.D., 1990, A practical model for organic richness from porosity and resistivity logs: AAPG Bulletin, v.74, p

54 54 Peters, K.E., L.B. Magoon, K.J. Bird, Z.C. Valin, and M.A. Keller, 2006, North Slope, Alaska, Source rock distribution, richness, thermal maturity, and petroleum charge: AAPG Bulletin, v. 90, p Prasad, M., Kenechukwu, C., McEvoy, E., Batzle, M., 2009, Denver, Colorado, Maturity and impedance analysis of organic-rich shales: SPE , presented at the 2009 SPE Rocky Mountain Petroleum Technology Conference. Robison, V.D., L.M. Liro, C.R. Robison, W.C. Dawson, and J.W. Russo, 1996, Integrated geochemistry, organic petrology, and sequence stratigraphy of the Triassic Shublik Formation, Tenneco Phoenix #1 well, North Slope, Alaska, U.S.A.: Organic Geochemistry, v. 24, p Tutuncu, A.N., 2010, Salt Lake City, Utah. Anisotropy, compaction and dispersion characteristics of reservoir and seal shales: presented at the 44 th US Rock Mechanics Symposium. Vernik, L., 1993, Microcrack-induced versus intrinsic elastic anisotropy in mature HC-source shales: Geophysics, Vol. 58, No. 11, P Vernik, L., and Nur, A., 1994, Ultrasonic velocity and anisotropy of hydrocarbon source rocks: Geophysics, 57: Vernik, L., and Landis, C., 1996, Elastic anisotropy of source rocks: Implications for hydrocarbon generation and primary migration: AAPG Bull., 80: Vernik, L. and Liu, X., 1997, Velocity anisotropy in shales: A petrophysical study: Geophysics, 62: Yan, F., Han, D., Yao, Q., 2012, Oil shale anisotropy measurement and sensitivity analysis: SEG Las Vegas Annual Meeting. Wang Y., Peters, K., Moldowan, J.M., Bird, K., and Magoon, L.B., 2014, Cracking, mixing, and geochemical correlation of crude oils, North Slope, Alaska: AAPG Bulletin.

55 55 XIII. Appendix Figure 43: Map of northern Alaska showing exploratory drilling density, pipeline infrastructure, and land ownership. North of the Brooks Range, federal ownership includes NPRA, ANWR and the offshore beyond the state-federal three-mile boundary. Ownership of nonfederal lands is divided between the state and Native American organizations. TAPS=Trans-Alaska Pipeline System (Ken Bird 2001)

56 Figure 44: Formation tops of all rock units in Merak-1. True Vertical Depth TVD is used in log analysis as it corresponds to the depth in my vertical type well. Rocks of interest are Hue, HRZ, Pebble, Kingak and Shublik. True Vertical Depth TVD is comparable to logging depth since both wells are vertical. 56

57 Figure 45: Formation tops of all rock units in Alcor-1. Rocks of interest are Hue, HRZ, Pebble, Kingak and Shublik. True Vertical Depth TVD is comparable to logging depth since both wells are vertical. 57

58 58 Figure 46: Diagram showing typical logging tracks used for qualitative delineation of NSA lithofacies. From left to right for Alcor-1 well: GR (API unit), Compensated Bulk Density (gm/cc), P and S wave velocity (m/s) and Vp/Vs. Diagram showing picks for top and bottom depth of each shale of interest. Matlab is used to color-code each facies and index their numerical values. From top to bottom: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). This color code is used throughout this study. Figure 47: Young s modulus and Bulk Modulus versus Poisson Ratio in Merak-1. Each lithofacies clusters show a distinctive trend between bulk modulus and Poisson ratio. Shublik separate itself from other clusters. In this figure, color code is: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik).

59 Figure 48: Young s modulus and Bulk Modulus versus Poisson Ratio in Alcor-1. In this figure, color code is: red (Hue), green (HRZ), blue (Pebble), black (Kingak), pink (Shublik). Several Hue shale data points have Poisson Ratio value of 0.5, which need to be removed. 59

60 Figure 49: Well-to-well cross correlation based on TOC and GR logs (Ken Bird 2012). Two wells of interest are 1.5 miles apart and have shown excellent correlation in terms of petrophysical properties and source rock character. 60

61 61 Figure 50: Organic mudstone classification (Gamero-Diaz et al. 2012). Figure 51: Crossplot of AI versus Vp/Vs of Kingak, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude.

62 62 Figure 52: Crossplot of AI versus Vp/Vs of Pebble, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude. Figure 53: Crossplot of AI versus Vp/Vs of Shublik, color-coded by GR show expected change of AI and velocity ratio with regard to GR. As GR increases, both velocity ratio and AI tend to decrease. The colobar indicates GR magnitude.