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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

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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)

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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.

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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.

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III. Contents

I. Abstract: ............................................................................................................................................ 3

II. Acknowledgement ............................................................................................................................ 4

III. Contents ............................................................................................................................................ 5

1. List of tables: ..................................................................................................................................... 6

2. List of figures: .................................................................................................................................... 6

IV. Introduction .................................................................................................................................... 10

V. Geological setting ............................................................................................................................ 13

VI. Dataset Description ......................................................................................................................... 18

VII. Methodology ................................................................................................................................... 21

VIII. Seismic Lithofacies Delineation: ..................................................................................................... 23

1. Logging Analysis: ............................................................................................................................. 23

2. Core data analysis ........................................................................................................................... 32

3. Rock physics template: ................................................................................................................... 37

IX. Application of existing petrophysical models ................................................................................. 44

X. Preliminary shale anisotropy characterization ............................................................................... 48

XI. Conclusion and Future Work .......................................................................................................... 51

XII. References ...................................................................................................................................... 52

XIII. Appendix ......................................................................................................................................... 55

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1. List of tables:

Table 1: Average source rock properties of major shale formations in NSA (Peters et al. 2006) .............. 17

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. ......................................................................................................................................................... 19

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. ...................................... 20

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.

.................................................................................................................................................................... 37

Table 5: Simplified composition for HRZ, Kingak and Shublik to use as inputs of soft sediment model. ... 38

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). ................................................................................ 38

Table 7: Original plug porosity and kerogen-modified porosity based on Alfred and Vernik's model to

account for kerogen porosity. ..................................................................................................................... 46

2. 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). .......................................... 11

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). ........................................ 13

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). ................................. 14

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. ...................................................................................................................................................... 16

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). ................................................ 17

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). ........................................ 18

Figure 7: Diagram showing how three core plugs of different directions are taken out of core slab of

Merak-1 well at depth 10795’3”. 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.

.................................................................................................................................................................... 20

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

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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. ........................................................................................................................... 24

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. ................... 24

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. ................ 25

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. ......... 26

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. ............................................................................................ 27

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. ...................................................................................... 28

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. ................................................................ 29

Figure 16: Relationship between compressional and shear velocity for bedding-normal (00) 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. ................................................................................................................................ 29

Figure 17: S2 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. 1990. .............................................. 31

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. ....................................................................................................... 31

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). ...................................................................................................................................... 32

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, 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. ......... 33

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. .......................................... 34

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. ................................................................................. 34

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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. ............................................................................. 35

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). ................................................................................................................... 35

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. ....................................................................... 36

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. ....................................................................... 36

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. .................................................................................................................................................... 39

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. ........................................................................................................................... 40

Figure 30: Crossplot of Vp/Vs versus AI of Pebble shale unit. Density is not a driving force behind this

trend............................................................................................................................................................ 41

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. ............................................................................ 41

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 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), (3) increasing gas saturation. The trend of increasing

shaliness is not clear as shown in Figure 53. .............................................................................................. 42

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. ................................................ 43

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. ................................................................ 43

Figure 35: The combined domain of pore system. Organic domain contains solid organic matter

(kerogen), organic porosity (filled with hydrocarbon Swk). Non-kerogen domain contains solid inorganic

matter (host rock or matrix) and inorganic porosity (filled with water Swnk). k-kerogen, nk-non-kerogen

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matrix, fk-fluid in organic domain, nfk-fluid in non-organic domain, b-bulk property. Vk is the volume of

the organic domain (kerogen matrix and its porosity) and Vnk is the volume of the inorganic domain (host

rock matrix and its porosity) (Alfred and Vernik 2012). ............................................................................. 44

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). .............................................................................. 45

Figure 37: Correlation between kerogen density and thermal maturity through studying core data of

various shale plays (Alfred and Vernik 2012). ............................................................................................. 46

Figure 38: Apply the modified porosity to account for pore spaces in kerogen, a much better correlation

between velocity and porosity is observed (R2=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. .................................................................................................. 47

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 (R2=0.7) and does not depend on formation (Prasad et al., 2009). ................. 47

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. ................................................................................................................................................... 48

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. ......................................................................................................................................... 49

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. ........... 49

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) .............. 55

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

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

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............................................................................................................................................................ 58

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).

.................................................................................................................................................................... 58

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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

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

Figure 50: Organic mudstone classification (Gamero-Diaz et al. 2012). ..................................................... 61

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. ................................................................................................................ 61

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. ................................................................................................................ 62

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. ................................................................................................................ 62

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

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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 real-

time, 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

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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).

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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).

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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 gas-

prone 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).

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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 (1.5-3.8 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

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).

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, 300-600, 200-300, 50-200 and less

HUE/HRZ

SHUBLIK

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. 2006 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% 250-400 Type I/II 150-300

Kingak 5% 400 Type II/III 1400

Pebble 2-4% 100-250 Type IV 50-200

Hue <2% to 4-5% 200-300 Type II/III 300-500

Table 1: Average source rock properties of major shale formations in NSA (Peters et al. 2006)

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. Well-

to-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

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, bedding-

parallel 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-to-

bedding 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

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 10795’3”. 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.

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.

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

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

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 low-

density 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

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

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

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. 2012 between TOC content and Vp/Vs ratio.

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 (00) 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

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:

∆logR = log (R

Rbaseline) + 0.02 ∗ (∆t − ∆t_baseline)

TOC = ∆logR ∗ 102.297−0.1688∗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

Figure 17: S2 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. 1990.

Spikes in the TOC logs might be attributed to anomaly in the deep resistivity log. Cross-

validation 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

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

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

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

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

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

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

1949) 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 0.433

psi/feet respectively) for calculation of effective pressure. Therefore, the effective pressure

gradient is 0.567 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 0.3 0 0.4 0.3

Kingak 0.3 0 0.5 0.2

Shublik 0.05 0.35 0.4 0.2

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/m3)

Quartz 36.6 45 2650

Illite 39.4 11.7 2750

Calcite 76.8 32 2710

Kerogen 6.8 3.6 1400

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

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 well-

positioned 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

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.

(3)

(2) (1)

41

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 52.

(3)

(2) (1)

42

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

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), (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.

(3)

(1)

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

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 2012. The model assumes that pre-

mature 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 Swk). Non-kerogen domain contains solid

inorganic matter (host rock or matrix) and inorganic porosity (filled with water Swnk). k-kerogen,

nk-non-kerogen matrix, fk-fluid in organic domain, nfk-fluid in non-organic domain, b-bulk

property. Vk is the volume of the organic domain (kerogen matrix and its porosity) and Vnk 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

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 ∗ ϱmCk ∗ ϱk

=TOC ∗ ϱnk

TOC ∗ (ϱnk − ϱk) + Ckϱk

(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.

ᴠk = K ∗ (1 − Φ)

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

8643 2.078 13.2 18.4

HRZ 8654 2.445 9.6 16.0

8664 2.323 11.4 17.3

10578 2.133 5.6 10.9

Shublik

10588 1.314 4.0 7.3

10598 1.246 3.2 6.3

10606 1.758 2.3 6.8

10616 0.257 1.3 2.0

10626 0.697 1.7 3.5

10634 2.485 5.7 11.8

10643 1.941 4.5 9.5

10654 0.783 4.3 6.3

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 (R2=0.8) compared

47

to the original trend between velocity and core plug porosity (R2=0.4). This suggests a possibility

of applying existing petrophysical models in cross-field applications.

Figure 38: Apply the modified porosity to account for pore spaces in kerogen, a much better

correlation between velocity and porosity is observed (R2=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 (R2=0.7) and does not depend on formation

(Prasad et al., 2009).

Vp=4400*(PHI/100)-0.428 Vs=2300*(PHI/100)-0.428

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

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.

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.

4800

5000

5200

5400

5600

5800

6000

0 20 40 60 80

Vp

(m

/s)

Angle to bedding

Shublik A 10739.75ft

Shublik D 10824.5

Shublik D Alcor 10637.2ft

2600

2700

2800

2900

3000

3100

3200

0 20 40 60 80

Vs

(m/s

)

Angle to bedding

Shublik A 10739.75ft

Shublik D 10824.5

Shublik D Alcor 10637.2ft

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

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 velocity-

density 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

XII. References

Alfred, D., Vernik, L., 2012, Cartagena, Colombia, A new petrophysical model for organic shales:

SPWLA 53rd 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 90122.

Aranibar, A., Saneifar, M., Heidari, Z., 2013, Denver, Colorado, Petrophysical rock typing in

organic-rich source rocks using well logs: SPE 168913, 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. 3-29.

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. 137-165.

Collins, F.R., 1961, Core tests and test wells, Barrow area, Alaska: U.S. Geological Survey

Professional Paper 305-K, 569-644.

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, 127-140.

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. 279–

302.

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. 143–187.

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.

1148.

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. 37-56.

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. 799-804.

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. 1105-1112.

Mindlin, R.D., 1949, Compliance of elastic bodies in contact. J. Appl. Mech., 16, 259-268.

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. 513-528.

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. 391-396.

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. 89–110.

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.1777-1794.

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. 261-292.

Prasad, M., Kenechukwu, C., McEvoy, E., Batzle, M., 2009, Denver, Colorado, Maturity and

impedance analysis of organic-rich shales: SPE 123531, 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. 257-272.

Tutuncu, A.N., 2010, Salt Lake City, Utah. Anisotropy, compaction and dispersion characteristics

of reservoir and seal shales: presented at the 44th US Rock Mechanics Symposium.

Vernik, L., 1993, Microcrack-induced versus intrinsic elastic anisotropy in mature HC-source

shales: Geophysics, Vol. 58, No. 11, P. 1703-1706.

Vernik, L., and Nur, A., 1994, Ultrasonic velocity and anisotropy of hydrocarbon source rocks:

Geophysics, 57: 727-7351.

Vernik, L., and Landis, C., 1996, Elastic anisotropy of source rocks: Implications for hydrocarbon

generation and primary migration: AAPG Bull., 80: 531-544.

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geochemical correlation of crude oils, North Slope, Alaska: AAPG Bulletin.

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.

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.

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.

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.

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

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.

63