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CCCD Augustine of Hippo on The Cause of Evil Research Paper

CCCD Augustine of Hippo on The Cause of Evil Research Paper.

I. Annotated Bibliography1. Begin by reading the instructions for the complete research paper (below). The annotated bibliography is just designed to help you get started on the research you will need to write this paper.2. From among those we have already studied this semester, choose one philosopher as the focus of your research paper. The title of your bibliography should include this philosopher’s name.3. Select what will be your “key” passage from this philosopher. Include this passage in full, together with a page number, at the beginning of the bibliography.4. Select the three supporting passages that your “key” passage will help explain. Include these passages in full, together with their page numbers, at the beginning of the bibliography.5. Select at least two secondary sources providing useful historical and philosophical background for your philosopher and chosen key passage. Provide full bibliographical information for both. Write a paragraph of 4-5 sentences for each source explaining why these particular secondary sources will be useful.II. Research PaperYour research paper should precisely meet the following requirements:1. Your paper should be formatted in the following way: 12 pt. Times New Roman font, 1” margins, double-spaced, 8-10 pages long (not counting the required works cited page), with no cover sheet. Use MLA parenthetical citation format. If you are unfamiliar with this citation format, visit:http://owl.english.purdue.edu/owl/resource/747/01/ (Links to an external site.)(Links to an external site.)2. Define your paper’s unique topic in the following way. Choose a single author we have discussed this semester as a focus for your paper. Review the reading assignments for this author and select one passage (around four to six sentences long) that you think is the most important passage for understanding everything else that this author has written. Then select at least three other passages (each around four to six sentences long) whose meaning is deepened and clarified by the key passage you’ve already selected.3. The body of your paper should consist of the following sections (please use sub-headings to clearly demarcate each of them from the others):a. An introduction that introduces the general problem and includes a thesis statement. The thesis statement must present an argument that makes a claim and supplies reasons supporting that claim (e.g., “This particular passage from Plato’s Euthyphro on piety is crucial to understanding the whole of the dialogue because. . .”). Your thesis statement must claim that a particular passage is the general key to reading and interpreting your chosen philosopher.b. A section that introduces your selected key passage and sets it in its textual context: what is the text, what is the text about, who is speaking, at what point in the argument are they speaking, and what are they aiming to prove?c. A section that gives relevant biographical/historical information about the author and the text and relevant general information about the philosophical period in which the author is working. At least two separate, secondary sources must be used in this section.d. A section that, similar to your weekly writing assignments papers, offers a detailed analysis of the meaning and arguments presented in the selected passage. This section should clearly explain why this passage is, in your view, the most important passage for interpreting the whole of the author’s work. Additionally, this section should clearly refer to and make use of the background information supplied in the preceding three sections. Strive to point out and define key philosophical terms. (This section should be at least 2 pages.)e. A section that explains the meaning of at least three other passages in light of the selected key passage. If your selected passage is the key to understanding the whole philosophical work, then how does it help us to understand these other particular passages? This section should be substantive and will require sustained attention: demonstrating the usefulness of your thesis will depend primarily on the work you do here. (This section should be at least 2-3 pages.)f. A clear and concise conclusion that ties to together the paper’s argument.g. A MLA style works cited page.2 mins ago
CCCD Augustine of Hippo on The Cause of Evil Research Paper

Explain in detail how interest rates are determined in the money market. Examine the likely consequences for the macroeconomy of a reduction in the rate of interest and highlight the factors that might limit the effects. This essay is going to demonstrate how the rate of interest is determined in the money market. It will examine the impact that a reduction in the interest rate has on the economy. The framework used will be the interest rate mechanism, where an increase in the money supply will change interest rates and stimulate interest-sensitive expenditures. It will then highlight the factors that can limit and offset the effects of a reduction in the interest rate. The interest rate is defined by Sloman et al. (2012) as the price paid for borrowing money. Two factors that determine the interest rate is the supply of money and the demand for money. The supply of and demand for money in the economy interact together to reach a level of equilibrium. According to Sloman et al. (2012) the money market is a market for short-term debt instruments in which financial institutions are active participants. Figure 1 and 2 illustrates the money market and the demand for money. The demand for money refers to an individual’s desire to hold their wealth in the form of money instead of using it to purchase goods or financial assets. The money demand curve is downward sloping as an increase in the interest rate leads to a decrease in the quantity of money demanded. Money supply is the entire stock of currency and other liquid instruments in the economy. The money supply is set by the central bank (Bank of England) and is exogenous (does not depend on the demand for money). The money supply is fixed and is not influenced by the rate of interest. In figure 1, the x-axis measures the money supply, the y-axis represent the rate of interest and the L curve represents the liquidity preference curve (demand for money). The money supply is represented by the vertical line Ms. The intersection of the money supply and money demand curves reveals the equilibrium rate of interest and is fixed at that point where they equate. According to Keynes the intersection of the curves is purely a monetary phenomenon. John Maynard Keynes (1936) in his book the General Theory of Employment, Interest and Money described the demand for money through liquidity preference framework. According to this theory, the primary reasons for holding money are for transactional, precautionary and speculative demands. The sum of all three demands make up the total demand for money. According to the theory, if interest rates are high individuals demand for money (liquidity preference) is low and when interest rates are low, the demand for holding money increases. In figure 2, the curve L1 is the transaction plus precautionary demand for holding money. L stands for the liquidity preference and by definition; the liquidity preference is the demand for holding assets in the form of money. L is the total demand for money balances and is derived by the horizontal addition of curves L1 (the transactions plus precautionary demand for money) and L2 (the speculative demand for money). The shift from L1 to L2 illustrates a shift in the liquidity preference (an increase in the demand for holding assets in the form of money). The interest rate mechanism is graphed in a three-stage process. Stage 1 illustrates the money market, where an increase in the money supply from M to M’ (with everything else being equal) leads to a fall in the rate of interest from r1 to r2. At stage 2, the fall in the interest rate leads to an increase in the level of investment from I1 to I2. The increase in the level of investment translates in the third diagram shown in stage 3. Lower interest rates increases investment as it becomes relatively cheaper for firms to invest and businesses to take out loans to finance greater spending and investment. Stage 3 shows how a rise in investment leads to a multiplied rise in the national income from Y1 to Y2. Stage 3 shows the Keynesian withdrawals and injections function where an increase in investment has increased the level of injections J1 to J2. This excess in injections over withdrawals will lead to a rise in the national income from Y1 to Y2. Interestingly, an increase in the level of income means that consumers will have more disposable income for consumption purposes (Sloman et al. 2012). Consumption is the largest component of aggregate demand and has an effect on other components of aggregate demand such as net exports and investment Griffiths and Wall (2007). Lower interest rates increases the level of consumption by making the opportunity cost of consumption is lower. This encourages greater expenditure as borrowing through credit cards becomes cheaper. Lower interest rates makes saving less attractive by reducing an individual’s incentive to save. This lower incentive to save encourages consumers to spend rather than to hold onto money. It also reduces the income from savings and the interest rate that is due on loans taken out. However, borrowing now becomes more attractive and this stimulates an increase in spending. Lower Interest rates can boost the prices of assets such as shares and houses. Higher house prices means that current home owners must extend their mortgages which further enables them to finance higher consumption. Interestingly, the higher asset prices increases the wealth of households (through the wealth effect) which increases their incentive to spend as confidence will be higher. Higher asset prices means that businesses are also able to finance their investment (purchase of capital) at a lower cost. Lower interest rates also reduces the cost of interest payments on mortgages by reducing the monthly cost of mortgage payments. This increases the disposable income of householders which increases their level of spending. Moreover, lower interest rate can reduce the value of the Pound Sterling. If UK interest rates fall relative to overseas, saving money in UK becomes less attractive as higher returns can be earned in another country. This reduces the demand for the pound sterling and causes the reduction in the value. In figure 6 at stage 2, the fall in the currency is due to a decrease in the demand for the Pound Sterling in the foreign exchange market. The rise in the supply of the domestic currency from S1 to S2 leads to a fall in the demand for the currency from D1 to D2 and this causes a depreciation in the exchange rate from er1 to er2. This fall leads to a rise in the demand for exports as UK exports become relatively cheaper and more attractive overseas. There will also be a fall in demand for imports (as they become more expensive) and thus causing an increase in the national income (which further increases spending). What if other factors can offset the full extent of a reduction in interest rates? There exist time lags in the economy that can limit the impact of rate cuts on the level on interest-sensitive expenditures. In figure 4, the increase in the money supply lead to a multiplied effect and resulted in a rise in the national income. However, the mechanism failed to highlight how a rise in income will also lead to a rise in the transactional demand of money (L1). In this circumstance, at stage 1, L1 would shift to the right and thus lead to a smaller fall in the interest rate than illustrated. Thus, the level of investment at stage 2 and the national income at stage 3 will not rise as much as shown as well. The overall effect of the money supply on national income will depend on the size of each stage. Their relative sizes depend on the shapes of the liquidity preference and investment curves (as in figure 6 and 7). A bigger change in the interest rate will be caused if the liquidity preference is less elastic. The more interest-elastic the investment curve is, the bigger the change in investment. If the marginal propensity to withdraw is lower and therefore the curve is flatter, this will cause a bigger multiplied change in the national income than illustrated (Sloman et al. 2012). Keynesian economists stress how volatile stages 1 and 2 are in the interest rate mechanism. What if increasing the money supply leads to no interest rate reductions? What if investment is inelastic and cannot be influenced by changes in rates. Figure 6 illustrates an elastic liquidity preference curve. The less elastic the liquidity preference is, the bigger the change that will be caused in the interest rate. Due to its gently sloping curve, a rise in the money supply from M to M’ will lead to an only small fall in the interest rate. This will them limit the impact that the interest rate has on consumption, saving decisions and any other interest-sensitive expenditures. According to Keynesians, the demand for money (L) can be very elastic in response to changes in the interest rates and the liquidity preference curve can become relatively flat. The full effect of a rate cut can be limited greatly by the nature of the demand curve. At r2, if individuals perceive and expect no further rate cuts, any increase in the money (from M’ to M’’) will have no impact on r. The liquidity trap is where Keynes believed this additional money will be lost in. within this theory, interest rates have a floor where an increase in the money supply has no further impact. The financial crisis 2008-09 was a predicament where policy makers feared that increases in the money supply will lead to idle balances lost in the liquidity trap. The central bank used an unconventional monetary policy known as quantitative easing, where they deliberately increased the base rate via the purchase of bonds and other securities in exchange for money. This process of credit creation was used to increase bond prices and thus reduce the interest rate and stimulate growth. Arguably, increases in the money supply will have some impact on the rate of interest as we have seen in the financial crisis where deliberate increases in the money supply lead to further increases in the interest rate and thus spending as well (Sloman et al. 2012). Figure 8 illustrates the effect on interest rates of an unstable liquidity preference curve. This figure further explains how the liquidity preference curve fluctuates due to factors such as expectations in the inflation rate and direction of the interest rate (to name a few). Therefore, due to its instability it is difficult to predict the effect on interest rates of a change in the money supply. Another factor that can influence the investment schedule are changes in investor confidence. An increase in investor confidence can shift the investment curve to the right and at any given interest rates, firms will want to invest more. A decrease in their confidence would shift the curve to the left. If investors believe that the economy is going to get out of recession, their confidence and level of investment will increase. If firms believe that inflation will rise and that the central bank will soon increase the interest rate, confidence and investment in the economy will be low (Sloman et al 2012). In Figure 7, a bigger change in investment will be caused if the investment curve is more interest-elastic. In the liquidity preference framework, investment demand is unresponsive to interest rate changes and that a large change in the interest rate is detrimental to affect investment. Evidence to confirm this was illustrated through the impact of investor confidence. This consensus on the behaviour of investment can be argued in that the focus should be more on how volatile and erratic investment is in response to confidence than its responsiveness to the interest rate. For example, in figure 9, the impact of a fall in interest rates is limited by business confidence. Initially, the reduction in the interest rate has increased investment. However, if the fall in interest rates is accompanied by an increase in business confidence by investors, the investment curve will shift from l1 to l2. On the other hand, if the fall in the interest rate is accompanied by a decrease in confidence then the investment curve will decrease and fall shift from l1 to l3. This impact is contrary to what was illustrated when the investment curve was believed to be inelastic. Therefore, expansionary monetary policy is likely to be more effective if firms have confidence in its effectiveness (Sloman et al. 2012). In the liquidity preference framework, the assumption is that an increase in the money supply leads to lower interest rates if everything else remains equal. However, in reality an increase in the money supply might impact other factors in the economy that could increase the interest rate instead of decreasing it. Two factors to highlight are the income effect and the price-level effect. The income effect describes how an increase in the money supply has an expansionary influence on the economy and this in effect raises the national income and wealth. The liquidity preference theory predicts that an increase in the national income and wealth will increase the interest rate and offset the original impact of an increase in the money supply. Another effect that can limit the impact of a reduction in interest rates is the price-level effect. In this effect, an increase in the money supply increases the overall price level which also increases the interest rate. In conclusion, economics is a social science where theories are constantly examined and redrafted. In the interest rate mechanism theory, an increase in the money supply will lower interest rates and stimulate interest-sensitive expenditures. This stimulation will have a multiplied effect on the level consumption, business investment, mortgage payments and asset prices. However, the impact of a reduction in the interest rate on the economy is quite a complex subject to address. Many determinants must be factored in for the full impact to be noticeable. Even if the overall effect of a reduction in the interest rate is quite strong, it is highly unpredictable to measure and estimate the magnitude of it. Investment is influenced by confidence and on elasticity to the interest rate. This changes the original impact of a rate cut. The nature liquidity preference curve can be highly unstable and not be impacted by any changes in the interest rate. There also other factors like the price-level, expectations and income that can impact and offset the intended purpose of an increase in the money supply. All the factors highlighted in this essay can limit and offset the impact of a reduction in interest rates on interest-sensitive expenditures and the growth of the economy. REFERENCES Keynes, J.M. (1936), The General Theory of Employment, Interest and Money, CreateSpace Independent Publishing Platform Griffiths, A. and Wall, S. (2007) Applied economics, 11th ed. Harlow: Addison Wesley Longman. Sloman, J., Wride, A. and Garratt, D. (2012) Economics, 8th ed. Harlow: Pearson Education Limited. BIBLIOGRAPHY http://www.bankofengland.co.uk/monetarypolicy/Pages/overview.aspx http://www.macrobasics.com/chapters/chapter8/lesson83/ http://harbert.auburn.edu/~thommsn/FINC-3700/ME7-WebChapters/WebApp04_4.pdf http://www.stlouisfed.org/publications/re/articles/?id=2505 http://www.bankofengland.co.uk/publications/Documents/quarterlybulletin/qb120104.pdf https://www.creditwritedowns.com/2010/10/on-liquidity-traps-and-quantitative-easing.html
Portland State University Business Question on Trade Deals Discussion.

I’m working on a business writing question and need a sample draft to help me study.

focus on what you learned from the article, facts you did not know or implications you had not considered. Also, be critical and assess how much you think the author ignores or minimizes other information and/or perspectives you think should have been included in the article, or deserved more attention or integration into their reporting or argumentation. Try to fill up one complete page with your analysis (target a word count of 400-500). The articles are attached belowhttps://www.thejournal.ie/eu-mercosur-deal-explain…
Portland State University Business Question on Trade Deals Discussion

MDC The Need for More than Justice by Annette C Baier Analytical Review

MDC The Need for More than Justice by Annette C Baier Analytical Review.

Write a short, objective summary of 350 words which summarizes the main ideas being put forward by the author in this selection. After reading all of Chapter 7, please select ONE of the following primary source readings:“The Need for More than Justice” by Annette C. Baier (starting on page 188)Please read instructions below:All reading summaries must follow standard formatting requirements. (That is, standard margins, font size, and paragraph spacing is observed. Students cannot manipulate font size, margins, and paragraph spacing to meet length requirements.)Reading Summaries require that you read a primary source selection from the end of each chapter, then write a short summary that identifies the thesis and outlines the main argument. Reading summaries are not about your opinion or perspective – they are expository essays that explain the content of the reading. All reading summaries must include substantive content based on the students reading of the material.When writing Reading Summaries, you may not quote the author without proper citations. In other words, if you use the exact words of the original author (copy-paste) you MUST do a proper citation. Similarly, if you use any other website (such as Wikipedia, Internet Encyclopedia of Philosophy, etc.) you must cite the source. Failure to cite sources properly is in violation of Student Rights and Responsibilities Manual which may result in grade penalties.When in doubt, quote/cite your sources. All quotes, references, and ideas lifted from any source – including internet sources MUST be properly cited in MLA/APA format.All reading summaries must be thoroughly proof-read and checked for spelling, grammar, and punctuation. Students should write in professional, academic prose and use only appropriate language. Spelling and grammar count towards your grade in every assignment.
MDC The Need for More than Justice by Annette C Baier Analytical Review

Complete Short 2 Page HRM Task (DEX)

essay writer free Complete Short 2 Page HRM Task (DEX). I don’t understand this Business question and need help to study.

Instructions
For this assignment, you will write an essay that explores the topics of gender gap and compensation.
In your introduction, explain whether you think the gender gap is a women’s issue, men’s issue, or both. Explain your response and reasoning within your introduction.
Then, divide the body of your paper using the headers below, and cover in that section what is indicated under the header.
Closing the Gap
Explain why the gender gap continues to be an issue in our society and what can be done to help close this gap in terms of opportunities and pay.
Legal Provisions
Identify legal provisions that are in place for addressing the gender gap. Hypothesize why legal provisions have not been successful in closing the gap. Discuss how ethics may play a role in future changes.
Recruitment Planning
Explain what human resource professionals should consider when planning compensation and pay during recruitment planning.
Support your essay with a minimum of two resources from the CSU Online Library. Your essay must be at least two pages in length, not counting the title or reference pages. Adhere to APA style when constructing this assignment, including in-text citations and references for all sources that are used. Please note that no abstract is needed.
Complete Short 2 Page HRM Task (DEX)

community experience

community experience.

Find Ted talk or documentary related to the multidisciplinary field of criminal justice. It can be related to public safety, transportation, business, housing, youth development, violence prevention, or another aspect of civic life. use it to answer the following question.- Name, location, date, time, sponsors- Description of the problem or issue to be addressed by the event or program- Summary of the background of the issue that is addressed through this program- Goals or Purpose of the program/project or event.- How does this project/program or event relate to criminal justice?
community experience

Lithofacies-dependent Rock Physics Templates of an Unconventional Shale Reservoir

Lithofacies-dependent rock physics templates of an unconventional shale reservoir in North Slope Alaska Abstract Organic-rich shale has become an increasingly important hydrocarbon resource around the globe due to rapid depletion of conventional reservoirs. Successful exploration and production schemes for shale source rocks 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 or TOC) and quality (hydrogen index or HI, which determines kerogen type) in promising shale formations through indirect seismic data, which is usually the only available source of information in frontier settings. We delineated different seismic lithofacies in the Alaska North Slope Alaska in terms of elastic, seismic and petrophysical properties. From this characterization process, rock physics templates of inverted seismic parameters (Acoustic Impedance, or AI, versus P-wave over S-wave ratio, or Vp/Vs are constructed for each lithofacies to assess pore fluid distribution and lithology. We proposed useful correlations between source rock attributes (TOC, Hydrogen Index or HI) and petrophysical properties (bulk density, porosity, gamma ray, sonic velocities Vp/Vs) of major lithofacies. These correlations, together with facies-specific rock physics templates, assist in mapping organic richness and reservoir properties from seismic-derived attributes. Existing shale petrophysical models, calibrated and constrained to North Slope geology, are then verified by this training dataset to observe their applicability in the basin. Introduction Alaska North Slope (formally the Colville Basin) is estimated to contain approximately 40% of total US 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 (Bird, 2001). Shale oil interest is gaining a lot of traction because of the development of advanced technologies over the past 20 years. Petroleum exploration on the North Slope is limited to the region near the Beaufort Sea coast located between the National Petroleum Reserve Alaska (NPRA) and the Arctic National Wildlife Refuge (ANWR). Few wells have been drilled outside of Prudhoe Bay region, resulting in sparse information for proper formation evaluation and lithofacies classification in shales. Traditionally, formation evaluation and production planning of shale plays pose considerable challenges due to their complex lithology and significant lateral and vertical variation of petrophysical properties. Therefore, a key issue for future exploration of the North Slope is the lateral variability of source rock away from known hydrocarbon accumulations. 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, 2010). A seismic lithofacies shares characteristic sedimentologic and rock physics properties, thus serving as a major force in controlling reservoir geometry and porosity. This study attempts to characterize petrophysical, geochemical, and elastic properties of shale lithofacies of the North Slope and build a reliable training dataset (P and S-wave velocities, bulk density) for classification. Previous rock classification techniques introduced in organic shale formations are strongly dependent on many core measurements to reasonably capture shale heterogeneity (Gupta, 2013). These are both time-consuming and expensive to acquire. Due to sparse core information in the study area, well logs are a viable candidate for rock classification as they provide relatively high sampling resolution in the vertical dimension, continuous interval properties, and information available in real-time. Cross-validation and proper calibration of log-derived properties with core data are regularly performed throughout this study. Geological Setting Four major source rock units have been identified in the Alaska North Slope. These are the Hue Shale, pebble shale unit (formally the Kalubik Formation), Kingak Shale, and Shublik Formation (Figure 1). The Highly Radioactive Zone (HRZ), also called the Gamma Ray Zone (GRZ) in Figure 2 at the base of the Hue Shale is separated based on petrophysics from the Hue Shale because of its different signature. The most relevant geological features, depositional history, and source rock characteristics of each lithofacies are discussed here. Figure 1: Generalized stratigraphic column for Alaska North Slope, emphasizing petroleum source rocks and their relative ages. GRZ, Gamma Ray Zone. The Lower Cretaceous Unconformity (LCU) lies under the pebble shale unit (Bird, 2001). Shublik The Triassic Shublik Formation of the Ellsemerian sequence 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 is of particular economic interest because it is the principal source of oil and gas in the North Slope, accounting for more than 90% of the recoverable crude oil and 82% of the recoverable gas (Bird, 2001). It is organically rich (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, 1996). Figure 2: Map showing major tectonic features of Northern Alaska. ANWR, Arctic National Wildlife Refuge; NPRA, National Petroleum Reserve-Alaska; PB, Prudhoe Bay (Bird, 2001). Kingak The Jurassic-Lower Cretaceous Kingak Shale comprises the bulk of the Beaufortian sequence that was deposited during rift opening of the Arctic Ocean (Hubbard, 1987). 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, 2004). Kingak Shale contains a mixture of marine and terrigenous organic matter deposited in a marine siliciclastic setting (Peters, 2006). The lower part of the Kingak is typically the most organic-rich interval with an 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 via development of enhanced porosity in sub-unconformity reservoirs, provision of a migration pathway for hydrocarbons, and juxtaposition of overlying marine mudstone source and seal rocks, such as pebble shale unit and HRZ of the Hue Shale (Bird, 2001). Pebble The pebble shale unit was deposited during a south-to-north marine transgression in response to subsidence of the rift margin. It is characterized by a small but distinctive proportion of pebbles and well-rounded frosted sand grains scattered through the shale (Molenaar, 1987). Pebble shale unit differs in its organic characteristics: being oil-prone in some areas and gas-prone in others. Despite its relatively high TOC 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, 2001). Hue The Hue Shale is the distal-deltaic condensed section of the Brookian sequence and was deposited in a deep-water basin plain environment. 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 signature. This organic rich interval has a range of TOC from 1.9 to 3.9 wt.% (Keller, 2001). General Classification 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 in biogenic and detrital quartz/feldspar). Based on limited X-ray diffraction (XRD) analysis and geological background, Hue Shale is classified as siliceous mudstone whereas Shublik Formation is classified as siliceous marlstone (yellow circles in Figure 3). Figure 3: Ternary diagram shows shale classification of Hue Shale and Shublik Formation (blue triangles) based on XRD analyses. Other notable shale plays are also presented in the diagram. Methodology In this study, regional geology, standard triple combo logging suites, petrophysical, and geochemical analyses of core plugs are basic inputs to obtain facies definition, which is the first step of a more comprehensive statistical rock physics evaluation workflow (Figure 4). Figure 4: Diagram showing quantitative seismic interpretation workflow with integration of geochemical data. In this study, we focus on the parts of the workflow that are related to the construction of a reliable elastic and geochemical training dataset of each pre-defined lithofacies. Quantitative seismic interpretation (Figure 4) demonstrates how rock physics can be applied to predict reservoir parameters such as lithology, pore fluid, and source rock character from seismically derived attributes. Based on available logs, cores, and geology, we identify major seismic lithofacies by observing cluster separation in cross plots of different properties. Rock physics aids in converting geologic and wireline logging information into elastic properties (Vp, Vs and bulk density ρb). Geochemical parameters are integrated into the workflow by establishing correlations between elastic and source rock properties. After performing proper scale calibration of inverted seismic data in the area of interest, we use this dataset to classify lithology and source rock character to detect best producing intervals. In this study, we focus on the parts of the workflow that are related to the construction of a reliable elastic and geochemical training dataset of each predefined lithofacies. Well log data (density, gamma ray, resistivity, and sonic wave velocities) are extracted for exploratory cross-plots and quantitative assessment. (The use of cross-plots between relevant log-derived properties to separate lithofacies proves to be a fast and simple process that can also be applied at the wellsite.) Based on the top and base depths of various lithofacies in the well, we 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. Neutron log cannot be used in radioactive shale intervals because 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 the same subset of core plugs due to different labs conducting experiments at different times. Therefore, existing correlations in the literature to expand the available dataset are utilized. Intrinsic variability of rock properties within a single lithofacies presents the biggest challenge of quantitative seismic interpretation, especially when an observed attribute change indicates a significant change across facies rather than a minor fluctuation within facies (Avseth, 2010). Dataset Two vertical wells located along the Trans-Alaska Pipeline System are shown in Figure 5. These wells, Merak-1 and Alcor-1, were drilled by Great Bear Petroleum in 2012 and cored the Hue and Shublik intervals extensively (Scheirer et al., 2014 and Scheirer et al., 2017). In addition to the standard log suite, available data include dipole sonic log and spectral gamma ray log. The two wells of interest are 1.5 miles apart and have shown excellent correlation in terms of petrophysical properties and source rock character. Vertical Seismic Profiling and 3-D seismic are also available for future study. Available core analyses include: porosity, permeability, oil/gas saturation, X-ray Diffraction (XRD), and computed tomography scans. In addition, geochemical data are available for core and cuttings samples. Cuttings measurements are not included in this study. Geochemical data include Leco TOC, programmed pyrolysis, and vitrinite reflectance (R0). Figure 5: Focus area is located between the NPRA and ANWR. The area of interest shows locations of two vertical wells of interest. The blue dashed line indicates the area of available 3-D seismic data. Seismic lithofacies definition Logging analysis Common logging tracks are plotted to verify several key signatures of each lithofacies (Figure 6). The density of the Hue-HRZ interval is relatively constant. However, the HRZ has significantly higher gamma ray, as expected, and lower sonic velocities than the overlying Hue Shale because of smaller clastic dilution (more clay content) and less proximal deposition. A spike at 8700 feet in the density log in the Merak-1 well is due to a change in equipment after setting intermediate casing. Due to these reasons, the Hue Shale and HRZ will be separated into two separate lithofacies. The pebble shale unit has a wide range of density values due to the pebbles and well-rounded sand grains in its fine-grained matrix. In terms of radioactivity level and acoustic properties, Kingak Shale is a relatively homogeneous interval. Nevertheless, the density values of the Kingak Shale vary considerably due to its depositional history, a mud-dominated succession of prograding shelf deposits characterized by multiple transgressive-regressive sequences. The Shublik Formation has abrupt high gamma ray bands interbedded with lower gamma ray intervals. Spikes in both the gamma ray and density tracks indicate different amounts of clay and carbonate, respectively, throughout the Shublik interval. It also has much higher velocities of both P and S waves compared to other facies because its matrix has a greater amount of carbonate. Figure 6: Diagram showing the log response of the organic rich shales investigated in this study. From top to bottom: red (Hue), green (HRZ), blue (pebble shale), black (Kingak), and pink (Shublik). This color code is used throughout this study. Cross-plots of P and S-wave velocities versus bulk density show some degree of separation between different shale units. Whereas Hue and HRZ display considerable fluctuations of acoustic velocity within a small range of density values, pebble shale and Kingak shear velocities show relative independence of bulk density. Despite the small distance between the Merak-1 and Alcor-1 wells, lateral variability in lithofacies’ properties is demonstrated by the data clusters for the pebble shale and Kingak intervals in Alcor-1, specifically, the absence of low-density components in Alcor-1 (Figure 7). Figure 7: Vp and Vs (m/sec) versus bulk density gm/cc of different shale lithofacies in two wells: Merak-1 (top panel) and Alcor-1 (bottom panel). Graphs are of similar scale for comparison. The wells are plotted separately to observe variation between them. Another useful cross-plot is Vp versus Vs (Figure 8). Shublik and Hue are readily separated from other clusters. Pebble shale, Kingak, and HRZ clusters overlap. Dashed blue lines represent lines of constant Vp/Vs, which have been suggested to be a good indicator of organic-rich shale (Vernik, 2011). In several published datasets compiled by Vernik as well as in core and log data from Bossier, the Woodford and Bakken shale plays fall within a relatively narrow Vp/Vs range regardless of the wide observed range of saturation, porosity, and effective stress. These parameters seem to be secondary in controlling the reduced velocity ratio typical of organic shales as compared to their inorganic counterpart. In Alaska North Slope, the spread of velocity ratio spans 1.6 to 2.4, significantly broader compared to other shale plays (Vernik et al. 1996). The organically 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, 2012) between TOC content and Vp/Vs. Figure 8: Vs versus Vp from dipole sonic log of Merak-1 and Alcor-1. Blue dashed lines represent constant Vp/Vs ratio. Plots are of similar scale for comparison. To create a bridge between geochemical data and petrophysical data, dense geochemical data are required. Computation of TOC from available logs, in this case resistivity log, is necessary to supplement the more sparsely distributed geochemical data. In addition to low resolution, resistivity measurements in logging devices are strongly dependent on thermal maturity. Oil generation results in an increase in resistivity whereas expelled gas (products of oil cracking at higher maturity) decreases resistivity (Mann, 1986). Low resistivity therefore can indicate both immature and overmatured 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 Δlog(R) technique). The method involves overlaying a properly scaled porosity log (or transit time log) on a resistivity curve (ideally from a deep reading tool). The separation between the 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, 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 (Tran, 2014). In this study, superposition of deep resistivity and sonic transit time logs on a predefined scale 50 μm/feet to one resistivity cycle in log scale) shows good separation in source rock intervals (Hue, Shublik, Kingak) and decent overlap in inorganic intervals. The Miluveach Sandstone (a non-source inorganic rock) is picked to be the baseline interval as the two curves run parallel and overlap in this interval. Values of baseline resistivity Rbaseline and baseline transit time in the Miluveach Sandstone Δtbaseline, as well as resistivity and transit time of layers of interest, are input to calculate TOC (Equation 1): ∆log⁡R=log⁡RRbaseline 0.02*(∆t–∆tbaseline) (1) TOC=∆log⁡R*102.297–0.1688*LOM (2) In Equation 2, LOM is the level of maturity and is determined separately for each source rock. For type II and III source rock, the cross-plot of programmed pyrolysis S2 peak versus TOC of core plugs is used to determine the LOM value of Hue/HRZ. In Merak-1, the LOM is 8.5 and in Alcor-1, it is 9.5. The LOM in both the Kingak and Shublik in both wells is 12 (Figure 9). Figure 9: S2 peak mgHC/gRock versus TOC (wt.%) of core plugs in geochemical dataset. Black lines indicate different Level of Maturity LOM as defined by (Passey, 1990). Spikes in the TOC logs might be attributed to anomalies in the deep resistivity log. Cross-validation with geochemical core data shows a reasonable agreement in organic-rich intervals in the Merak-1 well (especially in the Shublik and Hue intervals). Only a small portion of the Kingak Shale is matched because we do not have enough core of this thick interval. In Alcor-1, the Shublik Formation is also sparsely sampled so this method cannot guarantee the match for the whole interval. Core analysis This study lacks a complete set of core plugs with geochemical, acoustic, and petrophysical data. Due to time constraints, data of different scales (well log versus core plug) is cross-correlated. Preliminary quality check shows that bulk density of log and core at similar depths are in reasonable agreement. 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 10). Log values of bulk density of the Shublik and HRZ show very good consistency with core measurements; thus, no correction is necessary. However, other factors may obscure the value of bulk density log such as varying pyrite concentration and natural fracture system. Log values of density in the Kingak 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. Figure 10: 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 North Slope shale units have 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. Cross-plots of Vp vs Vs and TOC vs hydrogen index (HI) show good separation between different lithofacies. A simple correlation between geochemical and petrophysical parameters is not easy to deduce because log response in shale intervals is complex and affected by not only the organics but also mineralogical and pore fluid properties of the rock (Tran, 2018). Looking closer at a single lithofacies, the correlation is stronger, but it is not as profound as the velocity-density relationship. Acoustic analysis in other notable shale plays (Bakken, Bazhenov and Niobrabra) is compiled by (Vernik, 2011), showing that Vp increases as HI decreases, except in high porosity shale where Vp is better correlated with porosity (or density). 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, 1986). 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 established earlier come in handy. In the Shublik Formation, TOC and acoustic velocities show a strong directly proportional correlation. Hue and HRZ clusters are significantly overlapping, as are pebble shale and Kingak (Figure 11). Figure 11: 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. Rock physics template Rock physics models link seismic properties to geologic properties. Expanding on the earlier rock physics diagnostics, 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 Alaska North Slope are created. 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 organic-rich shale (Avseth, 2010). This RPT approach allows a 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 the HRZ and Shublik Formation in Alcor-1 (Table 1). To simplify the matrix composition, only minerals that are of significant amount and critical inputs in existing rock physics models in the literature (quartz, clay, and carbonate) are considered. 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. Table 1: Simplified composition for HRZ, Kingak and Shublik to use as inputs of soft sediment model 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 1 presents the simplified lithology of the HRZ, Kingak, and Shublik intervals for the rock physics soft sediment template. The soft sediment model uses Hertz-Mindlin contact theory to calculate high-porosity end members at critical porosity and the modified lower Hashin-Shtrikman bound (Mindlin, 1949 and Hashin, 1963) to interpolate 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 RPT used here requires several inputs (effective pressure, volume composition) to calculate shale elastic properties (acoustic velocities at different saturations, bulk density) (Tran, 2018). Pressure data is not available in the two wells studied here, so standard lithostatic and pore pressure gradients are assumed (1 and 0.433 psi/ft respectively) for calculation of effective pressure. Therefore, the effective pressure gradient is 0.567 psi/ft. Other inputs of the soft sediment model are mineral and fluid bulk/shear modulus and critical porosity (0.7 for shale). This model calculates shale elastic properties and yields a Vp/Vs versus P-wave impedance trend superimposed onto log-derived data points. The soft sediment model examines expected changes of these seismic attributes with regard to changes in pore fluid, pressure, clay content, and mineralogy (blue arrows in Figure 12). This step also serves as a checkpoint to ensure log quality. The cross-plot of AI versus Vp/Vs reveals the trend of RPT-related property change due to shaliness/clay content in the Hue/HRZ (marked by blue arrow 2 in Figure 12). The sub-branches in the trend represent expected change during pore fluid substitution as gas displaces water in pore spaces (Sw varies from 0 to 1). Fluid substitution has to be used with caution because shale lithology (clay minerals) defy the assumptions of Gassmann’s formula (Smith, 2003). 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 of matching 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 an elastic sphere pack subject to a confining pressure, is more applicable to sand than to shale (Avseth, 2010). Another explanation is that the logging device directly measures a layer of low-density organic material at those depths corresponding to dark blue data points in the RPTs. Figure 12: A rock physics template (RPT) of Hue and HRZ intervals 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (2) increasing shale content, (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 petrophysical properties). The cross-plot of AI versus Vp/Vs for the pebble shale unit does not show much density dependence. Figure 13 shows the RPT for the Kingak Shale, in which density proves to be the principal driving force of Vp/Vs -AI trend; clusters of various density magnitude clearly separate from each other. Figure 14 shows that the soft sediment model works well in the Shublik Formation to predict bulk density because the range of bulk density accurately matches density values of data points. In the RPT for the Shublik, high density members fall in the lower density range because of the absence of high-density pyrite in the model. The model is limited to interchangeable substitution of two fluids (in this case, water and gas). The predicted saturation of the soft sediment model shows slight overestimation 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. Figure 13: Rock physics template (RPT) of Kingak Shale 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (2) increasing shale content, (3) increasing gas saturation. Figure 14: A rock physics template (RPT) of the Shublik Formation 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (3) increasing gas saturation. There are several challenges in modeling the composition of organic-rich shale and the porosity effects on their velocities. Porosity is not easily determined from log data due to complication in lithology and ambiguity in measurement accuracy, such as neutron tools in the log suite or ultra-low 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. conclusion Major shale lithofacies in the Alaska North Slope can be qualitatively delineated in terms of elastic and petrophysical properties using simple cross-plots. This is especially true for the Shublik Formation, the Hue Shale, and the HRZ. Gamma ray 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 shales, but little useful correlation is obtained. Weak inverse correlation between Vp/Vs and TOC is observed in North Slope lithofacies. Organic material is not the sole driving force controlling the velocity-density trend because mineralogy and fluid properties also play a part. Clay content plays a key role in the velocity-density trend of the Kingak Shale, assuming that it is directly related to gamma ray. Existing shale petrophysical models can be applied if it is properly calibrated to specific regional geology of the North Slope. The soft sediment model is applied to produce rock physics templates, which result in good agreement for 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 the statistical rock physics workflow. There is a need to account for different physical scenarios across the area that is prospective for unconventional exploration that might not be present at the wells. A possible solution is to use correlated Monte Carlo simulation to expand the training dataset to account for natural variability. Substantially more core analyses will improve the quality of the training dataset and thus add value for more reliable correlations. acknowledgments We wish to thank Great Bear Petroleum for providing the dataset needed for this study. We also appreciate Ken Bird and Les Magoon for providing insightful comments during this study. References Avseth, P., Mukerji, T. and Mavko, G., 2010. Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk. Cambridge university press. Bird, K.J., 2001. AAPG Memoir 74, Chapter 9: Alaska, A Twenty-First-Century Petroleum Province. Gupta, N., Rai, C.S. and Sondergeld, C.H., 2013. Petrophysical characterization of the Woodford shale. Petrophysics, 54(04), pp.368-382. 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., Edrich, S.P. and Rattey, R.P., 1987. Geologic evolution and hydrocarbon habitat of the ‘Arctic Alaska microplate’. Marine and Petroleum Geology, 4(1), pp.2-34. 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. Mann, U., Leythaeuser, D., Muller, P.J., 1986, 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, p. 391-396. 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. 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. 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. Scheirer, A.H., Magoon, L.B., Bird, K.J., Duncan, E. and Peters, K.E., 2014. Toward successful petroleum production from unconventional and conventional reservoirs in the central Alaska North Slope. Unconventional Resources Technology Conference (URTEC). Scheirer, A.H., Magoon, L.B., Bird, K.J. and Duncan, E.A., 2017, September. Evaluating the Shublik Formation as an Unconventional Resource Play on the Alaska North Slope. In Unconventional Resources Technology Conference, Austin, Texas, 24-26 July 2017 (pp. 3258-3264). Society of Exploration Geophysicists, American Association of Petroleum Geologists, Society of Petroleum Engineers. Smith, T.M., Sondergeld, C.H. and Rai, C.S., 2003. Gassmann fluid substitutions: A tutorial. Geophysics, 68(2), pp.430-440. Tran, M.T., 2014. Formation evaluation of an unconventional shale reservoir: Application to the North Slope Alaska (Master’s dissertation, Stanford University). Tran, M., Aminzadeh, F. and Jha, B., 2018, August. Effect of Coupled Flow and Geomechanics on Transport of a Fluid Slug in a Stress-sensitive Reservoir. In 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association. Vernik, L. and Milovac, J., 2011. Rock physics of organic shales. The Leading Edge, 30(3), pp.318-323. Vernik, L., and Landis, C., 1996, Elastic anisotropy of source rocks: Implications for hydrocarbon generation and primary migration: AAPG Bull., 80: 531-544. Yan, F., Han, D., Yao, Q., 2012, Oil shale anisotropy measurement and sensitivity analysis: SEG Las Vegas Annual Meeting. list of figures Figure 1: Generalized stratigraphic column for Alaska North Slope, emphasizing petroleum source rocks and their relative ages. GRZ, Gamma Ray Zone. The Lower Cretaceous Unconformity (LCU) lies under the pebble shale unit (Bird, 2001). Figure 2: Map showing major tectonic features of Northern Alaska. ANWR, Arctic National Wildlife Refuge; NPRA, National Petroleum Reserve-Alaska; PB, Prudhoe Bay (Bird, 2001). Figure 3: Ternary diagram shows shale classification of Hue Shale and Shublik Formation (blue triangles) based on XRD analyses. Other notable shale plays are also presented in the diagram. Figure 4: Diagram showing quantitative seismic interpretation workflow with integration of geochemical data. In this study, we focus on the parts of the workflow that are related to the construction of a reliable elastic and geochemical training dataset of each pre-defined lithofacies. Figure 5: Focus area is located between the NPRA and ANWR. The area of interest shows locations of two vertical wells of interest. The blue dashed line indicates the area of available 3-D seismic data. Figure 6: Diagram showing the log response of the organic rich shales investigated in this study. From top to bottom: red (Hue), green (HRZ), blue (pebble shale), black (Kingak), and pink (Shublik). This color code is used throughout this study. Figure 7: Vp and Vs (m/sec) versus bulk density gm/cc of different shale lithofacies in two wells: Merak-1 (top panel) and Alcor-1 (bottom panel). Graphs are of similar scale for comparison. The wells are plotted separately to observe variation between them. Figure 8: Vs versus Vp from dipole sonic log of Merak-1 and Alcor-1. Blue dashed lines represent constant Vp/Vs ratio. Plots are of similar scale for comparison. Figure 9: S2 peak mgHC/gRock versus TOC (wt.%) of core plugs in geochemical dataset. Black lines indicate different Level of Maturity LOM as defined by (Passey, 1990). Figure 10: 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 North Slope shale units have low porosity. Kingak log values of density are lower than core values possibly due to sampling bias of core plugs towards pyrite-free and unfractured intervals. Figure 11: 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 12: A rock physics template (RPT) of Hue and HRZ intervals 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (2) increasing shale content, (3) increasing gas saturation. Figure 13: Rock physics template (RPT) of Kingak Shale 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (2) increasing shale content, (3) increasing gas saturation. Figure 14: A rock physics template (RPT) of the Shublik Formation 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 given at right. Blue arrows indicate conceptual geologic trends: (1) decreasing porosity (or increasing bulk density), (3) increasing gas saturation. list of tables Table 1: Simplified composition for HRZ, Kingak and Shublik to use as inputs of soft sediment model