The Journal of the Society of Petroleum Evaluation Engineers

Transcription

1 The Journal of the Society of Petroleum Evaluation Engineers Celebrating 50 Years of Promoting the Profession of Petroleum Evaluation Engineering Volume VI, Issue 1 Spring, 2012

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3 Table of Contents Foreword...5 In This Volume...6 Feature Articles Well Performance and Economics of Selected U.S. Shales...7 Leslie O Connor and John Seidle, SPEE Members Shale Engineering Rawdon Seager, SPEE Member Use and Mis-Use of the Graphical P/Z Plot Richard F. Krenek, SPEE Member The Need for an Inferred Resources Category in Reserves and Resources Reporting Michael T. Scott Key Issues in Determining Discount Rates for Valuing Real Property Dr. Hal Heaton The Exploration Game: Statistical Decision Theory Using Bayes Formula Dr. E. L. Dougherty Abstracts and Reviews Review of SPE , Exponential vs. Hyperbolic Decline in Tight Gas Sands: Understanding the Origin and Implications for Reserve Estimates Using Arps Decline Curves T. Scott Hickman Review of SPE , Credit Ratings and Cash-Flow Analysis of Oil and Gas Companies: Competitive Disadvantage in Financing Costs for Smaller Companies in Tight Capital Markets John Davis Reference and Source Articles Engineering and Geophysics...66 Assessment of Undiscovered Oil and Gas Resources of the Devonian Marcellus Shale of the Appalachian Basin Province, 2011 An Overview of Some Key Factors Controlling Well Productivity in Core Areas of the Appalachian Basin Marcellus Shale Play Introduction to Aspects of Reserve Growth Assessment of Undiscovered Oil and Gas Resources of the Cook Inlet Region, South-Central Alaska,

4 Reference and Source Articles Engineering and Geophysics (continued)...66 Review of Emerging Resources: U.S. Shale Gas and Shale Oil Plays Uncertainty and the Volumetric Equation Economics and Finance...69 The Role of WTI as a Crude Oil Benchmark Factors Influencing Oil Prices: A Survey of the Current State of Knowledge in the Context of the Oil Price Volatility Does Paper Oil Matter? Energy Markets Financialization and Equity-Commodity Co-Movements Petroleum Property Valuation The Weak Tie Between Natural Gas and Oil Prices On The Portents of Peak Oil (And Other Indicators of Resource Scarcity) On the Relation between Expected Returns and Implied Cost of Capital Legal and Regulatory Issues...72 Expert Testimony: Regression Analysis and Other Systematic Methodologies Cross-examination Based on Draft Reports is an Uninformative Exercise Daubert challenger reaps $4.7M in attorney/expert fees Expert s hyberbole comes back to haunt in Daubert hearing Oldies but Goodies...75 Government Influence On Industry Operations, Granville Dutton, Journal of SPEE Apparently not much has changed Sources on the Internet

5 Foreword Petroleum Evaluation Engineering is the application of scientific and economic principles to the estimation of the rate and volume of production of hydrocarbons that can be expected from development of a property or project and the amount and value of the income derived therefrom. The purpose of The Journal of the Society of Petroleum Evaluation Engineers (JSPEE) is twofold; first, to provide a source of information regarding evaluation principles and practices that can be applied by evaluation professionals in their daily work and, second, to establish a forum for discussion of topics of interest. The Journal of SPEE is intended to be a peer-reviewed publication that will cover the general topic of evaluation of oil and gas properties and projects with specific emphasis on the practical application of (a) geoscience theory and analysis, (b) reservoir and production engineering theory and experience, and (c) economic and financial theory and practice, to the estimation of future production, income, and value of those projects and properties. To provide useful information, the Journal seeks to attract original papers and articles from SPEE Members (and from non-members) on a range of topics covering as many aspects of evaluation practice as possible and will focus on those areas of engineering and economic practice that are not commonly covered in other publications. In addition, JSPEE will include (i) reviews and excerpts of papers and articles published in other journals and reliable sources, (ii) references to books, articles, presentations and internet sites that may be of interest, and (iii) links to data sources and other information of use to evaluation professionals. The JSPEE can provide the foundation for discussion by offering topics that provide new and interesting information and/or insights and suggest improved application(s) of existing methodologies, some of which may elicit differences of opinion. In this way, the JSPEE hopes to provide a forum for the discussion of important professional topics. But it will be up to SPEE Members and other readers to determine whether the Journal has utility in advancing our profession and fulfilling the objective of SPEE... to disseminate facts pertaining to petroleum evaluation engineering among its Members and the public. This volume is a proto-type of the proposed new Journal. Some aspects of the JSPEE may be changed, expanded or discarded in future issues depending upon the response of SPEE Members to the Journal. Accordingly, along with this publication, you will find a link to a Survey Form that asks a number of questions about the Journal (as presented in this model) and about the willingness of Members to provide (a) content for the Journal in the form of full papers or short articles, reviews of other publications, or contribution of references to information sources, and (b) provide support for the Journal by serving on editorial and/or peer-review committees. The support of Members of the JSPEE, based on the responses to the survey will, in large part, determine the future of the Journal. Please take some time to review this volume and provide your responses to the Survey Thank you, The JSPEE Editorial Committee Richard J. Miller, James W. Haag, R. Keith MacLeod, and Rod Sidle 5

6 In This Issue This volume of The Journal of the Society of Petroleum Evaluation Engineers is offered as a model for future issues. There are six technical papers covering a range of topics and written by knowledgeable authors. Two of the papers, one by Leslie O Connor and John Seidle, and one by Rawdon Seager are expansions of presentations made to the SPEE Annual Meeting of June, 2011, and deal with the current topic of Shale Gas Development. Another paper written by SPEE Member Rick Krenek, discusses an issue that many of us who have worked with natural gas reservoirs will find interesting. Three other papers (solicited for this issue) by Michael Scott, Hal Heaton, and E.L. Dougherty address the topics of Reserves Definitions/Classification in relation to the PRMS, Cost of Capital Discount Rates, and Statistical Decision Theory respectively. In addition, there are reviews of two recent SPE papers by SPEE Members Scott Hickman and John Davis. This is a segment of the Journal that we hope would be expanded. There are many papers and articles written for SPE and AAPG meetings and conferences that could be of interest to SPEE Members if brought to their attention. The Reference and Source Articles section includes a number of papers and articles on Engineering and Geophysics, Economics and Finance, and Legal and Regulatory Issues that have been gathered from a wide range of sources. The JSPEE also includes a section listing websites that offer information on product pricing, shale gas development activity, and discount rates. Finally, we resurrected a Journal paper from 1969 by Granville Dutton which was a thoughtprovoking discussion in its time and which clearly shows that the more things appear to change, the more they really stay the same. Disclaimer The Journal of the Society of Petroleum Evaluation Engineers is intended to promote the profession of petroleum evaluation engineering through the dissemination of information that may be of interest and use to members of the profession; and by providing a forum for the discussion of engineering and evaluation issues. Papers, articles, reviews, and references published in the JSPEE, whether peer-reviewed or not, are selected based on the information they provide. Discussion of any paper, article, review or reference published in the JSPEE is invited and encouraged, in written form, as a Comment which, if found appropriate, will be published in a later issue of the JSPEE as space permits. Papers, articles, reviews, and references published in the JSPEE, whether peer-reviewed or not, are not the product of SPEE, are not endorsed by SPEE, and do not necessarily represent the policy of SPEE on any matter which may be discussed or presented in the JSPEE. Any opinions expressed in any paper, article, review, or reference are the opinions of the author(s) and not necessarily those of SPEE. 6

7 This paper was originally presented at the SPEE Annual Meeting, Amelia Island Resort, Florida, June 6, 2011 Well Performance and Economics of Selected U. S. Shales Leslie S. O Connor, SPEE Member, and John P. Seidle, SPEE Member MHA Petroleum Consultants Introduction Exploitation of oil and gas resources held in shales has been made possible by recent advances in horizontal well drilling and completion technologies. Each shale is unique, no shale play has gone through an entire life cycle, and considerable confusion exists around the economics of shale gas and oil exploitation. Sources of this confusion include unknown well performance, high well costs, and an uncertain U. S. natural gas market. Initial gas production rates of shale wells depend strongly on well completion, and the wells tend to decline sharply. Long term shale well performance remains unknown in most plays and estimated ultimate recoveries (EUR s) quoted by some independents are being questioned by evaluators. Shale wells are expensive with a good fraction of the cost coming from multiple fracture stimulations of long horizontal laterals. Current U. S. natural gas prices, uncertain as ever, are subject to long term downward pressures as concerns about the strength of the U. S. economic recovery weaken natural gas demand. Rapid expansion of shale gas plays threatens a supply glut and transportation bottlenecks. This paper begins with a brief review the unique characteristics of shale gas reservoirs and the wells which drain them. Next, shale well performance prediction methods will be briefly discussed. Lastly economic viability of selected domestic gas shale plays will be investigated. Characteristics of shales Shales, one of the most common sedimentary rocks, are fine-grained rocks originating from clay minerals and muds. They are characterized by thin laminae which break with curving, splintery fractures sub-parallel to the bedding plane. If sufficient organic matter is present in the initial sediments, hydrocarbons are generated as a shale matures. Shales are both source and reservoir and can be gas prone, oil prone, or mixed. Liquid hydrocarbons, gas, and water can be present in the shale pore space and gas can be sorbed on the shale matrix surface. Shale porosities are typically less than ten percent and determination of shale fluid saturations by wireline logs is problematic, complicating volumetric calculation of original gas in place (OGIP) and original oil in place (OOIP). Shale matrix permeabilities are very low, on the order of nanodarcies, and most of the flow capacity of any shale reservoir is due to natural fractures. In-place hydrocarbon volumes in shales are pervasive but lower on a unit reservoir volume than conventional reservoirs, making them diffuse, continuous, heterogeneous hydrocarbon resources. With none of the gas-water or oil-water contacts seen in conventional reservoirs, shale reservoirs are defined by facies pinchouts, thermal maturities, and stratigraphy. Shale plays are geologically complex, not simple resource plays, and commercial shale gas and oil exploitation requires a blend of geosciences and engineering. More than 30 U. S. shale basins (Figure 1) are currently experiencing some level of activity. The technically recoverable shale gas resource in the U. S. is currently estimated to be 862 Tcf ( EIA, 2011) and the fraction of this resource which can be classified as reserves is a source of contention in the oil and gas community. With over a decade of development, the Barnett is the most mature shale gas play in the U. S. and, rightly or wrongly, is often considered to be an analog for emerging shale gas plays. The shale condensate and oil resource (this paper considers only liquid oil in shale, so called tight oil, not oil produced by retorting shales, a liquid also described as shale oil ) is even less well defined. Production of these liquids from shales is problematic when 7

8 the multiple transverse fractures. Volume of this box, the product of lateral length, tip-to-tip fracture length, and fracture height is referred to as the stimulated reservoir volume (SRV). the hard drawdowns required for commercial production from these low permeability formations causes near wellbore pressures to fall below the dew point or bubble point pressures. Assignment of such reserves is problematic and is not considered here. Properties of the shales considered here, the Haynesville, Bakken, and Marcellus shales, are summarized in Table 1. Table 1. Reservoir properties of selected shales property Haynesville Bakken Marcellus age U Jurassic Dev-Miss Devonian fluid type gas oil gas depth, ft 11,000 9,500-4,000-8,000 11,000 thickness, ft pressure, psia 9,900 8,500 2,400-4,700 temperature, F Shale well completions Shale wells are typically completed with a long lateral, stimulated with multiple transverse fractures. Completions vary little between the various basins, regardless of the fluid being produced. The well is typically a monobore, rather than multiple sidetracks kicked off from a vertical well. Lateral lengths have steadily increased to more than 10,000 feet with an optimum length yet to be determined. Current industry practice is to cement a liner in the lateral followed by perforation in clusters and stimulation with thirty or more hydraulic fracture stages (Figure 2). Lateral azimuth is often chosen to be perpendicular to the major stress direction making any induced fractures parallel to the major stress direction and transverse to the lateral. The resulting completion is visualized as a rectangular box defined by the long lateral and Shale well capital costs can exceed $10,000,000 with over half the total well costs being required for stimulations. Up to thirty or more hydraulic fracture stimulations are placed in some 10,000 foot laterals, requiring roughly20,000 barrels of water and2,000,000 lbs of sand. Regardless of the shale formation, only about a third of the load water is recovered, leaving two thirds in the formation. Legitimate concerns from local residents about adequate water supplies and treatment or recycling of produced water are growing in virtually every shale play. Addressing such concerns will lead to increased regulations, longer permitting times, and increased well costs. Shale well performance predictions With very low reservoir permeabilities, shale wells often require years to reach the boundaries of the SRV. Several additional years of production data are required before decline curve analysis can be confidently applied to such wells. A common strategy for estimating EUR s in other low permeability plays is use of the subject well s initial production rate (IP) with a correlation of IP and EUR developed from analogous wells. With the exception of perhaps a few Barnett wells, no full cycle well histories are yet available for shale plays, precluding use of this method for estimating shale well recoveries. Shale gas and oil wells typically show high initial rates and dramatic, hyperbolic declines. The gas production decline of an unidentified gas well (Anderson, 2011) shown in Figure 3 has an initial rate of 3.2 MMcfd and a 8

9 first-year decline of 22% per year. Fitting a decline curve to this early, transient production data yields a hyperbolic decline curve with a long tail. While this approach may be useful for predicting production rates over the next few years, the long tail gives erroneously high estimated ultimate recovery. The two hyperbolic declines shown in Figure 3 have EUR s of 49.8 and 85.5 Bcf. Newly developed production data analysis methods, using plots of a normalized rate-pressure variable against the square root of production time and a flowing material balance, provide estimates of the in place gas or oil currently seen by the well, providing a lower bound on OGIP and OOIP. Recovery factors for these in place volumes and, hence, EUR s are the subject of debate but are currently thought to be 5 to 10% of the original fluid in place. Simulation of shale well performance for reserves estimation is problematic for two reasons. Technically, history matching of only transient production data with a numerical model is insufficient for simulation of long term well performance. Economically, many regulatory bodies prohibit assigning reserves solely via simulation. Numerical models can be part of a suite of techniques for estimating reserves but cannot stand alone in determination of shale oil and gas reserves. The practical result of applying several limited techniques to shale well production decline data are nonunique production forecasts. Production decline forecasts for the unidentified gas well discussed above, shown in Figure 4, all match the early time data nicely, but estimated ultimate recoveries vary from 29.2 to 85.5 Bcf, a factor of nearly three. Uncertainty in shale gas recovery has been documented in the past (Berman, 2009), but more recent is the EIA s slashing of their Marcellus gas estimates by nearly 80% (NYTimes, 8/24/2011). The inability to accurately estimate shale well EUR s will plague evaluation of shale oil and gas reserves for the foreseeable future and deserves additional research. This paper utilizes public domain type curves realizing full well that they are probably erroneous but are nonetheless widely used throughout the oil and gas industry. Haynesville shale well breakeven gas price The Haynesville shale of east Texas and western Louisiana is overpressured ( psi/ft), deep (~11,000 feet) and produces primarily gas. Industry activity to date indicates a sweet spot centered in three parishes of northwest Louisiana (Figure 5). A type curve typical of 9

10 Combining the SEC and NYMEX Henry Hub gas price forecasts (Figure 7) with the two Haynesville type curves gives four economic scenarios for this play. The resulting cash flow values based upon a 10% discount factor, shown in Table 4, were all negative, prompting the those developed by independents (Petrohawk, 2010) for sweet spot wells is contrasted with that developed by Thompson, et al, (2010) for the Haynesville in general in Figure 6. Mathematical constants for the hyperbolic equations of the two type curves are collected in Table 2. First year decline for the sweet spot type curve is 82% while that of the generic Haynesville type curve is 89%. A minimum decline of 5% per year was imposed on both type curves here to prevent long tails. This study employed public domain economic information, augmented as necessary by reasonable assumptions. Haynesville well capital costs were 9.4 MM$ and question of breakeven gas price for a Haynesville well. Defining a breakeven price as that price which gives a zero cash flow (discounted at 10%) value and using the sweet spot type curve yields a breakeven gas price of $5.147/MMBtu. The January 1, 2012, NYMEX price forecast reaches this level in Table 2. Haynesville type curve parameters Ref: Petrohawk, 2010, and Thompson, 2010 parameter Petrohawk Thompson qi, mcfd 18,000 8,911 b Di, yr De min, %/yr yr EUR, bcf lease operating expenses were $2.17/mcf, which includes gathering and compression. The Haynesville production had a price differential of $1.081/MMBtu less than Henry Hub prices. These and other pertinent economic parameters are presented in Table 3. All economic cases considered in this paper assumed a working interest (WI) of 100%, a net revenue interest (NRI) of 82.5%, and an effective date of January 1, Table 4. Haynesville shale well PV10 s m$ sweet spot generic NYMEX -4,215-8,561 SEC -3,887-7,839 10

11 Bakken shale well cash flow, discounted at 10% (PV10) sensitivity to initial rate The Bakken play of western North Dakota, eastern Montana, and southern Saskatchewan is an oil play producing primarily from the middle member of the Bakken formation with some newer wells testing the stratigraphically lower Three Forks formation. This study utilized an industry type curve, the Brigham type curve, developed for oil production from the Bakken formation (Darbonne, 2011). Production behaviors and reservoir characteristics vary across the play and those used for this study, from western North Dakota, are not necessarily appropriate for other areas of the Bakken play. The two Bakken oil production type curves are shown in Figure 8 and the associated cumulatives in Figure 9. Mathematical constants for both Bakken type curves are listed in Table 5. Initial rates of both curves appear to be similar but at late times the generic curve rate is twice that of the Brigham curve. This apparent rate superiority vanishes when comparing the cumulative oil production shown in Figure 10. The slightly higher production rate of the Brigham curve leads to a 10-year cumulative production a third higher than that of the generic curve. Cumulative production of the generic type curve catches up to the Brigham cumulative only Table 5. Bakken type curve parameters Ref: O&GI, Aug 2010, and pub domain data parameter Brigham generic qi, bpd b Di, yr De min, %/yr yr EUR, mstb after 24 years on production. Initial rate of the Brigham type curve is 906 bpd and the first year decline rate is 72%. Initial rate of the generic type curve is 850 bpd and the first year decline rate is 83%. Economic value of for these wells depends on a good completion and high initial rates. These wells are typically completed with over 30 hydraulic fracture stimulation stages and are not without mechanical risk. The question arose about the sensitivity of PV10, as a measure of economic value, to initial oil production rate and completion efficiency. 11

12 Bakken economic parameters, primarily from public domain information, and augmented by prior experience, are collected in Table 6. Total well capital costs were 7.1 MM$ and operating costs were $10,000 for the first two months falling to $4,000 per month thereafter. To better understand the effect of initial rate on PV10, this work utilized two scenarios. The first scenario used the NYMEX January 1, 2012, oil price forecast and a 2% cost escalation factor. The second scenario employed the SEC December 31, 2011, price of $96.19 per barrel and no cost escalation. The SEC price is compared with the January 1, 2012, NYMEX price forecast in Figure 10. The resulting PV10 values and the fraction of PV10 realized in the first 10 years are summarized in Tables 7 and 8, respectively. The PV10 values, all positive, ranged from 10.7 to 12.3 MM$. Roughly three-fourths of the PV10 value in all four caseswas captured during the first decade of production. To investigate the effect of completion efficiency on PV10, additional economics were run with the Brigham type curve initial rate reduced to three quarters then half of the initial rate of 906 bpd. The resulting deterioration of PV10 value is illustrated in Figure 11. For both scenarios, halving the initial rate reduced PV10 by about 20% whereas halving the initial rate reduced the PV10 value by a factor of three. Marcellus shale well sensitivity to drilling delays The Marcellus shale play primarily encompasses Pennsylvania and New York and is gas prone with some reports of liquids in selected sweet spots. Very little public domain data exists for this play yet public opposition Table 7. Bakken PV10 summary, mm$ price forecast Brigham generic NYMEX SEC Table 8. Fraction of Bakken PV10 in first 10 years price forecast Brigham generic NYMEX 75% 74% SEC 75% 74% is mounting due to concerns about fracture stimulation fluids possibly contaminating shallow aquifers. Such concerns have prompted a ban on Marcellus well completions in New York State which may not be lifted for a number of years. The influence of drilling delays on Marcellus well economics was addressed by investigating changes in the PV10 values and payout times resulting from delays of one, two, and five years. An industry type curve for a Marcellus well (Anderson, 2011) is depicted in Figure 12. The initial gas rate is 2,600 Mcfd and the first year decline is 67%. These and other hyperbolic decline curve coefficients are collected in Table 9. Imposition of a 5% per year minimum decline led to a 50-year cumulative production of Bcf. Marcellus economic parameters used in this study are listed in Table 10 and include a total well cost of 5 MM$ and monthly operating costs of $2,000 per well per 12

13 delay increases the PV10 value with the five-year delay case increasing PV10 value about 30% over the base case PV10. This curious behavior is explained by the gas price, shown in Figure 7, rising much faster than well capital and operating costs, assumed to be 2% per year. Drilling delays also affect time to payout. As seen in Figure 14, a one-year drilling delay does not significantly increase time to payout, a two-year delay pushes payout month. For simplicity, this case used on the NYMEX December 31, 2011, pricing shown in Figure 7 and a cost escalation of 2% per year. Table 9. Marcellus type curve parameters Ref: Anderson, 2011 parameter value qi, mcfd 2,600 b 2.50 Di, yr De min, %/yr 5 50 yr EUR, bcf The PV10 values for a base case, 2012, Marcellus well and those associated with delays of one, two, and five years are shown in Figure 13. In all cases, a drilling back about a year, and a five-year delay results in a nineyear payout, an increase of just over 40%. Let s take a look at the economics. Note this analysis does not take account of lease costs which could be an important component of the economics of drilling delays. 13

14 Conclusions Exploitation of U. S. shales has grown rapidly over the last few years due to advances in horizontal well drilling and completion technologies. Projected oil and gas recoveries from shales are widely quoted in the media as reasons for significantly increasing domestic reserves. However, a closer inspection of shale well economics in three selected shale plays indicates modest to disappointing economics. Breakeven gas pricing for Haynesville wells was calculated to be $5.147/MMbtu, a price which the current (January 1, 2012) NYMEX Henry Hub gas price forecast reaches in Completion efficiency is critical for Bakken wells as halving the initial oil rate decreases PV10 by a factor of 3. Drilling delays in the Marcellus play could, if current NYMEX gas price forecasts are accurate, increase the PV10 value by a third but could also increase time to payout by just over 40%. Biography Leslie O Connor is President of MHA Petroleum Consultants, LLC, with over 30 years of petroleum experience. From 1997 to 2006, Ms. O Connor headed up the Denver operations for Sproule Associates Inc., another major world-wide consulting firm. Prior to this, she worked for 16 years at other petroleum consultancy and E & P firms, and gained experience in all basins in the U. S., the North Sea, China, Romania, and South America. John Seidle, a Vice President with MHA Petroleum Consultants in Denver, Colorado, has more than 30 years experience in coalbed methane and shale gas reservoir engineering. Author or co-author of more than 25 technical papers, six patents, and one monograph, he is also an instructor for industry courses in gas reservoir engineering. Acknowledgements Use of tax information provided by the SPEE website is gratefully acknowledged. References Anderson, Dave Fekete Production Data Analysis Manual. Berman, A. August 10, Lessons from the Barnett Shale suggest caution in other shale plays. php/2009/08/lessons-from-the-barnett-shale-suggest-cautionin-other-shale-plays.accessed December 12, Darbonne, Nissa In Pursuit of Bakken. Oil and Gas Investor. August, p. 73. EIA. April 5, World Shale Gas Resources: An Initial Assessment of 14 Regions Outside the United States. gov/analysis/studies/worldshalegas/. Accessed December 12, NY Times, August 24, Geologists Sharply Cut Estimate of Shale Gas. Accessed January 5, Petrohawk analyst presentation. May 24, Thompson, et al An Overview of Horizontal Well Completions in the Haynesville Shale. SPE Presented at the Canadian Society for Unconventional Gas conference, Calgary, Alberta, November 29-31,

15 This paper was originally presented at the SPEE Annual Meeting, Amelia Island Resort, Florida, June 6, 2011 Shale Engineering Rawdon Seager, SPEE Member, Gaffney, Cline & Associates Contributor: George Vassilellis, Gaffney, Cline & Associates Abstract With the significant emphasis by many companies on the development of shale gas, a need has arisen for a consistent approach to the evaluation of these reservoirs that empirically-based methods cannot accomplish. While undoubtedly useful, it is recognized that the use of type curves may lead to potentially misleading optimistic results especially if they are not constrained by volumetric estimates and an understanding of the physical processes involved. This paper presents an engineering approach (called shale engineering to reflect the underlying technical basis) that can be applied when sufficient data are available. The method involves the numerical treatment of key physical processes that are involved in hydrocarbon extraction from shale by addressing both the natural and the imposed rock attributes that result from massive stimulation. This is achieved by preparing two types of numerical simulation models: one describing the impact of the hydraulic fracturing process and creation of the stimulated rock volume (SRV), and another that combines the artificial and natural rock attributes to allow for prediction of flow performance in the long term. In this way, overall computing time can be kept reasonable, while maintaining the necessary level of detail where it is required. The method is concisely explained and an example of its use is presented. Introduction There has been a paradigm shift in the supply of natural gas in the United States following the boom in the location, assessment, development and production of shale gas 1. The EIA 2 recently estimated that there are a total of 862 Tscf of technically recoverable shale gas within the US (Figure 1). Figure 1 North American Shale Gas Plays 15

16 Similar formations are found throughout the world (Figure 2), although they are presently at a much earlier stage of evaluation. The highest potential is presently seen to be in China, Argentina and Mexico. Figure 2 EIA Map of 48 Major Shale Gas Basins in 32 Countries In the last three years, there has been in excess of US$70 billion of inward investment in shale gas and shale oil transactions in the US (Figure 3). Figure 3 US Shale Transactions The very significant growth during the past decade is illustrated in Figure 4. Note that production from the forerunner play, the Barnett, is at plateau and is expected to decline in the future. 16

17 Figure 4 US Historical Shale Gas Production Figure 5 shows the EIA s view of the contribution of shale gas to the total US supply for the next 25 years. By 2035 it is expected to provide nearly half of total gas consumed in the US. Figure 5 EIA Annual Energy Outlook

18 All this is presently taking place in an environment of historically low gas prices when considered in relation to the price of oil. On an equivalent basis, the prices of oil and gas (as represented by West Texas Intermediate and Henry Hub, respectively) have been relatively close (Figure 6). Figure 6 Historical US Oil and Gas Prices However, since around 2006, there has been a disconnect with the price of gas falling far behind that of oil. In mid-2011, gas was about one fifth the price of oil on an energy equivalent basis. Depending on the price outlook that one wishes to consider (for instance, Figure 7), this situation may be predicted to continue into the future for many years. It is therefore no wonder most shale gas participants are moving their activity away from relatively lean gas plays such as the Haynesville to richer plays such as the Eagle Ford and Marcellus. Figure 7 Henry Hub Gas Price Projections 18

19 The Challenge of Predicting Shale Well Performance When dealing with shale reservoirs, formation description requires special attention. Resource concentration may follow regional trends over significant areas while well performance is often influenced by natural fracturing (in addition to stimulation effectiveness) that may have more localized attributes. The placement of horizontal wells becomes an intricate exercise in balancing the targeting of zones of rich hydrocarbon concentration, optimum rock brittleness and the risk that hydraulic fractures could extend out of zone into possibly water-bearing reservoirs. This article does not venture into the aspects of sweet-spot selection, targeting and optimization, but rather focuses on key variables that can describe the physical and mechanical processes that drive production performance. In this quest, the objective was to provide a simple, practical, but yet relevant estimation process that is applicable to risk assessment, field development and performance optimization and that can be refined as more data become available. In this process, the gas and oil presence in these rocks is quantified by the summation of the volumes that occupy pores in the inorganic and organic matter. The former are similar, yet finer, than the pores in conventional reservoirs. The latter are associated with organic matter that is porous and also has the ability to release gas that is adsorbed on the rock surface. The volumetric estimation of shale gas and shale oil in place depends on specialty tools and core analysis that provide the necessary resolution to measure this fine porosity, fluid types and saturations. The level of petrophysical sophistication to achieve this task requires the detailed lithological description and measurements of organic matter attributes such as total organic content, kerogen type and maturity. It is also necessary to have a comprehensive understanding of the rock mechanics and the processes that lead to the creation of the stimulated rock volume (SRV) and the relation between its flow characteristics to the micro-seismic surveys that are often employed during the stimulation process. The objective of shale engineering is to describe both the mechanism that leads hydrocarbons from the rock to the fractures and the process that creates fractures to connect these hydrocarbons to the well. Because of these differences from conventional reservoirs, traditional reservoir engineering tools and concepts may not apply. For instance: The relation to in-place volumes is questionable The material balance approach does not apply Flow is not entirely related to pressure difference The effect of natural fractures is often misunderstood As mentioned above, decline analysis is uncertain One of the methods frequently applied to derive the estimated ultimate recovery (EUR) from wells is that developed by Arps in That approach is based on the empirical observation that flow rate declines with time from an initial rate, qi, based on a decline rate, D, and a decline exponent, b, as shown in Equation 1 below. The exponent, b, can vary from zero (exponential decline) through hyperbolic decline (0<b<1) to a value of b = 1 (harmonic decline). However, with shale reservoirs, an early fit to the production data can frequently be made using a b value close to 2 (which is equivalent to linear flow). Under these conditions, use of Arps equation can lead to a significant over-estimation of EUR. In theory, if the exponent b is equal to or greater than unity, the rate decline becomes asymptotic, which, absent any modifying control, leads to the impossibility of producing infinite volumes from a finite resource. The issue is illustrated conceptually in Figure 8. 19

20 Therefore, any production trend that can be described with a decline exponent greater than unity must be considered to be transitional and, as the flow regime changes, the value of b will tend to decrease until, ultimately, it is expected to be less than 1 (and perhaps to approach zero). Unfortunately, there is presently no known method to establish how the decline parameters will change with time or when it would be appropriate to impose a terminal decline rate (a common method used to try to avoid gross over-estimation of recovery). Depending on rock and stimulation characteristics it may take many years before boundary-dominated flow is achieved. Other researchers have proposed different decline schemes that appear in some cases to fit shale wells better, but up to this point there is no universally accepted decline formulation that can match short and long term trends and that is consistent with the physical processes of production. As illustrated by Fetkovich in 1973, the conventional decline schemes can be supported by simple physical models that relate to radial flow under fixed boundary conditions (i.e. constant drainage volume, constant permeability and constant bottom-hole pressure). In shale wells it will be difficult to demonstrate that all these conditions (except the constant bottomhole pressure) can be sufficiently maintained. What are some of the other issues relating to field development planning that cannot be easily addressed using decline curves? They do not involve flow-path constraints (such as surface operating pressures) It is difficult to incorporate changes in well drilling and completion design over time Geo-mechanical effects within the reservoir are ignored The dual contribution of hydrocarbons from inorganic and organic rock matter is not accounted for as a differentiating factor In other words when decline trends are analyzed and are summarized as type curves they may not be adjusted for variations in pressure, organic material, brittleness and natural fracture occurrence (to name just a few key variables) which may be known from regional studies. In order to capture the effect of such parameters on production, a large production dataset is required, which may not be available at the time when development decisions need to be made. Even when such studies have been conducted in mature plays (like the Barnett) it is recognized that such norms may not and should not be applied to other shale formations unless they can be shown to be truly analogous. As there is no systematic way to address these factors, long term predictions become questionable. Despite these drawbacks and challenges, the use of decline analysis is prevalent in the industry. Typically, operators will derive typecurves based on the performance of available production wells to form the link between the in-place resource and the desired projection of production performance. There are indeed other methods that can be applied, such as more advanced decline analysis and analytical models, but this paper will focus on an approach that seeks to model the performance of shale reservoirs using a specially-adapted numerical simulation work flow based on shale engineering that can replace the type-wells with a methodology that incorporates known shale properties and matches observed performance. It seeks to provide more confidence in the estimates by constraining the attributes of the stimulated reservoir volumes based on information from the fracturing process, such as treatment pressures and rates, proppant concentration. The shape of the SRV is guided by the shape of the microseismic surveys, recognizing that many of the recorded events may not be associated with the presence of proppant, or even fluid. (Figure 9) and may therefore be in parts of the reservoirs that is not in communication with the wellbore. 20

21 It is also recognized that the induced permeability throughout the SRV diminishes with distance and by depletion as dictated by a simplified geo-mechanical coupling between pressure and permeability. This coupling is applied both for pre-existing natural fractures and for the matrix. Variations in the fracture permeability with pressure are the most critical in the SRV propagation process, while fracture and matrix variation with pressure is important in the subsequent pressure depletion process. The starting point for fracture permeability is related to the fracture density observed in image logs and is the main tuning parameter to match maximum pressures achieved during stimulation. This leads to SRV extensions that are logically related to the pre-existing natural fracture occurrence and this concept is validated by the fact that this SRV description is close enough to match production observed by different fracture stages through production logging. It also seems that when a production prediction is made for that given SRV, the decline shape matches closely the observed decline shape. Another feature of the fracturing process is the creation of enhanced permeability within the reservoir, even in fractures that are not propped. This may be understood by reference to Figure 10. During the initial pressuring-up of the well and nearby rock ( loading ) there comes a point at which a quantitative change in permeability arises due to rock slippage before the fracture pressure is reached (the blue lines in Figure 10). Continued pumping results in fracturing the rock at the red point. Once pressure is released, the rock follows the orange path to the unstressed state, which is at a higher permeability that the starting value (impact of hysteresis). Unfortunately, there is presently no established method quantitatively to derive the effect of this hysteresis, but the shapes proposed by Moos and Barton (reference 5) can be adopted to direct laboratory tests and mini-frac tests that can measure these control points at different scales. At this stage, the application of such hysteresis curves is relatively crude, more intuitive than absolute, but it is not inconsistent with certain observations like the pressure at which shearing can initiate, the order of magnitude change in rock permeability that can be achieved by cracking a rock in mini-frac tests, the stiffness of the rock that relates to aperture opening after tensile fractures are initiated, and so on. Shale Engineering Models Description A typical shale reservoir and hydraulically-fractured horizontal well drilled to drain it can be conceptually described as shown in Figure 11. The matrix is essentially tight and fluid movement is governed by non-darcy flow and desorption. There are probably natural fractures leading to a dual porosity and dual permeability system. Immediately around the perforations will be the induced major fracture that will be propped, connected with an ill-defined network of smaller induced fractures of the entire SRV. Several numerical methods have been presented in the literature to provide a basis to perform numerical simulation and history matching as a tool to provide production forecasts. These attempts are based on a static view of the SRV by making a direct connection to micro-seismic surveys. In other words the SRV is ring-fenced by the micro-seismic events and is equally attributed Figure 10 Permeability Change Due to Fracturing Permeability, md Permeability. vs pressure Loading path Unloading path Theoretical loading curve Theoretical unloading curve Critical Pressure, psi Initial Pressure, psi Frac Pressure, psi ,000 10,000 15,000 20,000 25,000 Pore Pressure (psi) 21

22 by an enhanced permeability. Previous methods often employ simplified flow formulations that do not recognize the dual sourcing of hydrocarbons (inorganic and organic pores). Previous numerical methods commonly ignore geo-mechanical effects that lead to loss of flow conductivity with pressure depletion and somewhat overstate the contribution from areas outside the SRV, leading to an over-estimation of the spacing required for optimal extraction. In order to simulate this system in an efficient manner, the shale engineering approach is to build two separate geological and simulation models (Figure 12). Both deal with rock geo-mechanics through dilation-compaction tables and fluid compositional formulation (if required). The first model, Model A, deals with the creation of the SRV by modeling the fracture propagation process and provides the SRV permeability. The model is constructed with a fine grid using as detailed a description of the rock as is available and matched to the fracture stimulation surface rate and pressure data (Figure 13), micro-seismic data and flow-back behavior. Model B is intended to model short and long term performance of shale wells (whether vertical or horizontal) and is constructed from a coarser grid to capture the entire well drainage area incorporating all fracture stages. In addition to wellbore hydraulics, the model consists of four components, each of which contributes to well performance: 1. Matrix 2. Natural fractures 3. Minor induced fracture network (micro-slippage fractures with little or no proppant) 4. Major induced fracture network (fractures with proppant) In a recent case study for a well completed in the Berea and adjacent Devonian shale formations, two of the fracturing stages (#3 and #7) were modeled in detail and the remaining stages were interpolated. (A complete discussion of this evaluation is provided in Reference 1.) The relevant performance is shown in Figure

23 The results of the micro-seismic surveys taken during each of the nine fracture stages are shown in Figure 15. It is notable that most events are close to the packers, probably due to local stresses imposed by packer-setting causing weak points that favor fracture initiation. The other observation worth mentioning is that micro-seismic responses are not confined to the area of the formation adjacent to the open frac sleeve. For instance, during injection into stage 7 (green dots), micro-seismic events are seen also in areas corresponding to stages 6 and 4, but not 5, 8 or 9. This confirms the theory that such events are not necessarily associated with fluid placement, but may reflect rock-to-rock interaction. Figure 16 Model B Flow Match Figure 15 also shows the production rate contribution from each stage as recorded by a production log taken five months after production started. It is observed that most production originates from stages 5 through 9 while the most intense micro-seismic activity appears around stages 3 and 4. A review of the display of natural fractures based on openhole logs prior to stimulation suggests that these features play an important role in identifying productive intervals, perhaps more so than micro-seismic events. As noted above, after modeling stages 3 and 7 using model A, it was possible to derive appropriate models for the remaining stages in Model B. Figure 16 shows the overall match of the model to the PLT rates and to the total production history of the well. The goal is to narrow the range of uncertainty of the projected performance of the well (Figure 17). Figure 17 Model B Prediction Cases 23

24 Application of the Model Once both models are constructed and history-matched in this way, it becomes possible to investigate a number of sensitivities relating to well performance and recovery. For instance, variations in fracture design (fluids, proppant, stage spacing) can be tested. In the example given above, it was possible to run a number of predictive cases with different well spacing assumptions. It was shown (Figure 18) that reducing the well spacing from 120 acres to 90 acres resulted in a 30% increase in cumulative cash flow over 20 years, but further spacing reduction to 80 acres had a negligible economic impact. It is recognized that further development of this concept of shale engineering is warranted by applying it to a variety of different situations. In this way, it is hoped that it will be possible to evaluate alternative development options earlier in the life of a shale asset thereby providing operators with another tool to improve decision making. Conclusions 1. Shale Engineering provides a new look at shale performance a. Includes rock mechanics b. Models mass transfer through diffusion c. Describes the induced reservoir 2. Transverse fracture stages do not perform similarly a. Despite using similar design along a relative uniform formation b. Subtle differences in natural fractures and stress conditions may trigger differences in the induced reservoir (SRV) 3. Micro-seismic events may reflect rock-to-rock interaction a. Overstate spacing assumptions b. Un-interpreted micro-seismic plots do not relate to the induced reservoir (SRV) Biography Rawdon Seager is currently Principal Reservoir Engineering at GCA s western hemisphere headquarters in Houston, Texas. Rawdon s main responsibilities include internal quality assurance for technical projects and providing clients with advice regarding reserve and resource evaluation and reporting. He has also provided expert testimony at international arbitrations. Rawdon has spoken at various industry events as well as presenting in-house and public courses to clients on matters relating to reserve estimation, classification and reporting. Rawdon began his career in 1972 as a Petroleum Engineer with Shell International in Malaysia, Brunei, the Netherlands and Australia. In 1980 he joined Roy M. Huffington Inc. in Indonesia where he became Petroleum Engineering Manager, before joining GCA in 1985 with whom he has held senior positions in the United Kingdom, Singapore, Argentina, Venezuela and the USA. He has a BSc (Honors) in Physics from Bristol University, England and an MSc (Distinction) in Petroleum Reservoir Engineering from Imperial College, London. Rawdon is an active member of the Society of Petroleum Engineers (and current chairman of the Oil and Gas Reserves Committee), Society of Petroleum Evaluation Engineers, American Association of Petroleum Geologists, UK Energy Institute and is a Chartered Petroleum Engineer in the UK. He is also registered as a European Engineer with FEANI. 24

25 Footnotes 1. The term shale gas refers to gas located within reservoirs that have traditionally been considered to be shales. They have high content of clays, but also important amounts of sand or carbonate lithology References Vassilellis, G. D., C. Li, V.K. Bust/Gaffney, Cline & Associates; D. Moos, R. Cade/Baker Hughes Incorporated. Shale Engineering Application: The MAL-145 Project in West Virginia. SPE Vassilellis, G., C. Li, R.J.H. Seager/Gaffney, Cline & Associates; D. Moos/Geomechanics International. Investigating the Expected Long-Term Production Performance of Shale Reservoirs. Arps, J.J. Analysis of Decline Curves T.P. 1758, Petroleum Technology A.I.M.E. September Fetkovich, M.J. Decline Curve Analysis using Type Curves. SPE 4629, September 1973, Journal of Petroleum Technology pp , June Moos, D. and C.A. Barton. Modeling Uncertainty in the Permeability of Stress-Sensitive Fractures American Rock Mechanics Association (ARMA), San Francisco, CA, July 1-3, Acknowledgments The author wishes to thank his colleagues at Gaffney, Cline & Associates and Geomechanics International for the technical work underlying the concepts portrayed in this paper. In particular, George Vassilellis has been central in promoting a more rigorous approach to shale formation evaluation. Rawdon Seager, SPEE, SPE, AAPG, Energy Institute, Gaffney, Cline & Associates 1300 Post Oak Blvd., Ste 1000, Houston, Texas Tel.: (713) Fax: (713) rawdon.seager@gaffney-cline.com Address and contact details as above. george.vassilellis@gaffney-cline.com 25

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27 Use and Mis-use of the Graphical P/Z Plot Richard F. Krenek Netherland, Sewell & Associates, Inc. Issue One of the more common techniques in reservoir engineering is to attempt to reduce complex equations to simple graphical trends, linear trends where possible. The graphical material balance methodology, known as P/Z analysis, is an example of such a technique. While rigorous material balance work can be complicated and require specialized software, P/Z analysis is a greatly simplified method for determining gas-in-place and potentially recoverable gas volumes from a volumetric gas reservoir. Regardless of whether a reservoir is volumetric, valuable information can be derived from the construct of a P/Z plot. For example, a multi-well P/Z plot is very useful in helping to determine whether each of multiple aggregated wells are producing from the same connected reservoir. This plot is also useful in identifying baffles, or restricted-flow boundaries between wells. But the reservoir engineer must make a critical judgment as to whether the simplified P/Z approach to material balance provides valid quantitative answers to the questions of original gas-in-place and recoverable gas volumes, a question that centers on whether the reservoir in question is a volumetric reservoir. A key issue is whether a P/Z plot alone can be reliably used to prove that a reservoir is volumetric. Discussion In order to build a graphical P/Z plot, a series of shut-in (static) bottom hole pressure measurements from the reservoir in question at different points in time are needed. A minimum of two pressure points are required, more points are preferable. For each pressure point, the down-hole Z (compressibility factor) is calculated and the static pressure is divided by the associated Z yielding a P/Z value. The various P/Z values are plotted on the y-axis of a coordinate graph as a function of the cumulative production at the date of each P/Z value. If multiple wells are included in the plot (or if a multiple well reservoir is being modeled using pressure data from only a single well), the various P/Z values are plotted as a function of the total reservoir cumulative corresponding to each P/Z value. In a volumetric reservoir, the relationship should plot as a straight line with the y-axis intercept representing the initial P/Z value and the x-.axis intercept representing the initial gas-in-place. The quality of the result is only as good as the quality of the input data. Hence, having quality shut-in bottom hole pressure data are important. Lower permeability reservoirs require longer shut-in times to achieve accurate shut-in pressures. The amount of deviation of various points from the established line provides some indication of data quality. Accuracy issues can be magnified when extrapolating limited pressure data from immature reservoirs. The mathematical derivation of the graphical P/Z relationship is based on the assumption that the reservoir is volumetric. A volumetric gas reservoir, sometimes also called a purely pressure depletion reservoir, is defined as a reservoir that acts as a tank, a closed system with immoveable boundaries. No appreciable amounts of liquids or gasses enter or exit the gas-filled pore space of a volumetric reservoir, except via the well-bore(s). For a volumetric gas reservoir, the water influx, water production, and water/ formation expansion terms of the generalized gas material balance equation are deemed negligible and are eliminated. The remaining terms can be easily rearranged into a linear equation of y = mx+b form where y equals the P/Z value at a given cumulative production (the x-value) and the y-intercept (the b-value) equals the initial P/Z value at zero cumulative production. If a reservoir is truly volumetric such that no now water influx or water production occurs and water/formation expansion terms are negligible, then the P/Z relationship will plot as a straight line. 27

28 A P/Z vs. Cumulative Production Relationship is Linear for a Volumetric Gas Reservoir Because the above statement is fact and because reservoir engineers often look for straight lines in historical data to project future trends, it may be tempting to assume the corollary; that a straight line P/Z vs. cumulative production relationship necessarily proves that a reservoir is volumetric. But that assumption has never been proven. In fact, the corollary has been disproved in a number of reservoirs around the world. A number of published authors have commented on this fact: Gas material balance is supposed to be one of the simplest subjects in the whole of reservoir engineering and, indeed, the physics and mathematics are quite trivial. Yet there are great subtleties attached to its application which have perhaps not been stressed sufficiently in the literature and, if unappreciated, can lead the engineer into serious error in assessing the reservoir drive mechanism (water drive or volumetric depletion) and the GIIP [gas initially in place]. Foremost amongst the sources of error is the use of the traditional p/z versus cumulative production plot in attempting to history match the performance of gas reservoirs. It can lead to a complete misinterpretation of drive mechanism and a serious overestimation of the GIIP. (1) A common method of predicting gas reserves is the graphical solution to the gas material balance equation. A special case of the material balance equation is linear in p/z with cumulative gas production (Gp) which predicts the initial inplace gas when p/z is extrapolated to zero. Derivation of this form is based on the equation of state, corrected for compressibility (pv = znrt), and, particularly, on the reservoir being closed (no water encroachment). A straight line on the p/z chart results when these conditions hold. However, an apparent straight line on the chart does not assure that the reservoir is closed. This leads to the principal conclusion that it is dangerous to extrapolate p/z charts on a straight line without considering the possibility of water influx. (2) Analysis of Reservoir D-1 also provides the opportunity to show that straight-line p/z performance does not necessarily mean that a gas reservoir has a depletion drive. Theory showing that depletion-drive gas reservoirs will exhibit a straight-line p/z plot has been developed. The corollary-that a straight line p/z plot proves the existence of a depletion drive-has not been proven, although it is frequently assumed in practice. (3) Mr. Lloyd Cason s article describes observed pressure response with depletion in a weak water drive field called Duck Lake, located in South Louisiana. From 1967 until depletion in 1981, the gas production rate decreased continuously as the reservoir pressure declined and wells were lost because of water production. Cason presented a P/Z plot showing that, despite the aforementioned production-based indications of weak water drive, the P/Z plot formed a straight line through points representing about 45 percent of the true gas-inplace. These linear points extrapolated to show an apparent original gas-in-place (OGIP) value that was ultimately 25 percent too high. Cason says, Early-time straight-line p/z can persist until 40 to 50% of the OGIP is produced and can result in overestimating the reserve by 25 to 50% (pg ). Bruns and Fetkovich show that straight-line P/Z response can last virtually the entire life of the reservoir. Through their in-depth analysis of simulated P/Z curves for reservoirs with various analytical aquifers, Bruns and Fetkovich also provide interesting insight into the aquifer characteristics that can lead to straight-line P/Z relationships in nonvolumetric reservoirs. These aquifers generally tend to be weak and of limited extent. A linear P/Z vs. Cumulative Production relationship does NOT necessarily prove the gas reservoir to be volumetric and does NOT necessarily yield valid original gas-in-place or recoverable gas estimates. Because water encroachment artificially supports reservoir pressure, the graphical P/Z approach will likely overestimate the correct original gas-inplace and recoverable gas volume for a reservoir with water drive or water influx. And because abnormally pressured reservoirs can experience appreciable and disproportionate pore volume reduction through formation compressibility upon depletion from the initial abnormal pressure state to the normal pressure state (relative to pore volume reductions occurring after normal pressures are reached), the graphical P/Z approach will likely overestimate the correct original gasin-place and recoverable gas volume in an abnormally pressured reservoir. 28

29 Conclusion The graphical P/Z plot can be a valuable tool for determining the initial gas-in-place and the recoverable gas volumes for a reservoir if it is volumetric; having no appreciable water influx or water production and no appreciable formation compressibility affects. Taken alone, the existence of a straight-line trend on the P/Z plot should not be viewed as proof that the reservoir is volumetric and the resulting initial gas-in-place and recoverable gas volumes should be viewed as uncertain. Uncertainty rises if the reservoir is abnormally pressured since abnormally pressured reservoirs are known to exhibit more flat early-time P/Z behavior followed by steeper late-time behavior. Uncertainty also rises if the reservoir is among reservoirs known to exhibit water drive or water encroachment, even if weak. A good practice is to review both production and P/Z behavior of nearby-analogous reservoirs that are already depleted. If these analog reservoirs exhibit predictable P/Z behavior through depletion, then the likelihood increases that the subject reservoirs are volumetric. References Dake, L.P. The Practice of Reservoir Engineering, 1994, pg Bruns, J.R. and M. J. Fetkovich, SPE 898, pg , March Cason, Jr., Lloyd D. Journal of Petroleum Technology, pg October Biography Richard F. Krenek II is Vice President and Team Leader Reservoir Engineer for Netherland, Sewell & Associates, Inc. Rick has a B.S. in Chemical Engineering from the University of Oklahoma, Rick joined NSAI in 1990 after serving as a Senior Project Engineer with Exxon Company, U.S.A. Rick has performed reserve evaluations in all major producing basins in the U.S. and various international basins throughout Europe, Southeast Asia, and the Americas and has led a number of large and complex field studies and equity determinations. 29

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31 This paper was originally published in The APPEA Journal 2010 The Need For An Inferred Resources Category In Reserves And Resources Reporting In SPE PRMS Michael T. Scott Abstract Estimates of recoverable hydrocarbon volumes for projects are reported for a variety of reasons. The recipients of recoverable hydrocarbon reports are typically company directors and management, accountants, investors, investment analysts, financiers, media and regulators. The evaluation and veracity of recoverable hydrocarbon volumes is wholly dependent upon the skill and knowledge of experienced petroleum evaluators and supervisors. Due to the technically complex and varied nature of evaluation techniques, this aspect of recoverable hydrocarbon reporting cannot be prescriptive or regulated and is outside the scope of this paper. After a project has been evaluated and the recoverable hydrocarbon volumes estimated, evaluators typically categorise and report recoverable hydrocarbon volumes in terms of existence, maturity and estimate uncertainty. The category terminology used by the evaluators to report the volumes of recoverable hydrocarbons should ideally mean the same thing to all evaluators and recipients of the report. Unfortunately, this is frequently not the case - evaluators and recipients can use similar category terminology that has a different meaning, which leads to confusion as to what is actually being reported. This situation has come about due to the evolution of the SPE reserves standards and needs to be addressed so that the industry can report using terminology that is plainly understood by all. Prior to about 1980, volumes of recoverable hydrocarbons were mainly estimated using deterministic best estimate techniques. The deterministic reporting definitions at that time mainly considered the existence and maturity of recoverable hydrocarbons. When probabilistic techniques started to become popular after about 1980, these techniques enabled quantification of the range of estimate uncertainty and solutions were sought to integrate the reporting of these new techniques with the existing deterministic reporting definitions. To cater for both deterministic and probabilistic estimate reporting, the Society of Petroleum Engineers (SPE), in consultation with other oil and gas societies, introduced probabilistic P90, P50 and P10 terminology and established a relationship between these new terms and the existing deterministic Proved, Probable and Possible terminology. The most recent standard published by SPE is known as the SPE/AAPG/WPC/SPEE Petroleum Resources Management System (SPE PRMS, 2007). However, the current standard has retained a discontinuity between the deterministic and probabilistic definitions, which was introduced when the probabilistic terminology was first equated to the deterministic definitions. The discontinuity has occurred because deterministic techniques were originally focused on the existence and maturity of estimates whereas probabilistic techniques are focused on the uncertainty associated with estimates - two very different concepts. The discontinuity manifests itself in two ways: 1. When deterministic and probabilistic Proved, Probable and Possible estimates are compared they will usually not be equal - both numerically and in physical meaning. 2. Probabilistic estimating techniques have lost the practical use of the deterministic Possible category, which was originally intended to describe recoverable hydrocarbon volumes that were inferred to exist but had not yet been penetrated by a well. The resolution of these two issues requires stepping back to the original (1955) deterministic definitions and then correctly applying uncertainty techniques to those definitions. The result is a system that aligns deterministic and probabilistic techniques and enables the deterministic Proved, Probable and Possible terminology to continue in the form that it was originally intended and is still being used today. The most important benefit however would be that all industry stakeholders would once again be aligned in their understanding of the definitions thereby demonstrating that the industry can effectively regulate itself. 31

32 A Short Classification History In 1944, at a Sun Oil divisional meeting in the USA, Frederic H. Lahee (Lahee, 1944) reported that he had been considering for several years the need for more precise definition of terms used in connection with the statistics of wildcatting and crude oil reserves. Lahee recognized that comparisons and additions of reserves were being made where the maturity and existence of the volumes were substantially different. At this meeting Lahee promoted the idea of classifying recoverable oil according to several categories (Figure 1). The system considered the proof of existence and the maturity of petroleum reserves. The concept of uncertainty in estimation was not typically being considered in this era due to calculations being undertaken manually. A. Proved Reserves 1. Drilled. 2. Undrilled. B. Probable Reserves 1. Undeveloped part of a pool. 2. Secondary recovery. 3. Behind pipe. C. Possible Reserves 1. In productive areas. (a) Shallower or deeper pools within fields limited. (b) Pools outside fields limits. 2. Areas not now producing but which are geologically similar to producing areas in the region. D. Hypothetical Reserves From non-producing regions underlain by sediments that produce in other areas. Figure 2. Lahee 1955 Classification. In 1950 Lahee (Lahee, 1950) again reported to his peers, Too often different persons use the same word with very different meanings, and he again proposed the definitions that were originally promoted in By 1955 Lahee (Lahee, 1955) had refined his original 1944 concept to a system that will trigger recognition from most readers (Figure 2). The main update in 1955 was that the Possible Discovered reserves category had evolved into the Probable reserves category. The 1955 system cemented the terms Proved, Probable and Possible into industry terminology. The 1955 Lahee system continued to be a maturity and existence classification system and appeared to be driven by the grid pattern drilling that is typically undertaken onshore USA. The deterministic system as Lahee proposed is still in use today especially in the USA where deterministic (Proved, Proved Undeveloped (PUDs) and Probable) terminology are commonly used. An important note is that Lahee used the term reserves to simply mean recoverable hydrocarbons. The term reserves was not specifically defined, which avoids the need for the adoption of additional defined terms such as resources. Fredrick Lahee is undoubtedly the father of modern reserves reporting. Lahee initially recognised the need for a standard system where recoverable hydrocarbons could be classified and compared according to existence and maturity criteria and then, over his professional lifetime, developed and refined the principles of such a system. In 1956 J.J. Arps (Arps, 1956) discussed the concept of uncertainty in reserve estimates and noted that it is desirable for the engineer making a reserve estimate to provide his client or his management with the possible spread in estimated ultimate recovery, both high and low, or, if he provides them with a single figure, to point out the probable error in his estimate. Later in 1962, Arps also provided a classification system that split the earlier Lahee classification system into primary and secondary recovery categories. This primary and secondary recovery reporting split never gained any traction in the industry. In 1974 V.E. McKelvey (McKelvey, 1974) presented to the U.S. Geological Survey a Total Resources classification system. This system introduced the reserves and resources terminology to the industry reserves being discovered and economic and resources being undiscovered and/or uneconomic (Figure 3). McKelvey also noted that the addition of developed measured and undeveloped measured categories would 32

33 Figure 3. McKelvey 1974 Classification. be a useful update to the matrix. It is possible that McKelvey was trying to align these terms with Proved Developed and Proved Undeveloped whilst also introducing an element of maturity to a system that only considers existence and economic viability. The McKelvey system appears to be mainly intended for use in the minerals industry but interestingly, in the original paper, McKelvey also makes reference to petroleum accumulations - possibly hinting that this system should be adopted for that industry as well, the terms measured, indicated and inferred being easily interchangeable with Proved, Probable and Possible. It is also worthy to note that the McKelvey system devotes two thirds of the categories to subeconomic situations, which seems not overly useful. In 2004 the United Nations (UN, 2004) introduced their Framework Classification (UNFC) for Energy and Mineral Resources. The UNFC is a complex three dimensional matrix that classifies remaining resources according to socioeconomic, feasibility and geological aspects of accumulations. The UNFC states that it is a universally applicable scheme but the three dimensional nature of the matrix and the alpha-numeric coding makes it hard to use and understand. The UNFC greatly expanded the maturity definitions with a number of sub Total Petroleum In-Place Discovered Undiscovered Commercial Uncommercial Reserves Contingent Resources Prospective Resources Figure 4. SPE PRMS classes that track the phases of a project from exploration through to production. The Society of Petroleum Engineers (SPE) through the years has adopted various parts of the prevailing standards the terms Proved, Probable and Possible as defined by Lahee being the most well known. Around 1980, as computer power increased and became available to the general public, probabilistic uncertainty estimating techniques were being adopted by professional estimators. To cater for these techniques, SPE adopted the cumulative probability P90, P50, P10 terminology and equated this to the 1P (Proved), 2P (Proved plus Probable) and 3P (Proved plus Probable plus Possible) categories. SPE updated their standard several times with the last major update in 2007 when the SPE/AAPG/WPC/ Production (Produced) On Production Undeveloped (but expected to be within a reasonable timeframe) Development Pending Unrecoverable Prospect Unrecoverable Approved for development Justified for development Development unclarified or on hold Development unviable Lead Play Developed Producing Non-Producing SPEE Petroleum Resource Management System (PRMS) was introduced. This system expanded project maturity (as per UNFC) and adopted an uneconomic category (as per McKelvey). Figure 4 details the SPE PRMS 2007 classification matrix. Possibly in an effort to include terminology covered by a number of other systems, SPE PRMS 2007 expanded its matrix to include Total Petroleum In Place, Unrecoverable hydrocarbons and a category for uneconomic recoverable hydrocarbons (Contingent Resources Development unviable). P90 90% cum. prob. (small number) 1P Proved 2P Proved + Probable 3P Proved + Probable + Possible 1C 2C 3C Low Estimat e P50 50% cum. prob. (middle number) Best Estimat e P10 10% cum. prob. (large number) High Estimat e 33

34 For completeness it should be noted that, in parallel to the systems noted above, the Former Soviet Union, China, Norway, Canada and other countries have also evolved systems, which all have positives and negatives when compared to the various standards that exist today. In addition, in the USA the Securities and Exchange Commission (SEC) requires USA listed oil and gas companies to report Proved reserves to standards defined by the SEC, which are different to the SPE definitions. There is no best system. The Nub of the Problem The western world continues to use Proved, Probable and Possible terminology but due to the evolution of the classification systems there are two subtle problems, which may even be preventing the current SPE PRMS 2007 standard from gaining universal acceptance: 1. The deterministic Proved, Probable and Possible categories do not mean the same as the probabilistic Proven, Probable and Possible categories - even though they appear to be defined to be equivalent (the equivalence discontinuity). 2. The probabilistic Possible category has been defined as an incremental part of the uncertainty estimation range and it no longer reflects what was originally intended in the deterministic definitions (the missing inferred category). The Equivalency Discontinuity The easiest way to explain the estimating discontinuity between deterministic and probabilistic techniques is to go back to the Lahee definitions of 1955 (Figures 2 and 5). 5. As can be seen from Figure 5, for a discovered accumulation in the original Lahee definitions, Proved was defined as what was in proximity to well penetrations and was split into developed and undeveloped regions. Probable was defined as the rest of the continuous accumulation outside the Proved area. In the original definitions Possible was outside penetrated areas i.e. the accumulation had not yet been drilled but was thought to have a reasonable chance of existing due to its proximity to the discovered accumulation. In reality Possible is an undiscovered estimate but the accumulation under consideration was expected to have a higher chance of existing than an exploration target. When probabilistic estimating techniques started to become commonplace around 1980, SPE adapted their prevailing standard by defining Proved as P90, Proved plus Probable as P50 and Proved plus Probable plus Possible as P10 - for a discovered accumulation. The implication being that, on a cumulative probability basis for a discovered accumulation, Probable is equal to P50 minus P90 and Possible is equal to P10 minus P50. This was the point when the discontinuity was introduced into the system. The Lahee (or deterministic) Proved, Proved plus Probable and Proved plus Probable plus Possible had nothing to do with P90, P50 and P10 on a cumulative probability basis for a discovered accumulation. In fact, Proved and Probable were the only terms associated with a discovered accumulation and Possible was defined as being outside of the discovered accumulation. Although it could be argued that there may be a very poor correlation between deterministic Proved and probabilistic P90 when an accumulation is penetrated by a low number of wells, as well numbers increase the deterministic Proved should start to approach the probabilistic P50. To explain this effect further: as well numbers increase for an accumulation the magnitude of the deterministic Proved will increase and, correspondingly, the magnitude of deterministic Probable will decrease. The sum of deterministic Proved and Probable will therefore theoretically be held at a steady value, which would be equivalent to the probabilistic P50 value for the whole accumulation. When an accumulation is fully drilled the deterministic Probable would be 34

35 zero and the deterministic Proved would be at its maximum value and would therefore be equal to the probabilistic P50 estimate. This apparent equivalence between the deterministic Proved plus Probable estimate and the probabilistic P50 estimate for the whole accumulation may explain the reason why the discontinuity has come about - if this point is viewed in isolation then the system seems to work - but unfortunately this is the only point where it does work. To reinforce the understanding of the differences between deterministic and probabilistic techniques for a discovered accumulation: Deterministic Proved will most likely be a different estimate to probabilistic P90. Deterministic Proved is a well conformable estimate and probabilistic P90 is an estimate based on a cumulative probability level for the whole accumulation. Deterministic Probable will most likely be a different estimate to probabilistic Probable (defined as P50 minus P90). Deterministic Probable is the whole accumulation volume minus the well conformable volume whereas probabilistic Probable is the P50 cumulative probability estimate minus the P90 cumulative probability estimate for the whole accumulation. Deterministic Possible is usually outside the continuous accumulation and will therefore not be equivalent to probabilistic Possible (defined as the P10 cumulative probability estimate minus the P50 cumulative probability estimate for the whole accumulation). As can be seen, differences in estimates using deterministic and probabilistic techniques are not only due to the different numerical techniques being used but also because the techniques have fundamentally different meanings. The implication of this discontinuity is that there is a potential situation for an oil field where the reported Proved estimate for a company using deterministic estimating techniques could be substantially and materially different to the reported P90 (also called Proved) estimate for a company that is using probabilistic estimating techniques. The use of the same terminology being used for two different techniques is an unacceptable condition for all stakeholders. Not only will stakeholders be confused at the differences in the magnitude of the estimates for the same terms but it sends a message to stakeholders that the industry has difficulty in regulating itself - or in the worst case, the industry is untrustworthy. The Missing Inferred Category Lahee (1955) defined a system with a Possible category that inferred that reserves (or recoverable hydrocarbons if you prefer) could exist nearby to a discovered accumulation. These inferred reserves were not considered to be discovered or undiscovered but were considered to have a reasonable chance of existing. It was a grey area between the two end-points of discovered and undiscovered. In most circumstances today the majority of recoverable hydrocarbon estimates would simply be classified as discovered or undiscovered. There are some circumstances, however, where the application of modern technology may drive the industry to determine a need for a deterministic Possible or inferred category. As an example of where this could be applied: consider a gas prospect that is made up of two fault blocks (Figure 6). Each of the fault blocks demonstrate a seismic direct hydrocarbon indicator but that indicator cannot be used to determine if the gas is in communication across the fault. A single well is drilled into the first fault block and gas is discovered. If the company that discovered the accumulation uses probabilistic techniques then the company will report P90 (Proved), P50 (Proved plus Probable) and P10 (Proved plus Probable plus Possible) estimates of the volumes that are expected to be recovered from that fault-block. If the company that discovered the accumulation uses deterministic techniques then the company will make a best estimate of Proved and a best estimate of Probable for the discovered fault block, which will most likely be different to the estimates reported by the company using probabilistic techniques. The equivalence discontinuity problem raises its head again - one system, one terminology and two different techniques resulting in different magnitudes of estimates for the same terms - and the company using deterministic techniques has yet to define and report the Possible recoverable hydrocarbons. The question now is: how is the other fault block to 35

36 be handled? The company using probabilistic techniques has three choices: 1. The company can assume that the other fault block is undiscovered (direct hydrocarbon indicator is not used as positive proof of existence), thereby either correctly estimating or underestimating the estimates of recoverable hydrocarbons, or; 2. The company can assume that the fault block is discovered (direct hydrocarbon indicator is used as positive proof of existence), thereby correctly estimating or over-inflating the estimates of recoverable hydrocarbons, or; 3. The company can apply some risked technique, and nobody has any idea what the reported volumes practically mean. As can be seen from the choices above, there are too many options for handling of the yet to be penetrated fault block. However, the company using deterministic techniques places the other fault block into the Possible. reporting category and reports a best estimate of what it may contain. The deterministic technique provides a much simpler and elegant solution to the problem than the probabilistic technique. Figure 6 demonstrates the differences between the systems and shows how the deterministic Possible category, as it was originally intended to be used, has effectively disappeared from probabilistic estimating techniques. Solving The Equivalence Discontinuity And Missing Inferred Category Accepting that it would be beneficial to have the deterministic and probabilistic techniques aligned, there are two practical ways to solve the equivalence discontinuity and missing inferred category problem: 1. Go back to the original deterministic definitions and apply the probabilistic uncertainty techniques to them; or 2. Accept the current probabilistic definitions and redefine the meaning of the deterministic Proved, Probable and Possible definitions. Due to the evolution of the definitions and the entrenched understanding of the deterministic terminology it would be almost impossible to practically implement the redefinition of the deterministic terminology. The only practical solution therefore lies in applying uncertainty techniques correctly to the deterministic definitions. When the estimation of uncertainty (by cumulative probability techniques) started to become commonly used, the designers of the reserves standards should have applied uncertainty to the existence considerations that the prevailing standards had defined. As an example, a single well or a number of wells in an accumulation can measure (or prove) a certain volume. The uncertainty associated with the estimate of hydrocarbon recovery from that Proved volume can be reported as the low, medium (or best) and high recoverable hydrocarbon estimates. Similarly, the volume that is outside what is considered to have been measured (or Proved) is classified as Probable (as per the original deterministic definitions) and the low, medium (or best) and high recoverable hydrocarbon uncertainty estimates for this category can be reported. Possible always remains outside the continuous accumulation that has been penetrated and the low, medium (or best) and high recoverable hydrocarbon uncertainty estimates for this category can be reported. Applying uncertainty to the deterministic existence categories immediately solves the discontinuity between the deterministic and probabilistic systems. The construction of this system is shown in Figure 7. A key change in the definitions is that P90, P50 and P10 no longer means Proved, Proved plus Probable or Proved plus Probable plus Possible. Proved, Probable and Possible revert back to their original deterministic definitions and these categories have uncertainty, which is reported at the P90, P50 and P10 cumulative probability levels. 36

37 The abbreviated terminology of 1P is still be defined as Proved, 2P is Proved plus Probable and 3P is Proved plus Probable plus Possible. The Future To put in place a classification system that can be practically understood and used by all stakeholders, three further areas need to be clarified. 1. Maturity Of Hydrocarbons: Stakeholders should be informed if a recoverable hydrocarbon estimate is developed or undeveloped because this provides further information on the risk associated with the estimate and the immediacy of expenditures or revenue. The UNFC and SPE PRMS 2007 have split this facet of classification into a number of categories and although it may be academically correct to break down the process into many discrete steps it does not add any practical value to the system. There are only two categories of interest to the majority of stakeholders: Developed Producing and Undeveloped. A company is obviously free to elaborate at great lengths in its reports on exactly where in the process a project may be but the key question is: is it developed and producing or is it yet to be developed? 2. Permit Life End Consideration And Uncontracted Hydrocarbons: The handling of recoverable hydrocarbons after contract life end needs to be considered. The Author proposes that unless a company is in receipt of a contract extension letter from the relevant authority then all recoverable hydrocarbons post the end of the current contract period should only be inferred at best and therefore these should be reported in the Possible category. It is misleading to report these volumes of hydrocarbons as Proved or Probable reserves due to the inherent uncertainty of ownership associated with these volumes - especially in areas where the rule of law and transparency is less than certain. Uncontracted hydrocarbons are difficult to handle in all systems due to the uncertainty of whether or not the volumes will be sold in the future - oil less so, gas more so. It is suggested that uncontracted recoverable hydrocarbons are catered for in either the Probable or Possible categories, depending on how confident the company is in securing a future contract for those volumes of hydrocarbons. 3. Uneconomic Recoverable Hydrocarbons: Stakeholders will need to decide if they wish to include uneconomic recoverable hydrocarbons in the classification system. The Author proposes that for almost all commercial applications the reporting of uneconomic recoverable hydrocarbons should not be a consideration. National oil and gas companies however may have a requirement to report technical ultimate recovery, which lies beyond the economic cut-off point. The Author suggests that this category lies to the right of the Possible category that was shown in Figure 7 and is a category that in most instances should be ignored. 4. Exploration: Exploration, as also catered for by Lahee as hypothetical reserves, needs to be split out because it is not covered by the Proved, Probable or Possible terminology. Estimates of recoverable hydrocarbon volumes for exploration can be sub-divided into targeted (the discovered equivalent of Proved plus Probable) and adjacent (the equivalent of Possible). Splitting exploration recoverable hydrocarbon volumes further into prospect and lead categories adds limited practical value. Companies should ideally in their reports however give an indication of when they expect to drill the exploration target thereby introducing a time factor into the reported estimate. Figure 8 shows the simplified matrix with the above considerations included. The most important category in Figure 8 is the P50 or Best estimate Proved Producing category. This category underpins company revenue, firm future company value and is used for depreciation for accounting purposes. All other categories have much higher risk considerations. 37

38 It is the Author s hope that this paper takes us a step closer. References Arps, J.J Estimation of Primary Oil Reserves. AIME Petroleum Transactions, Feb Lahee, Frederic H Standarization in Compiling Data on Exploratory Drilling And Crude-Oil Reserves. Presented at Sun Oil Co. spring meeting, Southwestern District, Division of Production, Houston, Texas, June 13-14, 1944, API Lahee, Frederic H Oil and Gas Reserves: Their Meaning and Limitations. Frederic H. Lahee, Presented at Sun Oil Co. spring meeting, Southwestern District, Division of Production, Houston, Texas, March 1950, API Summary In the Author s view, the reporting standards and definitions took a wrong turn around 1980 when uncertainty techniques were erroneously applied to the prevailing deterministic definitions. Not only are the deterministic and probabilistic definitions different in fundamental meaning but the deterministic Possible category, which allowed for the reporting of inferred recoverable hydrocarbons, was essentially removed from the probabilistic system. With the entrenched understanding of the deterministic Proved, Probable and Possible terminology, the introduced misalignment between the deterministic and probabilistic techniques can only practically be solved by taking a step back to the original deterministic definitions and then applying probabilistic uncertainty techniques to those definitions. The aligning of the Proven and Probable categories so that the deterministic and probabilistic definitions have the same meaning and the re-introduction of an inferred category meaning the same as the historical deterministic Possible category would go a long way to securing a universally acceptable standard. Due to the current state of the definitions it is apparent that the definitions are not well understood. All stakeholders in the industry need a standard that is simple to understand and is consistent in its terminology. Congress Lahee, Frederic H The Terminology of Petroleum Reserves. 4th World Petroleum McKelvey, V.E., Concepts of Reserves and Resources. USGS. December 30, UN, United Nations Framework Classification (UNFC) for Energy and Mineral Resources SPE PRMS, Latest Society of Petroleum Engineers Reserves and Reporting Standard. Available from Substantial additional material has been reviewed to develop and refine the concepts and prepare this paper but this material has not been specifically referred to in the text. Biography Michael Scott has a Batchelor of Science (First Class Honours) degree in Civil Engineering from Strathclyde University in Glasgow, Scotland, and a Master of Engineering degree in Petroleum Engineering from Heriot-Watt University, Edinburgh, Scotland. He is a petroleum reservoir engineer by profession and during his 25-year career he has worked for Esso UK, Texaco UK, Esso Australia, Woodside Energy and Cooper Energy. During his career Mr. Scott has been fortunate to gain experience and knowledge in a large number of countries working across new ventures, exploration, developments, production and downstream operations. Michael Scott Cooper Energy Limited Suite 4, Level 4, South Shore Centre The Esplanade, South Perth, 6151 Western Australia, Australia admin@cooperenergy.com.au 38

39 Key Issues in Determining Discount Rates for Valuing Real Property Hal Heaton, PhD Marriott School of Management Brigham Young University Introduction Many appraisers are startled when they talk with actual buyers of real properties and determine the discount rates that actual buyers and sellers of real properties use. Buyers and sellers use discount rates that are sometimes substantially higher than those derived using standard weighted average cost of capital (WACC) calculations. When typical WACC calculations are done, appraisers often use data from traded securities such as stocks or bonds to determine the required returns for equity and debt investors. However, when actual transactions are analyzed, the implied discount rates are often dramatically higher than those calculated from stocks and bonds. Why is there a difference? And which rate is most appropriate? This study will discuss two reasons why the difference may exist. One key concept lies in the subtle, but critical difference between the definition of expected cash flows and what I will refer to as most likely cash flows. Another key concept, which has had rising impact in the last few years, is the issue of liquidity. In this study I will first discuss the logic and usual process for estimating the cost of capital. Next I will define and illustrate the critical difference between most likely versus expected cash flows. Finally, I will then address the issue of differences between discount rates calculated from stocks and bonds versus those determined from actual property transactions to emphasize the critical difference between required rates of return on highly liquid securities and highly illiquid properties. Estimating the Weighted Average Cost of Capital One of the common approaches used to value a property is the income approach in which the appraiser forecasts the future expected cash flows from the property and then discounts them to present value. The cash flows are discounted at a rate which reflects the risk of the cash flows and is often referred to as the cost of capital for the property. The cost of capital reflects the return that investors could obtain on other investments of similar risk and characteristics; a buyer of the property must offer the same return to investors in order to raise the capital to buy the subject property. The standard textbook method to estimate a company s cost of capital is to find companies with traded securities which are in the same business from which cost of capital data can be estimated. Fair market value requires the rate that a typical buyer and typical seller would require for the risk of the cash flows used to value the property. Financing takes two general forms: debt and equity. Although there are other types of financing such as preferred stock, convertible debt and others, most of these can be closely modeled as a combination of common stock and debt. The appraiser will determine the appropriate mix of debt and equity for financing the business in the long term and assume that whatever form of security is actually sold, in the long run the mix will approximate the debt/equity mix selected. The cost of capital will reflect the weighted average of these two sources, i.e. Weighted Average Cost of Capital (WACC) = w d k d (1-T) + w e k e (Equation 1) 39

40 where w d is the percent of financing raised in the form of debt, k d is the long term cost of debt, T is the tax rate paid by the borrower, w e is the percent of financing raised in the form of equity, and k e is the cost of equity. Hence, to estimate the cost of capital, an appraiser must look at the amount and cost of debt available to a buyer and then the cost of equity, given the level of debt. To determine the cost of debt, appraisers will often estimate what credit rating the property could obtain at the level of debt assumed and then obtain average yields on debt of that credit rating from standard published sources. This data comes from the prices of traded debt securities. It is important to note that, when determining the cost of capital for a property, an appraiser must look solely at the debt capacity of the property on a stand-alone basis. The appraiser must not impute the credit rating of a larger, more creditworthy buyer who has other assets to act as collateral for the debt and attribute that debt capacity to the acquired property. The amount of debt (as a percent of value) that a large, diversified company can obtain may be substantially higher than the amount of debt that a buyer could obtain using only a single, standalone property as collateral. This fact may explain part of the difference between the textbook WACC calculation and the return that actual buyers and sellers of a property may require - appraisers may have assumed the percent debt that an actual property can obtain is the same as the large, diversified companies from which the data was obtained. To determine the cost of equity, appraisers often look at the traded stock prices of companies in the same business as the property being appraised and measure the risk of those stocks. Perhaps the most commonly used approach is the Capital Asset Pricing Model (CAPM). The CAPM is developed from a macroeconomic analysis which demonstrates that, in efficient markets, investments will offer a return measured by the equation: Required Return = R f + ß(R m - R f ) (Equation 2) where R f is the risk free rate, R m is the market expected return and ß (beta) is the measure of systematic risk and incorporates both the volatility of the investment and the correlation of the investment with the market. The data used to calculate beta as well as the risk free rate and the expected return on the market in Equation 2 almost always come from the traded stock securities of the companies selected as comparable to the subject property. What Is Expected Cash Flow? The first explanation of why there may be a sizable difference between the standard WACC calculated as explained above and the required return that buyers and sellers use on an actual property lies in the nature of the cash flow being discounted. Finance texts emphasize that the cash flow to be discounted is expected cash flow. However, many people do not understand what expected actually means. Expected has a precise mathematical definition: the mean of the probability distribution. In short, the expected cash flow is the average cash flow that would be observed if the investment could be repeated many, many times. This is often very different than most likely cash flow. For example, suppose the engineers working on a project, forecast that if everything goes as planned, $1000 in cash flow will result. Say this happens with 90% probability. However, 10% of the time something goes wrong and the cash flows are zero. Expected cash flow is the scenario weighted average cash flow if the investment could be repeated many, many times. For this example, expected cash flow would be: 90% x $ % x $0 = $900 ( Equation 3) The expected cash flow ($900) must be discounted in the valuation process, not the most likely cash flow ($1000). The confusion arises because the owners of the property often refer to the $1000 as the expected cash flow since they are ignoring the low-probability 10% event. But $1000 is actually the most likely cash flow, not expected cash flow. The risk of shutdown or other factors affecting cash flows should be accounted for by forecasting all possible scenarios, weighting the scenarios by their probability and then computing the average or expected cash flow as in Equation 3. Unfortunately, buyers and sellers often rely on a single forecast of cash flows and often this single forecast is the most likely cash flows not true expected cash flows that valuation requires. Of course, buyers and sellers 40

41 know that negative scenarios can happen, but these negative scenarios may not be specifically reflected in the cash flows since they may not be very likely to happen. However, buyers and sellers adjust for this fact by using higher discount rates than would otherwise be required. Adjusting Discount Rates for Most Likely Cash Flows The best way to deal with this crucial difference between expected and most likely cash flows is to forecast various scenarios and then weight each scenario by its probability as in Equation 3. The standard weighted average cost of calculation is only appropriate if this is the case. However, most appraisers use only a single cash flow forecast and ignore what the cash flows for possible negative scenarios. As a result, the single cash flow often reflects the error of using most likely cash flows rather than expected cash flows. If such is the case, then the adjustment to the discount rate must reflect the magnitude of the difference between most likely and the truly expected cash flows. Using the example above, suppose the cash flow is a perpetuity. To value perpetuity correctly, the expected cash flow of $900 should be discounted at the WACC. If the WACC is 10%, then the true value is:.. $900/0.10 = $9,000 However, if the appraiser makes the mistake of misusing $1000 as the expected cash flow then a discount rate of 11.1% must be used to obtain the correct value:.. $1000/0.111 = $9,000 Experienced buyers and sellers of similar properties will often know from prior experience that to come out whole with a portfolio of projects, 11.1% must be used and they will indeed use that higher rate of 11.1%. Appraisers who naively calculate a weighted average cost of capital of the 10% used in the example are stunned to observe actual buyers and sellers using 11.1% for valuing the $1000 cash flow. The failure to recognize the critical difference between expected and most likely cash flows explains their astonishment. The necessary adjustment to the naïve WACC gets larger as the time horizon of the project gets shorter. For example, if the project above with a $900 expected cash flow is only a one period project instead of a perpetuity, then the true value is: $900 / 1.10 = $818 The appropriate discount rate to use if the most likely rather than the expected cash flow is used is over 20%! $1000 / = $818 This is, needless to say, much higher than the 11.1% for an infinite-lived project. Most practitioners deal with projects that have similar lives and so the discount rate they use will reflect the average life of the project. Unfortunately there is no other way to estimate the appropriate discount rate than to know from experience how much of an adjustment is required to compensate for the use of most likely and expected cash flows. This adjustment may be very different for a refinery than it is, say, for an oil drilling project. Difference in Required Returns on Securities and Required Returns on Properties Another frequent explanation of the difference between discount rates calculated with the standard textbook procedure and the actual rates that buyers and sellers really use stems from the difference in securitytype investments and physical properties. Consider two investments with identical risk and cash flows. One can be bought and sold simply by clicking on an icon on your computer and mere seconds later cash appears in your account ready to be used for any other purpose you choose. The second investment can only be sold by searching for a buyer, negotiating a price, hiring lawyers to write up a contract, accountants to review accounting records, and specialists to perform due diligence. Which would you rather own? Overwhelmingly investors choose the easily traded investment. Liquidity refers to the ease and cost of buying or selling an investment. Investors will require a higher return on assets of similar risk if the assets are more difficult to sell, require higher transaction fees to buy or sell, or cannot be bought or sold in small pieces (like common shares) as cash needs arise. Appraisers who attempt to value operating property such as a refinery, a railroad, a telecommunications network or other operating property must obtain data. Often, the data is obtained from the traded securities (stocks and bonds) of refinery, railroad or telecommunications companies which own property 41

42 as similar to the subject operating property as possible. However this data derived from securities cannot be properly used without significant adjustment. Securities will sell for premium prices to the underlying property for a number of reasons: 1. An investor can get needed cash quickly from securities. Securities can be normally sold with the click of an icon at very low cost and the cash will appear in the account within seconds. An operating property might take weeks or months to sell the property and require large costs for the legal, accounting, negotiation, and due diligence efforts involved. 2. An investor can invest a little or a lot in securities; a few shares or many shares can be sold. A physical property is typically sold in a large, single block. 3. An owner of an actual operating property must deal with the management hassle and effort of hiring and firing employees, setting compensation levels, marketing the product, building relationships with customers, developing extensive technical knowledge about operating the equipment in a property, meeting extensive regulatory requirements, developing extensive reporting documents, and a variety of other problems. An investor in a stock does not need to worry about any of this. 4. An investor in the stock has absolute limited liability; the most the investor can lose is the amount invested in the stock. In contrast, even if the project is separately incorporated, the owner of a property often does not have limited liability. Environmental issues, employee litigation, or a variety of other lawsuits have attached the owner s other assets to pay damages. Consider the huge amounts of legal liability that British Petroleum (BP) faced in the oil spill near Louisiana in The amount of damages BP paid were a sizeable multiple of BP s investment in the oil rig. 5. Stocks represent ownership in businesses that have substantial growth opportunities; companies can build new properties and enter new businesses. A single property which is the subject of a property tax appraisal is limited to whatever the single property can produce, e.g. refinery companies can build new refineries; a single refinery is limited to whatever it can produce. The potential growth of companies can dramatically affect the price at which the share is sold and hence the equity rates as calculated in cost of capital studies. 6. Stock prices capture future net present value from projects and assets which do not even exist today. A property value for property tax purposes in most states should reflect only the value of the property which exists on the lien date. Liquidity, as reflected in issues number 1 and 2 above, is the focus of much financial research on the difference in required return between liquid and illiquid but identical-risk investments. The issue of liquidity has exploded in importance in recent years due to fundamental changes in capital markets and the sources of available capital. In the early 1980s, President Reagan was faced with a Social Security system that was no longer selfsustaining; he proposed significant changes including a major increase in the required contributions. One of the indirect impacts of the serious debate surrounding social security at the time was a realization that declining birth rates and longer lives would severely impact the ability of Social Security to meet its future obligations. As a result, individuals and companies realized they could no longer assume that Social Security would cover all of Figure 1 42

43 their retirement needs. Investments in retirement programs and personal savings rose dramatically starting in the early 1980s. As Figure 1 illustrates, pension fund assets have risen from well under a trillion dollars to over $15 trillion since the early 1980s: The huge increase in the amount of money in these pension funds has had an enormous impact on the value of liquidity. These entities are regulated and must regularly report their value to determine if they are underfunded. To have a reported value, they must have prices. To have prices, they need liquidity. As a result, except for a small portion of their fund that may be allowed to be relatively illiquid, pension funds must invest primarily in liquid investments for which they have available prices at any given point in time. As a result, these entities usually restrict their purchases so that illiquid investments can only be a small (often 5% or less) of the portfolio. As a result of the increasing dominance of these investment vehicles, the difference in value between liquid investments and the value of illiquid investments has grown wider and wider over the years since However, the biggest impact on the value of liquidity came with the 2008/2009 downturn. A number of investments, especially mortgage-backed securities that were thought to be liquid, suddenly became illiquid. In the ensuing drop in markets, even some money market instruments, always thought to be absolutely liquid, froze up. Banks, corporations, and other entities that desperately needed cash could not obtain liquidity (cash) by selling investments that they had always been able to sell easily. Suddenly, investment managers who had never given a great deal of thought to the issue of liquidity became painfully aware of its importance. The need for liquidity became so intense that the rate on short term Treasury bills actually turned negative! Why would anyone pay the U.S. government for the privilege of loaning it money? Because investors knew that if they needed their money back at a moment s notice, they could always sell Treasury bills into an enormous, liquid market. In short, they were paying for liquidity. Research and empirical studies in the finance literature offer dramatic support for the need to adjust for liquidity differences: Liquidity (or marketability) is a key attribute of capital assets, and it strongly affects their pricing investors prefer to commit capital to liquid investments, which can be traded quickly and at low cost whenever the need arises. Investments with less liquidity must offer higher expected returns to attract investors. (1) liquidity-increasing financial policies may increase the value of the firm. This was demonstrated for our numerical example. If the spread is reduced to 0.486% [from 3.2%] (as in our low-spread portfolio group), our estimates imply that the value of the asset would increase to $75.8, about a 50% increase. (2) Our study contributes to the academic literature since we believe we offer the cleanest and most precise measures of the value of liquidity. Due to the unique experimental design inherent in REITs, especially the precision of underlying asset values, we are able to not only verify a link between liquidity and required returns but we also are able to accurately quantify these gains. our estimates of wealth creation jump to around 23% when comparing exchange traded claims to nontrading ones. (3) As business students also learn from the most commonly used textbooks, liquidity adds value: Securities that cannot be converted so quickly and cheaply into cash need to offer relatively high yields. (4) The Appraisal of Real Estate, perhaps the most powerful authority for many appraisers, also makes it very clear that liquidity must be considered: The rate of return on an investment combines a safe rate with a premium to compensate the investor for risk, the illiquidity of invested capital and management involvement. (5) The need to adjust for liquidity differences has long been recognized by assessors. For example, the California State Board of Equalization in its Assessors Handbook indicates: Liquidity Preference. An asset is liquid if it can be readily converted to cash at its current market value. All else being equal, investors prefer to hold assets that are liquid. The return for liquidity preference is the yield component required for holding assets that are not readily convertible into cash. Most financial assets are liquid. Real 43

44 estate and most business assets, however, are relatively illiquid, and real estate investors must be compensated for this reduced liquidity. (6) The argument based on lack of liquidity is a much stronger one. There is no question that financial assets are significantly more liquid than real estate assets. Rate of return estimates using the CAPM reflect returns on financial assets; thus, in practice at least, the CAPM assumes that all assets are liquid. An adjustment for lack of liquidity can be made in two ways: (1) consider lack of liquidity as an added risk factor and add a premium for it to the cost of equity estimated by the CAPM; or (2) value the real estate asset using the CAPM/WACC without any liquidity adjustment, and then apply a liquidity discount to the estimated value. In both cases it is difficult to arrive at a supportable estimate of the adjustment. (7) Various studies have placed the liquidity premium, depending on the characteristics of the actual property, from 2% to over 10%. Summary Many appraisers rely on standard textbook approaches to determining a weighted average cost of capital by obtaining data from traded stocks and bonds. They are often stunned at the difference in required return when they compare discount rates using the standard method and rates required by actual buyers and sellers of properties. One explanation of the difference may lie in how the cash flows to be discounted are determined. If the appraiser starts not with expected cash flows but rather with the most likely cash flows, then the appropriate discount rate may be dramatically higher than the standard weighted average cost of capital. The second explanation stems from the fundamental difference between required returns - at the same level of risk as measured by beta or other risk metric - on stocks and bonds which are quickly, easily and inexpensively traded as compared to property which may take weeks or months to sell as well as extensive accounting, regulatory, due diligence and legal costs. Footnotes 1. Amihud, Y. and Mendelson, H., Liquidity, Asset Prices and Financial Policy. Financial Analysts Journal (Nov/ Dec): Amihud, Y. and Mendelson, H., Asset Pricing and the Bid-Ask Spread. Journal of Financial Economics, 17(2): Benveniste, L., Cappozza, D. and Seguin, P., 2001, The Value of Liquidity. Real Estate Economics, vol. 29(4): R. Brealey, R. and Myers, S., Principles of Corporate Finance, eighth edition, 827. Burr Ridge, Illinois: McGraw Hill. 5. Appraisal Institute, The Appraisal of Real Estate, thirteenth edition, 464. Chicago, Illinois: Appraisal Institute. 6. California State Board of Equalization,1998. Assessors Handbook, Section 502, Advanced Appraisal (December): California State Board of Equalization, Assessors Handbook, Section 502, Advanced Appraisal: Appendix A (December): Biography Dr. Heaton is the Denny Brown Professor of Finance, in the Marriott School of Business, Brigham Young University. He holds a PhD. in Finance and an MA in Economics from Stanford University and an MBA and BS in Mathematics from BYU. Hal Heaton, PhD Marriott School of Management Brigham Young University Provo, Utah Phone: (801) Fax: (801) halheaton@byu.edu 44

45 The Exploration Game Statistical Decision Theory Using Bayes Formula Dr. E.L. Dougherty SPE Distinguished Member & SPE Legion of Honor Recipient of SPE Cedric K Ferguson Medal & J. J. Arps Award Emeritus Professor, University of Southern California Preface This paper is a revised extension of the following two papers I submitted to the SPE: 1. The Exploration Game, SPE 1440, February The Oilman s Primer On Statistical Decision Theory, SPE 3278, August The SPE informed me that its Editorial Committee found both highly interesting but after extensive debate concluded that they did not contain sufficient information of interest to SPE members to warrant publication. Both are available via spe.org s OnePetro. Ironic that less than 20 years later, SPE began publication of a stream of papers on this subject and SPE Bookstore today offers two books on the subject: Making Good Decisions and Decision Analysis in E&P. INTRODUCTION Statistical decision theory along with its graphical counterpart, the decision tree is a powerful and practical management tool. The method depicts and quantifies uncertainty, thus helping a manager to make sounder decisions. The technique provides an easily understandable framework that enforces a disciplined analysis formal or conceptual of the risks surrounding a decision. As has become the norm in SPE papers, we use the acronym SDT as a convenient shorthand. SDT guides the decision maker to: 1. think through the decision he is about to make, 2. clearly pronounce his objective, 3. define the alternative actions available, 4. specify the possible outcomes -- physical, political, otherwise -- that could affect results, 5. pick quantitative yardsticks to measure costs, benefits, and desirability, 6. write down what he believes the probability of each action-outcome pair to be. SDT s decision tree traces the sequence of events and outcomes and assigns a probability-weighted value to each possibility. Since computers perform required calculations almost instantaneously, the impact of critical assumptions and data uncertainties can be thoroughly tested. Finally, the method provides a readily understandable one picture is worth a thousand words way of showing how things stack up, thus allowing the judgments and experiences of others to be called on. Technical articles explaining the concepts of SDT and recommending its use abound. All creditable university MBA and Management Science curricula include courses on it. Shelves are full of books on SDT. The References list the earliest and latest books on oil and gas decisions and one in between. As we have already implied, probabilities are the foundation of SDT. We assume the reader is familiar with the basic concept of probability p is a number between 0 & 1 expressing the likelihood of an uncertain or chance event. 45

46 You know that if you toss a coin, a head or a tail is equally likely so p(h) = p(t) = 0.5. Or if you roll a die, p(1) = p(2) = p(3) = p(4) = p(5) = p(6) = 1/6. In simple situations such as this, the probability of each event is well defined. In more complex situations probabilities are determined by the following logical counting process: 1. Define the event whose probability you want for example, a 2 turns up on the die. 2. Count the total number of equally likely possible outcomes events -- of the chance process with the die this is Count the number of equally likely ways the designated event can occur for a 2 this is Divide the 2nd number by the 1st and this is your probability for die = 2 this is 1/6. Let s consider a couple of more examples to illustrate the process. What is the probability that someone I meet has the same birthday as I? Ignoring leap year the person s birthday could be any of 365 days. Only one of those would be the same as mine. Therefore, the probability is 1/365. What is the probability of obtaining 2H & 2T when 4 coins are thrown? This is the analog of determining the probability of a 7 when two dice are tossed to be 6/36 = 1/6. With 4 coins there are 2 4 = 16 equally likely outcomes. Counting we find 5 distinct outcomes as follows: 1 4H 1 4T 4 3H-1T 4 1H-3T 6 2H-2T The desired probability, p(2h2t) = 6/16 = 3/8. What you do not realize is that things are more uncertain than you think, as we drive home in the study of offshore bids later. This paper, which gives examples illustrating how to apply decision theory to petroleum situations, has two sections, The Exploration Game and Exploration Decision Examples. In the first the principles of the method are explained in a context in which the underlying probabilities are known thereby avoiding detracting from the explanation by concern over specifying them. In the second section, realistic example applications are presented. Here, the concept of subjective probabilities is introduced. These numbers are obtained by combining historical data (classical probabilities = long run frequency) with opinions of knowledgeable experts expressed as fractions between 0 & 1. THE EXPLORATION GAME The Exploration Game, a fascinating educational pastime, pits an individual, or a group of individuals playing collectively, against an entity called Nature. Individual players are called Oilmen. A group of Oilmen playing collectively, or an individual playing alone, is called an Operator. The game proceeds as follows: The Oilmen enter the room in which Nature is located. Nature has a large spinner with a calibrated dial and three bags, called Prospect A, Prospect B and Prospect C. Prospect A contains 2 white balls and 4 black balls, Prospect B contains 3 white balls and 3 black balls, Prospect C contains 4 white balls and 2 black balls of the circumference of the dial is yellow (Prospect A), 0.15 of the circumference is green (Prospect B), 0.05 of the circumference is red (Prospect C). All of this is known to the Operator. Exploration Choice A B C 46