Decisions made every day at the asset level affect corporate-level profitability, and oftentimes, this big-picture impact is not obvious to those on the front lines. How can exploration and production (E&P) organizations enable frontline managers to make decisions for a particular field within the larger context of the larger corporate portfolio? A view of the corporate portfolio shows how oil and gas (O&G) assets come together to drive or diminish value. The E&P industry was originally structured in siloed multidisciplinary teams, and this has resulted in inherently suboptimal overall performance for O&G assets. Projects run behind schedule and over budget, reserves are frequently downgraded after final investment decisions, and value winds up destroyed. Juxtaposed with the huge capital expenditure (CAPEX) required to meet long-term global hydrocarbon demand, an increasing need exists to leverage technology and integrate people, processes, and platforms to produce better results. This paper discusses the decision dynamics of E&P organizations within the context of the lifecycle of a hydrocarbon asset, identifies the weak links in this decision-making process, and suggests ways of achieving a more collaborative process for making value-added decisions. It demonstrates, using a case study of an exploration project and an acquisition target, how organizations can build resilient portfolios by continuously calibrating to rapidly changing technical and market conditions, thus, unlocking "option value" and achieving balance between risks and rewards. This paper seeks to trigger a new way of thinking in how organizations could leverage an integrated platform to solve the disconnect between underlying technical risks resident in engineering and execution models and the economic risks inherent to the market and its models. Much like how the integration of point engineering models significantly advanced the utility of the digital oilfield in the early 2000s, the coupling of petrotechnical, operational, and economic models will unlock a new, greater level of upstream optimization. This paper uniquely shows how possible this integration is today and helps progress the adoption of expected value thinking in the industry. Additionally, the paper introduces the effective use of a cloud-enabled integrated solution to evaluate prospects in a timely manner with minimal information and less resources, thereby, delivering results in mere minutes that would have previously taken a team of professionals several months to achieve.

The paper discusses a novel workflow and tools for stochastic optimization under uncertainty aimed at maximizing the value of exploration and early appraisal assets using a public example of an East Africa offshore gas discovery. Following an initial discovery well, the prospect enters an appraisal phase with tangible uncertainties in resource base (a P10/P90 ratio of 3.1), well productivity (P10/P90 of 2.1), and cost estimates (30%/+50% tolerance). The choice of LNG train capacity and development concept changeover point has a profound effect on asset evaluation results used for attracting new project partners. These uncertainties raise a number of critical questions: How can a facility engineer elaborate these key parameters given the three-fold uncertainty in resource base at this project stage? Will these choices change under a random walk in oil prices or given different attitudes towards risk by the management? Does maximization of project after-tax value and of production entitlement imply identical values of control parameters in these circumstances? The challenges are resolved by sampling the solution space with 54,000 full-cycle evaluation trials run in just over 13.5 hours using a combined power of an exploration decision support tool with fit-for-purpose and fit-to-data risks, resource, and value assessment that honors all risks and uncertainties; a workflow management tool for decision optimization based on a multiple-realization approach powered by various uncertainty and optimization methods; and a proprietary link between these two applications. The original operator's view on the facilities concept was found to reflect a P50 of reserves uncertainty and a P50 of worldwide LNG train capacity distribution. Optimizations were performed to address five progressively complicating problem formulations. The achieved outcomes match conclusions that could be derived with a reasoned train of thought in economy-of-scale context meaning our solution is applicable for modeling situations lacking simple analytical solutions.

Unconventional plays requiring large, high cost development programs now dominate the North American onshore Oil and Gas business. Large drilling programs are often justified and forecast based on ‘type curves": forecasts for a typical single well, intended to be repeated as many times as necessary to represent an extensive drilling program. When companies want to add uncertainty analysis to their forecasts, that work often begins with extending the type curve to include a range of possible single well outcomes, often by including the production forecast for a P10 and P90 well on the curve. When Monte Carlo analysis is performed on this expanded type curve for a substantial program, the program uncertainty all but disappears. This is a simple illustration of the law of large numbers. There is still a considerable amount of uncertainty in resource plays, of course, but it is not due to well-to-well variation, at least in the long term. We created a set of synthetic tight oil well data and subjected it to a number of Monte Carlo analyses to illustrate this point. In these analyses, we show that correlation among the wells accounts for far more of the uncertainty encountered than does well to well correlation. Factors that might be correlated include long term decline, drilling and completion cost and IPs due to interference and/or reservoir quality. Examples are shown for the decline and cost correlations.

The translation of risk from conventional to unconventional resources moves the fulcrum from risk of finding, towards risk of commerciality. We are often certain a hydrocarbon producing system exists; but, uncertain in what manner (production profile) it will produce. This reality is incompatible with traditional methods of risking in which recoverable volumes are estimated from volumetric calculation, and risked volumes may be calculated as the product of unrisked volumes and some risk factor. Production profile is then determined after-the-fact. Unconventional resources are typically evaluated from the basis of a type well or production analog first, and then the recoverable volume is emergent from this production profile. Like the chicken-and-egg problem, uncertainty of recoverable volumes must be estimated in association with one or more production profiles. The threshold of commerciality can be calculated, and the chance of commercial success determined by the intersection of the threshold volume on the distribution of recoverable volume. This distribution is truncated and the outcomes above the threshold are rescaled to represent success outcomes… but, failure outcomes are disregarded. This paper presents a simple workflow using Frequentist and Bayesian approaches, respectively, for proper risking of uncertain recoverable volumes for an unconventional resource, taking into account the chance of false positives from appraisal well information. A subjective risk tolerance can be included to respect how aggressive or conservative a company may be in pursuit of a project. A method of re-casting production profiles is demonstrated as an improvement over the common method of scaling, or factoring, the initial production rate. The scaling approach may reach unrealistic values of initial production in an attempt to achieve the proper recoverable volumes. Productive yet uneconomic outcomes should be included in any thorough project evaluation. Failure to do so may overestimate the value of a given project. However, the chance of such outcomes occurring is often seen as a difficult-to-determine value. Quantifying the uncertainty in type well volumes allows evaluation of the imperfectness of information of early well results. Enhanced knowledge of the project risk, the chance of success, a view of the success case, and an understanding of false positive outcomes, can lead to higher quality decisions.

Technical professionals are often asked to estimate "ranges" for uncertain quantities. It is important that they distinguish whether they are being asked for variability ranges or uncertainty ranges. Likewise, it is important for modelers to know if they are building models of variability or uncertainty, and their relationship, if any. We discuss and clarify the distinction between uncertainty and variability through strict definition, illustrative analogy and numerical examples. Uncertainty means we do not know the value (or outcome) of some quantity, eg the average porosity of a specific reservoir (or the porosity of a core-sized piece of rock at some point within the reservoir). Variability refers to the multiple values a quantity has at different locations, times or instances – eg the average porosities of a collection of different reservoirs (or the range of core-plugs porosities at different locations within a specific reservoir). Uncertianty is quantified by a probability distribution which depends upon our state of information about the likelihood of what the single, true value of the uncertain quantity is. Variability is quantified by a distribution of frequencies of multiple instances of the quantity, derived from observed data . That both are represented by ‘distributions’ is a major source of confusion, which can lead to uncritical adoption of frequency distributions to represent uncertainty, and thus to erroneous risk assessments and bad decisions. For example, the variability of natural phenomena is sometimes well-approximated by normal or log-normal distributions, but such distributions may not be appropriate to represent the uncertainty in outcome of a single occurrence. We show there is no objectively ‘right’ probability distribution for quantifying the uncertainty of an unknown event – it can only be ‘right’ in that it is consistent with the assessor's information. Thus, different people (or teams or companies) can legitimately hold different probabilities for the same event. Only in very restrictive, arguably unrealistic, situations can we choose to use a frequency distribution derived from variability data as a probability distribution to represent our uncertainty in an event's outcome. Our experience as educators of students and oil & gas industry personnel suggests that significant confusion exists in their understanding of the distinction between variability and uncertainty. This paper thus provides a resource for technical professionals and teachers to clarify the distinction between the two, or to correct it where it has been wrongly taught, and thereby help to improve decision-making.

Declining global economic conditions and increased pressure to find new, affordable sources of energy create a turbulent macro environment for oil and gas companies. Stakeholders present conflicting goals of increased return on investment while simultaneously reducing risk and providing greater transparency into project performance. The traditional models of large scale investment into major capital projects can no longer guarantee success; instead, organizations must focus on capital management throughout a project's life cycle. Significant effort, time, and resources are invested in project delivery. However, poor decisions during the planning phases may result in loss of value during execution. By combining decision analysis tools and techniques with integrated, dynamic planning systems, companies are able to carefully manage their capital across the project life cycle to understand their costs, resources, and schedules. These companies benefit from increased collaboration leading to more informed decision making balancing risk and reward. This paper presents an innovative method for evaluating and dynamically planning the development of uncertain upstream investments. It centers on a paradigm shift in the way upstream managers assess investments, toward an approach that incorporates decision analysis tools and techniques with integrated dynamic planning systems. Case study examples are provided to illustrate key principles.

A management decision-making and planning tool has been developed to provide a quick, high-level resource quality assessment of oil sands assets to enable economics-based ranking of investment opportunities. It is systematic and transparent, largely avoiding the subjectivity and human bias often associated with ranking assets for capital allocation. The model utilizes the available reservoir characterization information (API gravity and petrophysical analysis including oil saturation, effective porosity, V shale, pay thickness), expected operating conditions (steam injection pressure, horizontal well lengths), and a reservoir risk assessment to predict the key performance metrics for an in situ oil sands project using SAGD (steam assisted gravity drainage) including oil rates, steam-oil ratio and recovery factors. The reservoir risk factor is a quantification of the production impact (lower expectations and/or increased uncertainty) from reservoir impairments based on expert opinions and reservoir simulation. These performance metrics can then be used to estimate expected overall economic potential (IRR) for a given asset. The ranking can be done at various levels: land sections, defined prospects, wellpad drainage areas, or at the individual (delineation) well level. Predictive analytics techniques, in this case multi-variable linear regression, were used to construct the model. It was initially based on thermal recovery theoretical models for the SAGD process for predicting oil rates and a simple energy balance for predicting steam-oil ratio (SOR). Subsequently it has been updated via industry production data "fitting", or applying the actual performance data of various mature, operating wellpads to improve the confidence level of the model. The result is a hybrid model; science-based but influenced by real operating and production experience. It has served as a primary tool used for strategic planning, in the setting of high-level performance targets (and probabilistic distributions thereof) for each of the assets. This tool has enabled a resource driven development strategy, allowing the company to focus technical resources on the assets that possess the greatest economic potential. Resulting business decisions include capital allocation (for additional delineation data) and more rigorous technical efforts (reservoir modeling and simulation) on the highest ranking prospects.

Unconventional plays have considerable uncertainty in production and economics. This paper proposes Decision Entropy Theory to represent uncertainty in unconventional plays in an objective and defensible way. The motivation to develop this theory is to account for the possibility of events occurring that are beyond our range of experience. This theory characterizes uncertainty in the context of making a decision; the case of maximum uncertainty corresponds to the maximum entropy for the possible outcomes of the decision. Assessing the value of test wells before committing to developing a play with unconventional resources is used to illustrate the theory. We pose the decision to develop an unconventional play in terms of the cost of development, the ratio of the return-on-investment for productive wells in the play, and the uncertain frequency of productive versus unproductive wells in the play. Results for the value of tests wells are obtained and provided graphically in non-dimensional relationships. Significant conclusions are the following: The probability distribution for the uncertain frequency of productive wells is important both for deciding whether or not to develop a play and for the value of information from test wells. It is unreasonable to assume that the probability distribution for the frequency of productive wells can be established based entirely on experience because that precludes the possibility of events beyond our experience. Such events can be particularly important with unconventional plays where experience tends to be limited. The value of test wells is enhanced when leaving open the possibilities for profit on a play with a small return-on-investment ratio and the possibility for loss on a play with a large return-on-investment ratio. There is a balance between relying entirely on historical data from analogous plays versus not relying on them at all. Within this balance, information for test wells at a new play can be combined with information from analogous plays to inform the decision about developing the new play. The value of test wells in a new play depends on the break-even frequency of good wells in the play; in the case of no information, this value is a maximum when the break-even frequency is 50 percent.

The meaning of risk or the characteristics that make a particular opportunity or decision ‘risky’ may vary widely from company to company and even within a company depending on the perspectives of the individuals involved. The perceived risk of a deal as viewed by operations or drilling executives might vary significantly from those in geoscience or human resource roles. The CFO might bring an entirely different perspective on the risk of an opportunity, as company level metrics, credit ratings, and investor guidance are considered. Each of these individuals or groups brings their unique domain expertise and understanding of risks to a particular decision. The challenge for any organization is in tapping into this knowledge and aggregating these points of view into a sound and consistent description of the risks to the company. Stochastic portfolio analysis provides an ideal structure for ensuring that risks are adequately considered from a corporate perspective, by capturing individual uncertainties, aggregating these into a common whole, and assessing aggregate uncertainty against the metrics relevant to company performance. When employed effectively, these techniques make it possible to assess each decision or opportunity in the context of company objectives, normalizing disparate risk measures into a common frame of company impact. Financial considerations and drivers that are key to the CFO may be assessed and optimized in tandem with consideration of drilling delays, dry hole probabilities, or variations in production decline characteristics. Many of the risks or areas of concern to the CFO are ideally evaluated during opportunity evaluations and business plan development. These questions might include: Is the depth of the inventory sufficient to exploit additional financing potential? Can a specific acquisition be funded internally? How would this acquisition compare to development of the existing opportunity inventory? How sustainable is the plan? Would financing support sustainable growth or will additional financing be needed? Should additional shares be issued? Will the value gained by developing certain assets offset share dilution effects? What working interest or scale of investment should be targeted for acquisition, given current balance sheet considerations? While many of these questions can be addressed with expected value data, the use of stochastic descriptions provides additional insight into the confidence that specific performance metrics (cash flow, debt ratios, earnings growth, etc.) may be achieved. Evaluating the portfolio performance and risks holistically, makes it possible to exploit the full range of options within the portfolio in mitigating specific risks and delivering targeted performance. As contrasted to methods that purport to compute corporate utility functions ( Bickel, 2006 ), stochastic portfolio analysis establishes a practical and transparent frame for assessing company risks. This paper provides a high level overview of the way in which stochastic portfolio analysis may be applied to assess risks at a company level. This includes integration of financial and operational risks in the evaluation of specific opportunities or as part of a typical strategic planning cycle. A case study is used to demonstrate both the methodology as well as the value and insights gained from application of these techniques. This example clearly shows how assessing the full spectrum of uncertainties at an aggregate level contributes to more effective portfolio allocations, leading to increased valuations and reduced exposure to risks.

A challenge in the oil industry is the ability to forecast uncertainty reduction resulting from new information. This is particularly important in exploration, appraisal and pilot programs, where management weighs the amount of information expenditure needed to make drill or development decisions. Although a key decision criterion for this is often value, another important criterion is uncertainty reduction, or confidence in the interpretation of critical uncertainties. So how can teams understand project uncertainties and the effects of different information or appraisal options on their uncertainty assessments? Through an efficient, multi-discipline reliability of information assessment interview process designed to help the team think clearly and openly about the amount of uncertainty reduction to be gained for different information/appraisal programs. The product is a series of confidence graphs depicting uncertainty reduction versus information options. The plots are developed using Bayesian mathematics and the reliability of information assessment interviews. They address: (1) the probability of a correct interpretation; (2) the probability the actual outcome will be the low outcome after having forecast a P50 outcome; (3) the probability the actual outcome will be the low outcome after having forecast a high outcome; and (4) the probability of getting a given outcome after having correctly forecast that outcome from the information (e.g., the probability of getting a P50 outcome after forecasting a P50 outcome from a test). The important aspects of this paper are the set of reliability of interpretation questions used to forecast uncertainty reduction potential, how to conduct those interviews and how to develop the resulting confidence plots. The questions address the drivers of uncertainty reduction potential: information tool accuracy; effect of the environment from which the information is being collected; the tendency of the interpretation to better forecast true low, true P50 or true high states of nature; and the representativeness of the information.

The petroleum industry has recognized that a consistent probabilistic approach provides improved understanding and insights into the investment decisions. Yet, although most oil & gas companies appreciate the impact of commodity prices on the value of their potential investments, few are implementing price models at the level of probabilistic sophistication and realism of their, say, subsurface models. We illustrate the implementation and calibration of the two-factor stochastic price model ( Schwartz and Smith, 2000 ) that allows mean-reversion in short-term price deviations and uncertainty in the long-term equilibrium level. It provides advantages over more basic methods but is still simple enough to be communicated to corporate decision makers. The balance between realism and ease of communication of the model has led us to choose this model in favor of one-factor models, which assume that only one source of uncertainty contributes to the uncertainty in prices, or other multi-factor models where two or more factors contributes to the uncertainty in prices. Previously, a Kalman filter was used to estimate the model parameters based on historical spot and futures prices. We illustrate how current market information (such as futures prices and options on futures observed in commodity futures exchanges) can be utilized to assess the parameters of the two-factor price model. As opposed to the Kalman filter technique, the implied approach to parameter estimation is easy and intuitive, and it will generate estimates that are good enough for most valuation assessments.

Oil & Gas (O&G) projects are inherently risky. Increasing economic volatility has compounded the complexity of decisions that O&G companies must make in selecting and investing in projects. Consequently, managerial toolkits to support decisions on economic viability of investments under uncertainty have proliferated, from established traditional models such as Discounted Cashflow Analysis (DCA), to more contemporary ones such as Real Options Valuation (ROV). A comprehensive literature review of the Decision Analysis tools used in upstream asset valuations was performed, and identified tools were mapped to a conceptual decision analysis framework. Perceived value and popularity of these tools were established by conducting a broad survey which attracted 308 respondents over a two-month period. The survey results indicate that the tools can be grouped into three broad categories: tools with universal acceptance; tools that are not embraced by the O&G industry; and tools with partial acceptance. The survey also included several demographics dimensions. Several tools exhibited an increasing trend in utilization with increasing company size, while others indicated a functional silo effect where tools’ acceptance levels varied considerably across job functions. Geographical impacts were not analyzed in detail as the sample space was skewed towards Europe and North America. Utilization and satisfaction levels are generally well correlated; however, a cross-analysis of these two dimensions indicates that discrepancies do exist. Hydrocarbon price forecasting and cost estimation tools provide a lower satisfaction relative to their utilization frequency, while some of the well-established tools appear to be struggling to gain acceptance despite being valued by their practitioners.

Traditional stochastic modeling efforts for exploration project assessment start from a model that incorporates estimates of uncertainties and risks and then simulates how the company (decision subject) acts as the risks and uncertainties are resolved (outcomes revealed). In resource play assessment the model also often includes learning of the experience curve (learning-bydoing) type: there is a model component that specifies how uncertain well costs and uncertain drilling durations are reduced as a function of the cumulative number of wells that have been drilled. This paper presents a framework for assessment of recovered resources and economic value that also models statistical learning. The framework includes an explicit model of how prior uncertainties are transformed into posterior uncertainties and simulates decisions that are based on the posterior uncertainties -- and not on full certainty. The learning application is shale gas resource play assessment and the learning is linked to pilot production. The work follows up on earlier work ( Haskett & Brown, 2005 ) that recognizes that analogs used to define well performance provide an envelope (a population) of well curves. The new integrated analytics presented in this paper use the pilot production to constrain the prior distribution and make decisions based on the resulting posterior well performance (EUR/well) curves. Modeling learning provides a basis for a more realistic simulation that captures not only the potential for more or less effective decisions, but also can be used to assess the value of information. Application is illustrated with an assessment that is used to support the decision to enter a shale gas resource play.

The petroleum industry has always struggled with the "disconnect" between modeling subsurface uncertainties and integrating them into full robust economic analysis. Why? Because there is inconsistent use of probabilistic analysis within asset teams and functions, using disparate software tools, few of which can communicate with each other. Depending upon the project and the company, commercial analysts may use single cases, scenarios, decision trees, best estimates or risk weighted analysis combined into a composite case. Few, if any, have moved their project analysis to a fully integrated stochastic approach. Again, why? Because an "all singing all dancing" subsurface through commercial model does not exist. Different functions in an asset team use different software tools, few of which communicate with each other. In addition, there are few software applications which support fully stochastic analyses, which further complicates the task. As a result, many subsurface uncertainty characterizations get reduced to a few cases which are passed to the commercial analyst. Wouldn't it be best to integrate both workflows? This would allow the subsurface teams to preserve their characterizations while the commercial analysts interact with the reservoir distributions in their native states. Have the commercial analysts run their calculations using their models and their decision making tools so they can clearly see when, where and how decisions may change based upon all uncertainties. It is more than modeling with precision; it is about knowing when we should be changing our minds. This paper is designed to stir some thoughts in this area and showcase an example where stochastic analysis and decision trees are combined in three different ways within a single case to provide proof to the adage "the whole is greater than the sum of the parts". A live working example will demonstrate how correlations, full economic analysis and value of information can best be achieved by combining forces. The aim is to demonstrate the power of new ideas and workflows which can be implemented and used today in a practical way.

The optimizer’s curse phenomenon seen in portfolio optimization appears similar to the winner’s curse observed in competitive bidding. A technique that calculates the effects of optimizer’s curse and sampling error bias is adapted for analyzing competitive bidding. The demonstration example is an auction of single risky project, which could be an exploration block containing one prospect. Bid optimization involves finding a function that expresses bid fraction as a function of estimates for 1) project expected monetary value and 2) number of other bidders. A foundation principle for a symmetric auction is that each bidder must be in equilibrium. The base model assumes that all auction competitors a) have matching judgments for the project parent population (in three parameters), b) all competitors the same estimate/actual error functions (in the three judgment parameters), c) use the same bid factor function, and 4) are risk neutral. Several correlations are embedded in the calculations. Incorporating specific company cost and information advantages, sub-models representing the project appraisal process to determine commercial success, and other such details is straightforward. The example assumes one company (us) has assessed chance of success, test cost, and discovery value for a subject prospect. A brute-force Monte Carlo simulation approach solves Bayesian calculations for the auction simulation. The calculations produce estimates of 1) probability of winning the auction and 2) expected monetary value versus bid amount, thus determining the optimal bid.

Sound decision making requires the elicitation and quantification of key uncertainties. Probabilities are, in general, subjective and most petro-technical experts find assessing them challenging. Furthermore, much evidence shows that, although they may not be aware of it, assessors find it difficult to make unbiased assessments. We show how the maximum entropy principle, an idea from information theory, can be used to overcome the challenge of uncertainty assessment in oil & gas decision-making situations. It has found great popularity in natural language processing, space communication, biomedical engineering, and many other fields but has received limited attention in oil & gas. After describing how the technique can be adapted to information typical of oil and gas, we illustrate its application to a field development decision. We conclude that the maximum entropy approach can incorporate many types of partial information. The examples and applications in this paper illustrate the relevance and power of the approach for quantifying probabilities in the context of oil E&P decision making. It is shown that arbitrarily "interpolating" between assessed probabilities, or ignoring dependencies, can lead to biased probability distribution, which in turn may lead to sub-optimal decisions.

Abstract Investing in an oil and gas field is capital intensive and usually involves many risks. Nevertheless, the economic evaluations of these types of investment opportunities often ignore risk or are based on subjective risk assessments that are frequently biased. Furthermore, as the life of the field progresses, well and seismic data become available and the risk level of the project changes. In this paper we demonstrate a decision and risk analysis (DRA) method that minimizes the subjectivity in the evaluation and that can be consistently updated as the life of the field progresses. We use a tank production model and Monte Carlo (MC) methods to simulate the possible economic outcomes, and ensuing risk, of a primary oil recovery project as a function of the uncertainties associated with the reservoir properties. During the early stages of the field (i.e. pre-discovery) the uncertainty information comes from the analysis of analog fields. In the post-discovery stage, the initial uncertainties are updated with well and seismic data using an application of Bayes theorem. The method demonstrated here gives decision makers a consistent and relatively transparent way to evaluate E&P investment opportunities. It also allows them to track how the risk level of a project changes through the life of the field. Introduction By far the most common decision criteria used in Exploration and Production (E&P) today is the discount cash flow analysis or DCF (Brashear et al., 2000). Most DCF analyses use a single Net Present Value (NPV) as a measure of the profitability of the project/investment. However, a major limitation of the so-called single value methods is that they fail to account for a project's uncertainties, as illustrated in Uman et al. (1979). The decision and risk analysis (DRA) is an alternative to the single value criteria because it explicitly addresses the uncertain nature of the oil and gas projects. In general we can divide the DRA into five steps (Newendorp and Schuyler, 2000): Identify the decision alternatives (e.g accept or reject an oil project). Identify the possible outcomes and calculate the NPV for each outcome. Assess the probability of each outcome (this step is also called risk analysis). Calculate the expected monetary value (EMV) for each decision alternative. Select the alternative with the largest EMV. The risk analysis is an essential component of the DRA, but is also the weakest link in the DRA chain (Newendorp and Schuyler, 2000). Because of the sampling limitations associated to oil projects, the probabilities used in the risk analysis often come from subjective assessments. However, Capen (1976) demonstrated that our ability to subjectively assess the probabilities of an event is very limited. It follows that the risk analyses that heavily rely on subjective assessments are usually biased. The typical oil project can be divided into four stages: exploration, appraisal, development and production (this sequence is also called the field life cycle ). Usually as the life of the field progresses we gather different types of data (e.g. from well logs or from seismic) that often help reduce the uncertainty of the project. It is therefore essential to revise the DRA as new data become available to reflect the changes in the project's uncertainties. We intend to demonstrate a decision and risk analysis framework that focuses on quantitative assessments of the probabilities. The framework is such that we can easily update it as new reservoir information becomes available through the field life cycle.

Abstract There are several different methodologies for finding the value of assets or projects that have both underlying uncertainty and the managerial flexibility to optimize decision making. This has led to questions about the differences between approaches to these so-called real option valuation problems; the assumptions, mechanics, and applicability of the various methods for different problems. In this paper, we show that traditional decision analysis methods can provide an intuitive generalized approach to valuing real options. The first characteristic of our approach is a departure from the binomial lattice framework with replicating portfolios that is typically implemented in the discrete-time approach in the Finance literature to a binomial decision tree with risk-neutral probabilities. This gives an equivalent but more intuitive framework for approximating the uncertainty associated with the changes in the value of a project over time. The second characteristic is a methodology for combining uncertainties for a project into a single underlying uncertain variable; the present value of the project itself. This approach provides greater flexibility in modeling of real problems, including the ability to include multiple underlying uncertainties and multiple options with complex payoff characteristics. We apply this method to an example acquisition valuation problem, showing the specification of project uncertainties, cash flows, and decisions, and demonstrate how solutions can be obtained with commercially available decision tree software. Introduction One of the primary responsibilities of a management team is to make decisions during the execution of projects so that expected returns are maximized or expected losses are minimized. This is especially important for projects with flexibility, such as those with the options to expand operations in response to positive market conditions, to abandon a project that is underperforming, to defer investment for a period of time, to suspend operations temporarily, to switch inputs or outputs, to reduce the project scale or to resume operations after a temporary shutdown. Can we quantify the potential value associated with these types of flexibility? In principle, the answer is yes. However, solving these types of problems can only be accomplished using an option pricing or decision analysis approach, and not the standard deterministic discounted cash flow approach that has been the primary method of valuation for the past half-century. As a result, widespread quantitative valuation of managerial decision-making under flexibility has been slow to arrive in practice. Some of the recent work to develop the notion of valuing optimal decision-making, including Copeland and Antikarov (2001) and Copeland and Tufano (2004), has attempted to introduce computational methods that are more accessible to practitioners. We build on this work by 1) applying an intuitive approach based on familiar decision analysis concepts and 2) incorporating an alternative valuation methodology from the option pricing theory of Finance within our decision analytic framework. In this approach, the project flexibilities, often called real options, can then be modeled easily as decisions that affect future cash flows. This specification of project uncertainties and decisions allows problems to be modeled and solved using commercially available decision tree software familiar to managers and other practitioners. The discussion presented in this paper draws on materials published previously in Brandao, Dyer, and Hahn (2005a,b), and additional details can be found there.

Abstract "Economic Capital Optimization" (ECO) provides a comprehensive methodology to improve a firm's expected long-term growth while reducing its risk. This framework adapts and extends proven economic capital methodologies developed in the financial services industry to address both the similar and unique financial issues facing oil companies. Economic capital is the capital a firm must maintain to sustain long-term growth by covering short-term and long-term needs or losses. For sustainable growth, a firm must have the capital to support the operations that create economic value, despite anticipated and unforeseen shortfalls in cash flow or changes in business cycles. Insufficient economic capital can disrupt a firm's operations, destroying value and eventually leading to potential default. Overcapitalization or poor allocation of economic capital can lead to corporate returns and growth that fail to meet investors' expectations. Optimizing economic capital requires properly aligning a firm's capital structure, strategies, capital allocation process and risk management practices to maximize long-term growth. ECO provides a practical framework for examining the interdependencies among these components and for understanding how each impacts the long-term success of the organization. This paper discusses the concepts behind ECO and its practical application in the oil industry. This paper also will demonstrate how an oil company can improve its expected long-term growth by applying the ECO framework to more effectively allocate economic capital among business units or projects, to adjust its economic capital to meet long-term strategic needs, and to better manage its risks. Introduction All companies are in the business of managing risk. Those that excel at it survive and prosper; those that manage risk poorly fail. Successful organizations must actively manage risk to create a competitive advantage over those that manage risk passively. Over the last decade, a new generation of risk management practices has revolutionized the financial industry, enabling companies to control and manage risk while maximizing shareholder value. This new methodology was developed in response to the financial disasters of the 1990's and increased volatility in the financial markets. At the heart of this revolution is the practice of economic capital analysis. In a recent survey of over 200 financial institutions by the Economist Intelligence Unit and PricewaterhouseCoopers, almost 75% of respondents either had adopted economic capital analysis or planned to introduce it. The survey also indicates that a large number of companies outside the financial industry are beginning to adopt this methodology. Economic capital is the lifeblood of an organization. If a company begins to deplete its economic capital, it suffers financially. Financial distress destroys shareholder value, forcing a company to meet its liquidity demands by monetizing assets or raising capital at a disadvantageous time, borrowing at higher interest rates, and/or discontinuing the execution of its strategy. If a firm exhausts its economic capital, the firm becomes insolvent and eventually dies. A company can have a superior strategy, but if it has insufficient economic capital, a setback or down cycle eventually will force the firm to delay or discontinue execution of that strategy, and the expected value creation will be lost. Over the last few decades, many oil companies have started aggressive strategies during rising oil prices, only to curtail or exit them when prices fell. Their actions are tantamount to buying at high prices and selling at low prices, a practice that decimates shareholder value.

Abstract Modern asset pricing methods that account for managerial flexibility in project valuation require the specification of the stochastic processes followed by commodity prices and by other underlying uncertainties.Simplifying assumptions about these processes can be made to facilitate numerical analysis using binomial trees or lattices; however these assumptions are often untenable.An example is the assumption of a stochastic process for commodity price that is lognormally distributed with variance that grows linearly with time, commonly referred to as a Geometric Brownian Motion (GBM). Although GBM processes are convenient for modeling, they produce forecasts that do not match the empirical evidence of mean-reversion in commodity prices, and result in significant errors in valuation.Several methods have been introduced for modeling mean-reversion, but they complicate the numerical analysis of valuation problems. In this paper we discuss a new method for modeling mean-reverting processes that enables straightforward binomial tree and lattice-based approaches to valuation.We provide an illustration by applying this method to model a one-factor mean-reverting price process, and then use this model in the valuation of an example oil and gas project with downstream managerial flexibility.We also solve the example using an alternative method, monte carlo simulation, and show that our results are identical to those obtained from this more computationally intensive approach. Introduction Discounted cash flow methods (DCF) have traditionally been the standard practice for the valuation of projects and for decision-making regarding investments in real assets.The seminal work of Black and Scholes[1] and Merton[2] in the area of financial option valuation led to the application of option pricing methods in valuing real investments under uncertainty, by recognizing the analogy between financial options and project decisions that can be made after some uncertainties are resolved. This approach has the advantage of including the value of managerial flexibility, which is frequently not captured by the traditional DCF approach. However, the practical use of valuation techniques based on option pricing theory has been limited by the mathematical complexity of these techniques and the resulting lack of intuition associated with the solution process, or the restrictive assumptions required to obtain analytical solutions. The primary source of complexity in moving from traditional DCF to approaches using option theory is the requirement to model the evolution of uncertainty in the value of the underlying real asset over time as a stochastic process.This is required because the firm's decision-making is dynamic, not static and predetermined as in the case of DCF.It is further required that market-based information (i.e., information on prices of traded assets) be used to specify the parameters of the stochastic process for the underlying asset.If not, as is often the case with options on real projects, there is no information on the market's view of the risk associated with the project.Hence, there is no market-based guidance to use a basis for selecting the discount rate to be applied to cash flows.