Improving Investment Decisions Using a Stochastic ...

SPE 71414 Improving Investment Decisions Using a Stochastic Integrated Asset Model S.H. Begg and R.B. Bratvold, Landmark Graphics Corporation, and J.M. Campbell, International Risk Management Copyright 2001, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the 2001 SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, 30 September–3 October 2001. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

Summary This paper addresses the need for a holistic, integrated approach to assessing the impacts of uncertainty on oil and gas investment decision-making. We argue that this cannot be accomplished effectively by just adding a capability to deal with uncertainty to classical, rigorous models of all the components that contribute to an investment decision evaluation. Further, we suggest that such an approach, if feasible, is not desirable. Instead, we propose the concept of a Stochastic Integrated Asset Model (SIAM) embedded in a decision support system. This approach involves trading-off some technical rigor for a more complete and accurate assessment of the impacts of uncertainty on the investment decision-making process. The main elements of the system are: simplified component models for each domain; Monte Carlo simulation engine; modeling language for customization, incorporation of interdependencies between components, implementation of decision logic and updating information as a result of learning. We illustrate how such a system identifies which uncertainties impact the decision the most; values the acquisition of information (data, technical analysis) and encourages flexibility in go-forward plans to mitigate and/or exploit uncertainties. Further applications are to the optimization of development plans, real options valuation and the generation of consistent, risked cash flows for input to portfolio analysis. We believe that application of such a system results in a true value-driven focus to the work of multi-disciplinary asset teams through its ability to integrate the technical and business aspects of decisions.

Introduction Business Drivers. The motivation for this paper is rooted in two observations of the business climate surrounding the oil and gas industry. The first is that over the past two decades

many oil companies have consistently under-performed in returning forecasted economic metrics that were the basis of 1 their investment decisions . This implies a systematic overestimation of returns and/or under-estimation of the risks of loss. Indeed, there is growing evidence (see Fig. 1) of a direct relationship between consistent, accurate assessment of uncertainties and risks and the creation of value, resulting in 2 improved financial performance. Recent papers by Simpson 3 et. al. and Jonkman et. al. indicate that this is impacting current business practices. Decision–makers (whether large or small $ decisions) need to make smarter decisions using better assessments of value that capture uncertainty and their ability to make a trade-off between reducing it and managing its impacts. But, what is “better” and how do investors, such as stockholders and governments, define it? The second observation is that the search for improved margins, often by merger, down-sizing and reduced cycletimes, is making it imperative for many companies to do more with less. They need to make cheaper, faster decisions that use an appropriate level of technical analysis with the acquisition of appropriate data (type, quantity and quality). But what is “appropriate” and how is it defined in the context of uncertainty? Holistic approach to evaluation. Answering these questions requires a holistic approach that incorporates all of the key elements that may influence a decision combined with an integrated capability to assess the impacts of uncertainty. However, with current, and even projected, computation power it does not seem practical to develop such fully integrated models based on classical models of all domains that contribute to evaluating a decision. By “classical” we mean models whose focus is detail, precision and an ascomplete-as-possible modeling of the physical principles underlying each component of the system. Attempts to evaluate decisions using classical models often end up reducing the scope of the problem to make it tractable by, for example, concentrating on the subsurface aspects and ignoring the surface and/or commercial uncertainties, or by greatly simplifying the ability of the component models to interact. This can lead to an unwarranted confidence in the accuracy with which it is believed the uncertainty has been captured. 4 Saleri gives a good review of some of the pitfalls in using “complex” models and indicates that they do not necessarily provide better answers. Further, such an approach, even if it were possible, is not always economically justified. A lot of

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time and money can be spent on data and analysis, without always knowing if they will impact the decision. Instead, we propose to trade-off technical rigor for the ability to handle all facets of the problem, their interrelationship and the impact of uncertainties. The philosophy underlying this approach can be best summed up by the quote from John Maynard Keynes “I would rather be vaguely right than precisely wrong”. In this approach the emphasis is on striving for a holistic, accurate assessment of uncertainty rather than precise, over-modeling of those parts of the system we understand well. This principle means that technical analysis should strive to provide accurate assessment of the risks and opportunities associated with business decisions that result from uncertainty. The proposed tool to implement this approach is a Stochastic Integrated Asset Model (SIAM), which contains simplified (by current technical standards) models for all domains influencing an investment decision, combined with a rich modeling of their interactions and associated uncertainties. A SIAM is the integration of a systems model with Monte Carlo simulation embedded with decision analysis tools. Application of the approach is iterative. It starts with the decision to be made, how accurately the decision criteria need to be assessed, and then works backward to define “fit-forpurpose” data and analysis techniques. Classical modeling and/or additional data collection should only be exercised to resolve uncertainties in those variables identified as having the biggest impact on the decision criteria. Figure 2 illustrates the difference between the SIAM and “classical” approaches. Previous work. The literature addresses various elements (technology and/or implementation) of the approach outlined 2 3 above. Simpson and Jonkman provide two recent overviews 5-21 and a sampling is provided throughout this paper . With a few exceptions, most articles tend to focus on either one technology (e.g. Monte Carlo simulation or decision trees) or on a subset of the problem (e.g. OOIP or processing facilities) or lack true economic evaluation (e.g. reserves defined by process-based recovery factor). Though informative and useful, they lack the fully holistic approach that is required to ensure that the results and analyses are not over-whelmed by the impacts of uncertainty in some aspect of the problem that has been excluded. Or, the analysis focuses on uncertainty independently in various elements of the system and then tries to integrate them. This continuation of the classical reductionist approach misses important interactions and/or dependencies and consequently loses the opportunity to elucidate behaviors, that have not been specifically modeled, to emerge from the overall system. Non-holistic thinking results from a natural tendency for domain experts (geoscientists, reservoir engineers, drillers, facilities engineers) to view their part of the evaluation system as being central. Even economists can fall prey to this thinking - the objective is not the metrics, but decisions, and all other factors that influence them, leading to execution of strategies and tactics to reach corporate goals. In this respect, companies that use multi-disciplinary teams, that cover all domains, will have more success in adopting a truly holistic approach.

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Previous work also tends to be technology-decision driven rather than business-decision driven. The goal is seen to be uncertainty/risk assessment and reduction rather than placing it in a broader context of determining its relevance, or irrelevance, to the investment decision. Common applications of decision tree methodologies (such as for value-of-information calculations) are limited in that an expected value resulting from a decision tree does not 22 fully convey the risks associated with a particular decision . Misunderstanding of the meaning of an expected value may be the reason for a manager’s disappointment (and subsequent skepticism in the methodology) when, for example, the “expected” value of taking more seismic was not realized in their specific project. Also, techniques based on expectedvalue fail to estimate the full worth of projects where variability is exploited to create value, such as a scheme to accelerate/decelerate production as oil price increase/decreases. Finally, classical methodologies for calculating metrics, such as expected NPV, for risky cash-flows fail to account for factors such as The value associated with the ability to react as uncertainties are revealed over the course of a project. For example, the ability to curtail a proposed investment stream if STOOIP turns out to be lower than expected. The premium paid for a risky cash flow from the perspective of the investor. The changing nature of risk between the various cash flow elements and its variation over time. Futhermore, a classical analysis mindset is not conducive to exploiting uncertainty by developing value-creating options, such as investing in the infrastructure required allow processing capacity to be expanded at a later date to capture 23 upside potential. See Campbell et. al. for more detail on the shortcomings of classical approaches to NPV calculation. A SIAM-based approach seeks to synthesize elements of classical and modern decision methods suggested by many authors. This paper attempts to integrate these aspects into a technology solution that is more powerful than the sum of its parts and to present an approach to its application. The essence of this approach is to take as broad as possible a view of the system about which a decision is to be made, focusing on the impacts of uncertainty, and only resorting to more detailed and precise analyses when justified by evidence that it will make a material impact on the decisions to be made. Benefits. The main benefits of adopting such an approach are the ability to: Identify key decision drivers and thus reduce costs by knowing where to focus resources – see Figure 3 for a schematic. Estimate the value of more data/information/analysis in reducing uncertainty Determine the point where it is more economic to plan to manage uncertainty than to reduce it.

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IMPROVING INVESTMENT DECISIONS USING A STOCHASTIC INTEGRATED ASSET MODEL

Create value by generating options for a flexible response to uncertainties as they become resolved over time. We also believe that implementation of this approach will enhance the work of multi-disciplinary asset teams by enabling each member to see the relevance, or irrelevance, of their contribution to the decision at hand. Further, a SIAM can provide the model basis for system-wide optimization. Finally, its widespread application across a Business Unit or Corporation can provide the consistency of input required for higher-level portfolio-based decisions.

Components of SIAM System As outlined above, we are really advocating an evaluation system that has, at its core, a SIAM, see Figure 4. First we will describe some of the general characteristics the system should possess. Holistic. It should contain component models representative of all key aspects of the “system” about which decisions are to be made. Fast, Simple Models. An iteration of the overall model should execute in order of 1 second to enable full investigation of uncertainties, options and scenarios. Component models therefore need to have a rich set of input parameters, be relatively low on sophistication/rigor and execute quickly. Complex Dependencies. Logic is necessary to model the interactions between the component models to provide feedback between them as the simulation progresses (such as processing facility constraints on production and dependencies between cost and production schedules). This is a critical requirement that is all to often given only cursory attention. Simulation-Based. All variables that are uncertain in reality can be assigned probability distributions and the model executed in simulation mode. Simulation provides a mechanism for combining uncertainties and correctly evaluating the outcomes of non-linear systems. Dynamic, Adaptable. It should simulate the behavior of the system as it evolves (uncertainties are resolved, decisions are made and implemented) over time. This includes the ability to learn and adapt in response to that learning, for example, by updating uncertainty estimates and development choices. In this respect it is different from classical simulation where all uncertain variables are sampled and fixed at the start of the process. Flexibility and Customizability. No two assets are the same. Whilst templates may provide a good starting point, it must be possible to customize the system to the unique facets of each physical system and/or decision. Distinguish Uncertain and Decision Variables. Input variables are categorized as being “uncertain”, “decision”, or “fixed”. Both uncertain and decision variables can have ranges assigned to them. In the former case the ranges represent uncertainty (such as recovery factor, oil price) and in the latter they represent quantities that we are at liberty to choose (e.g. number of

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wells, pipeline diameter, processing capacity). The distinction becomes quite important when performing sensitivity analysis and optimization. Multiple Scenarios. The system should be able to manage multiple, distinct predictions of uncertain “states-of-nature” (e.g. depositional environment) and “states-of-the-world” (e.g. new LNG market) as well as distinct choices (e.g. TLP or FPSO). The above description advocates a stochastic systemsmodeling approach to the problem. Table 1 provides a brief comparison between the approach suggested here and the more usual approach of trying to wrap uncertainty around classical models. Although the differences may not be as black-and-white as Table 1 suggests, it suffices to give a good “flavor” of the differences. Figure 2 illustrates how the approaches evolve over time. In the following sections we outline major components that the system should contain. We will later illustrate its application and benefits by a partial implementation of the ideas using software that is commonly available. Simplified Domain Models. The goal of rapid execution requires that the component models of each domain be fairly simple. Also, Saleri4 provides a strong justification, based on accuracy, for trying to reduce complexity within component models. However, the models need to be scalable to capture different levels of sophistication depending on the decision to be made and the point one is at in the process. For example, the simple models required for evaluating an infill well would be differ significantly from a new field development. Multimillion cell models of the subsurface and recovery dynamics, or full surface-facility process-models violate this criterion. Methods for he improving the fidelity of simple models by using classical models to derive them or calibrate them are discussed below. Simplified models, ideally, should stem from a rich parameter set covering all significant factors that might influence the decision criteria. One form of such models already exists – the analytical models that were used in the industry before the advent of high-speed computing. The main domains are as follows. Resource Potential models cover typical G&G domains such as gross rock volume, fluid contacts and properties that control OOIP and flow. Development Plan models cover resources for drilling, processing facilities and export. They are used for two purposes. The first is to estimate costs and their timing. The second is to interact with resource and production models to provide constraints and timing for production. Production models cover forecasting of the rates of various fluid streams that can be achieved when the development models are applied to the resourcepotential models. The cost and production streams that result from these models are highly dependent on their interactions. We therefore reject approaches that perform separate analyses on any component and then later try to recombine them.

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Modeling Language. We agree with Saleri’s4 advocacy of complexity reduction within component models of the system. However, we do think it is necessary to try to preserve the complexity of interaction between models. A high-level modeling language (not programming language) is a key component for several reasons. It is the mechanism by which the desired customization, adaptability, complex dependencies and model evolution can be implemented. Further, it is required to enable an “if-then-else” kind of logic for making decisions along the way as the simulation progresses. The importance of these features of a modeling language is evidenced by the current popularity of spreadsheets Simulation Engine. Simulation is a key component for several reasons. First, information about the full distribution is needed to fully understand uncertainty in the output variables (value metrics), not just an expected value and/or standard deviation. Second, the individual domain models and their interactions often exhibit non-linear behaviors. Simulation integrates the impacts of the uncertainties in the inputs. Further, true expected values, if they are the objective, cannot be derived from the expected values of input parameters for the kinds of non-linear models observed in nature and in the market place. (OOIP calculations with fluid contacts, reservoir simulation and option pricing are all non-linear models.) Third, simulation generates the information needed to calculate the sensitivity of the output variables to uncertainty in the input variables, whilst account for the dependencies between the latter. Finally, exploiting variability in the input parameters, like oil price, requires demonstrating the value of a development scheme that allows production volumes to change with rising or falling prices. Ignoring price fluctuations shifts the focus towards uncertainty in the long-term trend of the average and thus fail to capture the value associated with price-sensitive options. We will return to this example later to illustrate some of the applications of a SIAM-based system. Scheduling. Scheduling receives special emphasis within a SIAM as a separate, though integrated, component of the system for two reasons. First, our ability to choose the time of decisions (to acquire information, drill wells, build facilities) is often neglected, yet is crucial to the ability manage risk and create value from projects. Second, scheduling ensures that the system accounts explicitly for constraints such as rig availability, weather impacts and for interactions between domain models such as limitations on oil production due to gas/water processing capacities. Economics. We break economics out here, rather than treat it as a domain model, since it is the consumer of the scheduled cost and production information generated by the resource, drilling, processing facility, export and fiscal models. There is an ever-growing literature about the limitations of classical techniques, for valuing the cash flows23-29 from risky projects. To calculate the value of flexibility, and consider value from the perspective of the investors, it is necessary to extend

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classical techniques to include the ideas of Modern Investment Valuation1,25, such as real options. These techniques account for risk directly within the cash flows (and, if necessary, may differ for each element of the cash flow) rather than being subsumed within a discount rate derived from heuristics, weighted average cost of capital or capital asset pricing model. They also allow risks of the cash flows to be priced by market instruments. Decision Support System. This term is defined here to be a collection of tools to aid interpretation of the results of the SIAM output and consequent decision-making. (This is distinct from the decision-making capabilities within the SIAM – see above sections on Modeling Language, Dynamic & Adaptable, Choice & Decision Variables). The tool set includes such classical techniques as framing tools, strategy tables and decision trees26 for comparing the expected value of alternative scenarios, consistent with the decision-maker’s risk attitudes30. Decision trees also form the basis of one methodology for implementing real option valuation techniques31-32. However, as mentioned previously, the expected value resulting from a decision tree is limited in that it does not fully convey the risks associated with a particular decision.. Hence the need to add a simulation component which can generate a distribution of values at each end node of the tree and to provide the ability to roll these up to an overall profile of uncertainty for the project. Decisions are often made on multiple criteria and so the system requires a technique for multi-criteria decision-analysis such as outlined by Saaty33-34. Further, not all factors that influence a decision are amenable to a probabilistic (subjective or objective) approach. Inclusion of the techniques described by Hall et.al.35 provides a formal procedure for incorporating, and analyzing the impact of, factors that are described linguistically. For example, “X is very important”, “and it depends on Y happening to some extent”, “but we don’t have much evidence for Y occurring”. Anecdotes also abound of where a future outcome was not even amongst the possibilities considered and assigned probabilities. Through its use of an “evidence–for” and “evidence-against” approach, this methodology has the additional advantage of forcing the evaluators to recognize, and explicitly account for, the fact that there may be outcomes that they have not included in their analysis. Finally, high-performance data analysis techniques are required for rapid exploration of the multi-dimensional data resulting from the simulations and for the extraction and understanding of key dependencies between input and output parameters. Going beyond traditional sensitivity or Tornado plots to understand the multi-dimensional nature of the sensitivity of uncertainty in decision criteria (such as NPV) to uncertainty in input parameters is critical. User and Data Interaction. Ideally the system should have a user-interface that is part graphically driven, part “spreadsheet-like” and exposes the modeling language. For example, graphical influence diagrams can be used to

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understand the structure of the decision by clearly identifying dependencies. They can then be used to drive the creation of a consistent SIAM along with a graphical tool for building the “development plan” for the decision being evaluated. By “development plan” we mean both its traditional use for new field developments and major re-development projects as well as for smaller-scale projects such as sidetracks, in-fills and new data acquisition. A spreadsheet style of interface is preferred, for example, for building component models that are based on table-lookups such as facility component sizes and costs as a function of production rates. A “control center” is required to set up and manage the various scenarios and options to be evaluated by the SIAM, and to link its output to the decision tools for choosing between them. It should also manage the ties to classical models which can be used to calibrate the SIAM componentmodels, estimate uncertainties in their input parameters or provide alternative models (see next section for more detail). Ideally, the system should also enable classical models, in place of the simplified component models, to be executed by the simulation engine. Persistent data storage is clearly required for both the input and output, but the nature of the data and the way the system will be used imposes requirements beyond typical current solutions. For example, it must support the ability to track the input/output data associated with alternative scenarios and options within a scenario. The data structures themselves need to be able to comprehend probabilistic data as a type, multiple realizations (and their input data) and consistency of data that is common across a portfolio of assets (e.g. oil price) or “states-of-nature” (e.g. source rock). Finally, it must support efficient exploration by data analysis tools.

Tie to Classical Models The SIAM-based approach advocates starting with as broad an analysis as possible, with a goal of understanding the impacts of uncertainty. Classical domain models still contribute once it has been established that there is value in reducing uncertainty or increasing the depth of analysis provided by the SIAM system. Even then, as illustrated in Figure 5, due consideration should be given, as a function of the decisions to be made, to the level of detail and rigor required. There are three ways that classical domain models can be used to improve a SIAM. Uncertainty Estimates. The simplest use of rich models is in making multiple runs to generate bounds, or provide some data points, from which uncertainties in the input parameters of the SIAM can be estimated. For example, say that Gross Rock Volume (GRV) is an input to a SIAM (rather than a component model of a SIAM). A number of different timedepth conversion models combined, with traditional mapping of surfaces, can be used to provide some date points from which a GRV PDF can be inferred. Likewise, a range of reservoir simulation runs could provide an estimate of recovery efficiency PDF. Generally, the classical models do not provide the PDFs directly in this approach. Better

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uncertainty estimation will emerge if it is considered as an interpretative exercise, with the classical models providing some data points. Other factors need to be considered, such as a recognition that the use of simplified models itself induces uncertainty and that the very nature of classical-modeling can lead to over-confidence in the accuracy with which uncertainties have been assessed. Calibration. We can go beyond mere uncertainty estimates and try to calibrate the simplified component models. The aim here is to improve the fidelity with which the simplified models represent the real world, rather than assess the uncertainty in their input parameters. We will use the calculation of OOIP of the resource as an example. Assume the simplified model consists of a straightforward geometricalbody representation, of the volume to be exploited. This could be as simple as a cylinder for an in-fill well or a combination of bodies that represent a whole field. Rather than use average property data from the wells (porosity, saturation, etc) as input to calculate the OOIP of the simple model, we can back out “effective” properties from a multi-million geo-cellular model that incorporates all the information about structure, fluid contacts property trends and their correlations. In this case the simple model’s GRV, OOIP, HCPV etc will match exactly those of the rich model. Uncertainties can then be assigned to these effective values, as above, recognizing dependencies between them. The application of calibration can be extended beyond the individual domain models to the overall results of the SIAM. In this case we could take the cumulative distribution of NPV and select, say, the input data for the 11 deciles (p0, p10, p20…etc) – see Figure 6. We could then run the rich model equivalents of these and calculate new NPV’s that are used to calibrate the CDF from the SIAM. Due to multi-dimensional correlations and the vagaries of “what is the p10 equivalent rich model?” this procedure still does not guarantee a true CDF but should yield better estimate. Generating Simple Surrogates. The goal is to derive simple parametric equations that characterize the changing response of the rich model to changing input parameters. Good examples are characterizing recovery efficiency or breakthrough time using a reservoir simulator. The procedure uses experimental design techniques to identify a minimum number of cases that will yield maximum information regarding how the simulator output varies as a function of its inputs. Analysis of variance is then used to identify the most sensitive input parameters. Next, experimental design is applied again to define a second series of runs, this time focusing on the variability of the previously-identified key input parameters. Finally, a multivariate regression technique is used to generate the coefficients of a polynomial equation that relates the response of interest to input key input parameters. We call this a simple surrogate for the rich model. This simple surrogate can then be used as one of the domain models in the SIAM and uncertainties assigned to its input parameters. 36 37 17 Papers by Jones , Narayanan and Corre provide examples and more in-depth discussion. The latter reference

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shows how the techniques can be applied to time-varying responses, such as cumulative production. We will later suggest how these same techniques can be used to represent the whole SIAM by a response surface, which is then used in a portfolio system.

Implementation. An example high-level workflow for implementing a SIAMbased system is presented in this section. The approach starts with the decision to be made, how accurately it needs to be assessed, and then works back toward defining “fit-forpurpose” data and analysis techniques. We then propose an iterative, scalable approach, getting progressively more rigorous and precise only when the value of doing so is proven. The first step uses decision-framing tools to comprehend the key decisions and the options and/or strategies to be evaluated. The structure of each strategy/option is then outlined using the influence diagrams with a special emphasis placed on eliciting dependencies between the elements. The influence diagram provides an outline for constructing the SIAM. Previous models and/or templates can be used as a starting point, and the modeling language employed to customize them to the specific projects to be evaluated. Input variables are categorized as “uncertain”, “decision”, or “fixed”. A simple one-at-a-time sensitivity analysis is carried out on the “uncertain” variables to determine which are likely to have the biggest impact. PDF’s are then assigned to these variables using currently available data and/or analysis. The SIAM is then run to generate a PDF of the decision metrics (e.g. NPV). Within the run, the “decision” variables may be used for optimization (e.g. number of wells, platform position) or this step deferred until a decision is reached. At a minimum, a sensitivity analysis of the “decision” variables identifies the key levers for changing project value. A multivariate sensitivity analysis is then performed on the “uncertain” parameters in order to determine which ones contribute most to the uncertainty in the output metric. A determination is made as to whether a decision can be made now or if more detailed analysis or information is required. If the latter, this should focus on the most important variables. Where justified, the SIAM is then updated both in terms of its component models (by the calibration procedures outlined above) and for better estimates of the uncertainties in the input parameters. The above process is repeated until such time that the uncertainty in the decision-criteria is reduced sufficiently to make a decision. The emphasis then switches to optimizing around the decision that has been made and developing plans to mitigate the remaining uncertainty. The SIAM model is not static over the life of an investment. The component models should become more detailed as uncertainties in particular parts of the system are proven to have significant impact, as shown in Figure 2. Effective implementation of this approach may require some change management in terms of rewarding decision processes and/or long term outcomes rather than the outcome of a single decision.

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Applications. The SIAM-based system has a wide range of applications, as indicated below. Moreover, these applications can be made to a wide range of investment decisions, from exploration and appraisal through to infill drilling and work-overs in producing fields. In each case the key is to build a holistic model and focus on a complete and accurate assessment of the impacts of uncertainty. To construct our illustrative examples, we have used DPL for influence diagrams and decision trees, Excel for model building and CrystalBall for simulation, because no commercially available software combines all SIAM elements, to our knowledge. Focusing Asset Teams. The holistic nature of the system enables asset teams to identify the true decision-drivers through sensitivity analysis. If only part of the system is modeled it is impossible to know whether or not there might be other factors that have a bigger impact. All impacts are also measured in economic rather than technical terms (dollars rather than OOIP or reserves). Two types of decision-driver can be identified Uncertainty Drivers are those model parameters whose uncertainty in value has the biggest impact on uncertainty in the decision metrics. For example, porosity, oil price, seismic velocity, GOR. Value Levers are those variables whose values the team can choose and which have the biggest impact on the decision metrics. For example, Production Sharing Contract terms, steel price, peak processing capacity In carrying out and interpreting the sensitivity analysis it is important to distinguish between these two types. It is also important to delve beyond the simple “tornado” style sensitivity charts to investigate the multi-dimensional nature of the sensitivities, that is, a key driver may be important only for particular combinations of other variables. The value of this type of analysis to asset teams is twofold. First, it gives an indication of where to focus human resources for further analysis and where to focus dollar resources on acquisition of more (a more rigorous approach is described in the next section). Secondly, it can create a more businessdriven atmosphere in the team through each member knowing the relevance, or irrelevance, of their contribution. It is our observation that technical specialists are willing to accept a “fit-for-purpose” analysis when that can, in fact, be demonstrated to be sufficient. Without this evidence there is a natural tendency to do the best job possible, often leading to a confusion between accuracy and precision. Scenario Analysis. The distinction between scenario analysis and sensitivity analysis around decision variables is not absolute. We characterize scenario analysis as the evaluation of discrete and significantly different alternatives whereas sensitivity analysis is more like variations on a theme. This applies to both uncertain variables (“state-of-the-world” scenarios) and decision variables (alternative choice scenarios). For example, different depositional environment

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interpretations, or choice between an FPSO and a TLP would be different scenarios. Extent of permeability continuity or choice of number of wells on a platform would be sensitivities. Generally, a different, or variant, SIAM would be required for each scenario whereas sensitivities can be investigated merely by putting ranges on the decision variables of a given SIAM. In practical terms, a SIAM would be built for each scenario considered and used to generate PDFs of the decision criteria metrics. Different scenarios are evaluated using a decision tree and/or multi-criteria decision analysis technique. The former is preferable where there is a hierarchy of 16 decisions. See Floris & Peersman for a fuller discussion of scenario analysis. Real Options Thinking. There has been a lot of interest, in the literature at least (ref list), in the topic of real options. 1,25 Modern Investment Valuation (MIV) and its special case, real options, is part of a discipline, called contingent claims 38 analysis . These methods emphasize that valuing investments is contingent on other conditions. A good analogy regarding MIV compares the classical DCF processes and MIV to buying a lottery ticket and playing poker. In the lottery, the buyer buys a ticket (place their bets) and waits for the outcome. On the other hand, poker permits the gambler to put a small amount down and then keep increasing the bet contingent on each round of cards. Classical DCF investment decision evaluation assumes the investor is playing the lottery, while the modern approach follows the poker analogy. Implicit in the approach is the idea that the opportunity to change the bet (or invest) over time conveys benefits by altering risk or improving profitability. We distinguish between real options thinking and real options valuation. Real options thinking focuses on assessing the value of the option to acquire information to reduce uncertainty and the value of flexibility (options) to exploit, or at least mitigate, the impacts of uncertainties as they are resolved. In this sense it can be considered as an extension of traditional decision analysis techniques. We reserve the term real options valuation to mean calculating the value of risky cash flows from the perspective of an external investor, where the risk is priced using a portfolio of openly traded market instruments that carry a similar level of risk. Classical methods of calculating NPV not only ignore the value of real options thinking and valuation, they penalize any delay in making the investment. Experienced decision makers who use NPV are well aware of these limitations and often use “gut feel”, or “strategic considerations” to compensate. Note that his argument merely extends NPV, and other metrics that result from a discounted cash flow analysis, and the traditional methods of calculating them to include the value of options and to properly evaluate the impact of risk. Real options thinking therefore provides some tools for quantifying “gut feel” and “strategic leverage” by objective valuation. Value of Information (VoI). The use of VoI has been growing in the Exploration and Production business. Its essence is to calculate the expected value of acquiring information, or doing more analysis, given its cost. For

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example, estimating the value of acquiring 3D seismic during appraisal. Rarely is the information perfect (if information says X, then X will be found) it is more likely to be imperfect (if the information says X there is a finite probability that X will not be found). The method is based on Bayesian revision of probabilities, normally calculated by using decision trees by comparing the expected project value without the information to that with it. We will not go into detail here, but refer the 39-42 reader to . The word “expected” is key. It implies that the VoI is that which would be expected in the long run with the consistent application of the methodology. It does not imply that the value will be realized for a particular decision. In the system described here, the necessary Bayesian updating is performed automatically by a simple “click and drag” to reverse decision and outcome nodes. This obviates the need for the user to calculate the updated probabilities, which we suspect is a significant factor preventing wider use of the technique. Typical application of the technique has moved from a means of justifying further data collection and analysis to reduce uncertainty, to providing a guide as to when it is more economic to go ahead, make the investment decision, and live with the consequences – the lottery analogy. However, it is possible to do better than just “live with the consequences”. This brings us to poker. Value of Flexibility (VoF). It is possible do better by creating options to react as uncertainties are revealed over the course of an investment. This concept is not as well established as VoI, so we define the term VoF in an analogous sense, except that the goal is to value these options to react as opposed to valuing the option to reduce uncertainty. In terms of the poker analogy, we can fold, stick or increase our bet after the first round. There are three aspects to “doing better”. The first is to develop options to mitigate the risks arising from negative impacts of uncertainty. For example, flexibility is created by deciding to install separate platforms for the producing wells and processing facilities and by delaying final specification of processing capacity until after a first phase of development drilling is complete. Then, if it is discovered that OOIP is at the low end of the predicted range (see Figure 7) we can scale back accordingly, or in an extreme case, exit. The issue to be evaluated is, given the uncertainty in OOIP, what is the value of waiting to make the final processing capacity choice worth compared to the value lost by delaying the onset of production? (A classical NPV analysis would only show the value lost.) If the net value is positive then we have raised the expected NPV. The second aspect is creating options to capture the upside. Planning to respond to much larger OOIP than expected (see Figure 7) also provides flexibility by deciding to pay some up-front costs for extra well slots on a platform, as well as waiting on the final decision for processing capacity. The issue to be evaluated is, given the uncertainty in OOIP, what is the value of the option to increase the number of producing wells compared to the cost of the slots (which have a finite probability of not being used). A positive net value raises the expected NPV.

8


In the examples above, if there had been no uncertainty there would have been no scope to create options to raise the expected NPV. It is in this way that, perhaps counterintuitively, uncertainty can add value if one has a capability to react appropriately. The core of real options thinking lies in devising such capabilities, evaluating their cost-benefit and then executing them. The third aspect of VoF is in planning to exploit variability. For example, what would be the net value of devising a processing and export facility the enabled production to be accelerated when oil prices are higher and decelerated when lower? Table 2 summarizes some of the input data for a SIAM model constructed to evaluate this question (a full description of this model would comprise a complete article). Key elements are an assumption of a mean reverting oil price with zero growth or decline in its long term trend and min and max caps on the “theoretical” variable facilities. Without any ability to relate production to oil price the expected NPV was $410m. Adding the capability just to decelerate production when prices are low raised this to $427m. Adding acceleration capacity raises the NPV to $497. Thus, we could consider spending up to $87m to devise such a scheme. ($m refers to million dollars) Of course, it is not possible to have continuously expandable tankers and processing facilities! However, the potential value of such a capability combined with the an options mindset could lead to a feasible solution such as contracting for an option on extra tankerage and a scalable battery of processing facilities. If the only uncertainty in oil price was the uncertainty in the drift of its long term mean there would be no value from such a scheme. However, recognizing, and modeling, short-term variations around that mean exposed a source of value creation. This was made possible by the simulation capability of the system. Executives desiring employees to “think out of the box” should realize that the common valuation system discourages, and, often, rejects such efforts. A shift in thinking from Value-of-Information to Value-ofFlexibility is key to starting down the path to improved investment decisions. Moreover, this kind of thinking may change the point at which VoI indicates it is better to make the investment decision. One possible outcome is that there is more value to be gained from the cost of exploiting the uncertainty than from the cost of reducing it. Real options thinking creates a positive mindset around facing and dealing with uncertainty rather than the usual negative reaction one Given the importance of creativity, classical decision analysis tools, such as influence diagrams and strategy tables, implemented with a process that encourages expansive 26 framing of the problem , are key components of the proposed evaluation system. The combination of decision tree and simulation capability provides the tools required for the calculations. The procedure is to compare the expected project values with and without flexibility and/or new information. Timing is a key component of this, hence our emphasis on the importance of scheduling and ensuring that dependencies are fully captured.

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Real Options Valuation. In the sections above the emphasis was on using established technologies with new thought processes to adapt traditional DCF analysis. A further step is to leverage the ideas under-pinning the valuation of financial options. The advantages of doing so are that it overcomes some of the limitations of classical NPV by simplifying the choice of the all important discount rate allowing different elements of the cash flow to be risked differently using the market to value risk There are two broad approaches to implementing these ideas. The first tries to find an analogy between the real option problem and a financial option, and then use the analytical 24,31,32 is a decision solution techniques of the latter. The second tree approach in which market risks (e.g. oil price) are accounted for by adjusting probabilities to reflect the premium paid by the market for equivalent levels of risk and private risks (e.g. recovery factor) are accounted for by traditional direct estimation of their probabilities. The value of the various options are then calculated by “rolling-back” the decision tree using dynamic programming. In each case, risk is no longer accounted for in the discount rate, which is chosen to be at or near the risk-free rate. We feel that the former approach is more limiting in that only a small number of uncertainties can be dealt with by the analytic solution techniques. It also tends to lead to a “force the problem to fit the tool” mentality. We believe the second approach is more amenable to adoption because it based on tools that many in the industry are already familiar with. It can easily be implemented in our proposed evaluation system. However, we also see a variant on this approach where the problem is structured similarly to a decision tree, but is solved by simulation with an optimization step. Although this is subject of further work, we believe it is an even simpler way to achieve the benefits of real options valuation. Classical DCF and NPV analysis took 20-30 years to replace payback 27 period as the norm for valuing corporate investment decisions. With this history, it is unlikely that real options valuation techniques will be adopted rapidly. Decision Optimization. Within the evaluation process there is the opportunity to perform optimization at two points. Either within the SIAM simulation process itself whilst evaluating different alternatives, or after it has occurred to optimize the chosen decision. To do this we take advantage of the ability of sensitivity analysis to identify those decision variables that are key Value Levers (see above). These Value Levers are the parameters we can change to optimize the decision. The holistic nature of the SIAM modeling process helps to ensure that optimization is performed against an objective function that incorporates the impacts of a wide range of factors and dependencies that influence value. There is much scope for optimizing decisions. For 10 example, the methodologies proposed by Ding and Startzman 44 and Iyer et.al. for optimizing well and platform numbers and locations could be implemented.

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Portfolio Management. The final application of the system discussed here is its capacity to provide the input required by 45risk-based portfolio analysis and/or optimization techniques 48 . These techniques can be used to allocate capital either between competing investment alternatives within an asset or between the assets themselves. When it comes to allocation between assets, we recognize that many companies resort to ranking-based portfolio decisions because of philosophy or through organizational barriers to implementation of a more rigorous approach. None-the-less, this is another area where growing interest is being displayed by the industry. There are two main ways in which our system can contribute to the input to a risk-based portfolio analysis/optimization system. The first is that it can supply the necessary PDFs for the value metrics (NPV, NPV/INV, etc) and can also provide distributions for the cash flows on which they are based. SIAM provides better estimates of these metrics for the various reasons outlined earlier. Secondly, its consistent application across all projects/assets contributing to the portfolio helps assure the normalized input that is a necessary condition for the success of portfolio analysis and optimization techniques. Finally, we have never seen any public portfolio optimization system that is capable of incorporating and manipulating the options associated with each asset. There is merit in applying the concepts behind experimental design and response surface modeling that were outlined in the “Generating Simple Surrogates” section. However, instead of applying them to classical technical models to generate a simple model for insertion in SIAM, they could be applied to the SIAM itself to develop a simple model, including options that can be exported to a portfolio. Whilst some may be alarmed at this nested reduction in model detail, we contend that it would carry much more information to the portfolio level than any current approach.

Limitations and Direction for Future Work We have already discussed requirements for further development around, multi-dimensional sensitivity analysis, decision analysis techniques that can incorporate “soft” heuristic and linguistic input, implementation of real options analysis, application of SIAMs to portfolio analysis and optimization. In addition to these topics there is a need to develop higher fidelity simple component models, whilst retaining complex dependencies between them and maintaining accessibility, speed of execution, robustness and interpretability. Also, more emphasis needs to be placed on incorporating metrics appropriate for investors whose definition of a return on investment is not measured in monetary terms. Spreadsheet issues. Through our own work we are well aware of both the benefits and drawbacks of spreadsheets. The benefits, particularly their flexibility, customizability and widespread acceptance, are well known. Their drawbacks are less often discussed. The first major drawback is their extensibility, particularly in a multi-user, multi-site, linked-

9

spreadsheet environment. Unless extreme care is taken over control of updates, it is easy for a user to modify one sheet in such a way that it mis-ties the linkage to other sheets. Its like having access to raw computer code – which is also a strong part of spreadsheets’ appeal. When dealing with highly integrated models with multiple dependencies, multiple data types, varying over time and multiple simulation runs, one quickly reaches the limits of what spreadsheets can do and the efficiency with which they do it. Second is the issue of auditability and error-checking. There has been a surprising amount of rigorous research carried out on the quantity and types of spreadsheet errors. The results are alarming, showing high error rates in even single-user simple-problem spreadsheets. Considering just cell entry errors (as opposed to errors of structure or logic) 49 Panko reports “Every study that has attempted to measure errors, without exception, has found them at rates that would be unacceptable in any organization. These error rates, furthermore, are completely consistent with error rates found in other human activities. With such high cell error rates, most large spreadsheets will have multiple errors …”. Our experience supports this finding. We recently found several serious errors used to justify multi-billion $ decisions. Correcting the errors reduced the value of the investment by more than 50%. The challenge is to develop systems that incorporate the main benefits of spreadsheets, especially their flexibility, whilst overcoming their deficiencies. A modeling language provides more flexibility, consistency and control, but at the expense of requiring users to go through a learning curve.

Conclusions The SIAM-based investment evaluation system seeks to integrate improvements in decision processes, valuation methods, and formal risk assessment, made possible by the far greater processing capabilities available to professionals today. Many of the current solution methods originated from the need to simplify or reduce complex systems to a manageable level when the slide rule or calculator was the dominant tool. A SIAM seeks to utilize the power of current systems to recombine the information into a holistic framework that allows professionals to understand and trade-off the uncertainties inherent in the entire system, not just the traditional or popular components. The holistic approach improves the investors’ chances of truly integrating the technical to business decision-making process, ensuring a proper balance between every component within the system; thereby minimizing the danger that sound analysis will be wiped out by ignored, poorly thought out, or omitted uncertainties elsewhere. The proposed system enables several decades of evolving improvements to classical evaluation and decision-making techniques to be implemented in a logical, unified process that fits the changing employee and investment dynamics of our industry. We have described how it can deliver faster, cheaper, and above all, better investment decisions, and demonstrated

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this with an example of how uncertainty can be exploited to create value by adding flexibility to development plans.

9.

Acknowledgments The ideas presented in this paper were not developed in a vacuum and reflect the input of many colleagues we have interacted with. Any errors are ours. We particularly thank Scot Evans, Stan Cullick and Ian Beck of Landmark, Prof. Jim Dyer of UT Austin and Gardner Walkup of Applied Decision Analysis.

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Jonkman, R.M., Bos, C.F.M., Breunese, J.N., Morgan, D.T.K., Spencer, J.A., and Søndenå, E.: “Best Practices and Methods in Hydrocarbon Resource Estimation, Production and Emissions Forecasting, Uncertainty Evaluation and Decision Making,” paper SPE 65144 presented at the 2000 SPE European Petroleum Conference held in Paris, FR, Oct. 24-25.

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Peterson, S.K., Murtha, J.A., and Schneider, F.F., “Risk Analysis and Monte Carlo Simulation Applied to the Generation of Drilling AFE Estimates,” paper SPE 26339 presented at the 1993 68th Technical Conference and Exhibition of the Society of Petroleum Engineers, Houston, TX, Oct. 3-6.

10. Ding, Z.X., and Startzman, R.A.: “A Software System for Oilfield Facility Investment Minimization,” SPE Computer Applications (Aug. 1996) 111. 11. Moore, L.R., and Mudford, B.S.: “Probabilistic Play Analysis from Geoscience to Economics: An Example from the Gulf of Mexico,” paper SPE 52955 presented at the 1999 SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, TX, Mar. 20-23. 12. Edwards, R.A., and Hewett, T.A.: “Quantification of Production Prediction Uncertainty and Its Impact on the Management of Oil and Gas Price Risk,” paper SPE 28330 presented at the 1994 SPE 69th Annual Technical Conference and Exhibition held in New Orleans, LA, Sept. 25-28. 13.

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Basin, Venezuela: Case Study,” paper SPE 38805 presented at the 1997 ATCE, San Antonio, TX, Oct. 5-8. 21.

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Kitchel, B.G., Moore, S.O., Banks, W.H. and Borland, B.M.: “Probabilistic Drilling-Cost Estimating,” SPE Computer Applications (Aug 1997) 121. Mudford, B.S.: “Valuing and Comparing Oil and Gas Opportunities: A Comparison of Decision Tree and Simulation Methodologies,” paper SPE 63201 presented at the 2000 Annual Technical Conference and Exhibition, Dallas, TX, Oct. 1-4.

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35. Hall, J.W, Blockley, D.I., and Davis, J.P. 1998 "Uncertain Inference using Interval Probability Theory", Int J Approx Reasoning, 19, No 3-4, 247-26 36. Jones, A.D.W., Al-Qabandi, S., Reddick, C.E., and Anderson, S.A.: “Rapid Assessment of Pattern Waterflooding Uncertainty in a Giant Oil Reservoir,” paper SPE 38890 presented at the 1997 SPE Annual Technical Conference and Exhibition, San Antonio, TX, Oct 5-8. 37. Narayanan, K., White, C.D., Lake, L.W., and Willis, B.J.: “Response Surface Methods for Up-scaling Heterogeneous Geologic Models,” paper SPE 51923 presented at the 1999 SPE Reservoir Simulation Symposium, Houston, TX, Feb. 14-17. 38. Mason, S. and Merton, R. “The Role of Contingent Claims Analysis in Corporate Finance.” In E. I. Altman and M. G. Subrahmanyam (editors), Recent Advances in Corporate Finance, Homewood, IL: 39. Richard D. Irwin, 1985. Head, K.J.: “How Could You Possibly Predict the Value of 3-D Seismic Before You Shoot It?,” paper SPE 56446 presented at 1998 GeoTriad Convention, Calgary, Canada, edited in World Oil, (Oct. 1998) 97-100.

27. Copeland, T. and Antikarov, V.: “Real Options: a practitioners guide”, Texere, New York City (2001).

40. Demirmen, F.: “Subsurface Appraisal: The Road From Reservoir Uncertainty to Better Economics,” paper SPE 68603 presented at the 2001 Hydrocarbon Economics and Evaluation Symposium, Dallas, TX, Apr. 2-3.

28. Chorn, L.G. and Croft, M.: “Resolving Reservoir Uncertainty to Create Value,” paper SPE 49094 presented at the Annual Technical Conference and Exhibition, New Orleans, LA, Sept. 27-30.

41. Koninx, J-P.: “Value-of-Information – from Cost-Cutting to Value-Creation,” paper SPE 64390 presented at the 2000 Asia Pacific Oil and Gas Conference and Exhibition, Brisbane, Australia, Oct. 16-18.

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42. Demirmen, F.: “Use of “Value of Information” Concept in Justification and Ranking of Subsurface Appraisal,” paper SPE 36631 presented at the 1998 Annual Technical Conference and Exhibition, Denver, CO, Oct. 6-9.

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43. Wallace, S.W., Helgesen, C., and Nystad, A.: “Production Profiles for Oil Fields”, (1985) CMI Report no. 852310-8, Chr. Michelsen Institute, Bergen. 44. Iyer, R.R, Grossmann, I.E.,Vasantharajan,S. and Cullick, S.aA.: “Optimal Planning and Scheduling of Offshore Oil Field Infrastructure Investment and Operations,” Ind. Eng. Chem. Res (1998) 37, 1380-1397. 45. Howell, J.I. and Tyler, P.A.: “Using Portfolio Analysis to Develop Corporate Strategy,” paper SPE 68576 presented at the 2001 Hydrocarbon Economics and Evaluation Symposium, Dallas, TX, April. 2-3. 46. Brashear, J.P., Becker, A.B. and Gabriel, S.A.: “Interdependencies Among E&P Projects and Portfolio Risk Management,” paper SPE 56574 presented at the 1999 Annual Technical Conference and Exhibition held in Houston, TX, Oct. 3-6.

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47. Orman, M.M. and Duggan, T.E.: “Applying Modern Portfolio Theory to Upstream Investment Decision Making,” paper SPE 54774 revised for publication from paper SPE 49095 prepared for the 1998 Annual Technical Conference and Exhibition, New Orleans, LA, Sept. 2730.

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SPE 22016 presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, Texas, April 11-12. 49. Panko, R.R.: “What We Know About Spreadsheet Errors,” Journal of End User Computing, Special issue on Scaling Up End User Development, (Spring 1998) 15-21

48. Hightower, M.L. and David, A “ Portfolio Modeling: A Technique for Sophisticated Oil and Gas Investors” Paper

Table 1. Comparison of SIAM-based and Classical Evaluation Systems Number of iterations Type of iteration Time to execute Evolution of model over time Decision Analysis Decision Focus (Metrics) Scope of Domains Covered Emphasis on timing, scheduling Goals Detail with which physical behavior of system is modeled Ability to model complexity of interactions/feedback Economics Flexibility & Customizability

SIAM System 1000-10000 Full Monte Carlo - entirely automatic Minutes-Hour Updated uncertainties, calibration of simple models, richer dependencies between component models Integrated

Classical 10-100 Rapid updates of model, handcrafting/interpretation, alternative scenarios Hours-Weeks New data/wells, increasing resolution, better modeling of physical processes Separate

Business (NPV, ROI)

Technology (OOIP, Production)

Whole

Partial

High

Low-Medium

Accurate assessment of uncertainty and its impacts

Get a better answer

Low - Medium

High

High

Low-Medium

Integrated

Export to "3rd Party"

Modeling Language Portfolio Analysis and/or Down-stream value-chain linkage Optimization

Workflow scripting SIAM system, Stand-alone Economics, Decision Analysis

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13

Table 2. Selection of Input Data for SIAM built to Calculate Value of Price-Produce Flexibility Calibrated Reservoir Data Main Main Effective Area, sq mi Main Effective Thickness, ft Satellite 1 Prob{Oil} S1 Effective Area S1 EffectiveThickness Satellite 2 Prob{Oil} S2 Effective Area S2 Effective Thickness Effective Porosity Effective Water Saturation Effective Net:Gross Effective Formation Volume factor Recovery Factor of Process Well oil rate, kb per day

Before Appraisal During Appraisal After Appraisal Min ML Max Min ML Max Min ML Max 15 30 45 20 30 40 20 30 190 200 210 190 200 210 190 200

40 210

0.7 0.8 0.9 1 2 3 1 2 5 10 20 8 12 18 8 12 100 130 200 150 170 190 150 170

3 18 190

0.3 10 90 0.18 0.30 0.60 1.15 0.10 2.00

-2 15 100 0.21 0.32 0.7 1.2 0.3 5.00

-1 20 120 0.23 0.37 0.73 1.25 0.35 7.00

Producing Wells Cost to Drill & Complete Cost reduction factor per well Max number of wells to apply learning Tangibles % of total cost Initial time to drill a well, months Time reduction factor per well Max number of wells to apply learning Dry Hole Cost as % producing well cost Probability of dry hole LOC per well Number of Rigs (wells that can be drilled in parallel) - max of 4

Type p10 ML u 8 10 u 5% 10% f f u 4 5 u 0% 5% f f f f f 2

p90 13 15%

Operating Expenses Oil, $/bbl Water, $/bbl Gas, $/scf Producing Platforms Oil, $/bbl (plat&pipe) Water, $/bbl Gas, $/scf Export OpEx/Tariff, $/bbl

Type p10 ML p90 u 1.8 2 2.2 u 2.8 3 3.2 u 0 0 0.003 u 0.5 0.75 1 u 0.5 0.75 1 u 0 0 0.0006 u 3.8 4 4.2

0.5 15 100 0.20 0.35 0.75 1.20 0.20 4.00

0.7 20 120 0.22 0.45 0.80 1.25 0.50 ####

-3 10 90 0.2 0.3 0.68 1.15 0.20 3.00

Processing Facilty

-2 15 100 0.21 0.32 0.7 1.2 0.25 5.00

-1 20 120 0.23 0.37 0.73 1.25 0.45 8.00

-3 10 90 0.2 0.3 0.68 1.15 0.25 4.00

7 10%

Processing Platforms Type p10 Base-Case Fixed Cost, $M u 300 Date of Start (1st Cost) f Months to Start-up from 1st Cost u 20 Base Capacity Max Oil, kbd d Min Oil, kbd d Water, kbd d Gas, Mscfd d Expansion Factor d 1.5 Oil, $M f 2 CapEx Option Water, $M f 2 Gas, $M f 5 Oil, $M f 50 CapEx Actual Cost Water, $M f 50 Gas, $M f 100 Distance to Market, miles d 25 Fixed Cost/Mile, $M (pipe, barge, etc) d 0.5 Overall Field Abandonment Cost, $M u 20.0

1991

1992

1993

1994

1995

1996

1997

1998

1

Company C 3

Schroder Ranking

5 7 9 11

24

36

2.0 3 3 10 100 100 150

2.5 5 5 15 150 150 200

0.8 1 30.0 50.0

Well Platforms/Pads Type p10 ML p90 Cost Uncertainty Factor u 0.90 1.00 1.20 Cost per slot, $M f 0.5 Cost per tie-in, $M f 0.2 Pipe Cost/Mile LP, $M/mi u 0.5 0.8 1 HP, $M/mi u 1 1.5 2 Number (6 max) d 4 Per Plat/Pad data WP1 WP2 WP3 WP4 Number of Slots/platform d 30 30 20 20 Base-Case Fixed Cost/Platform,$M f 100 100 80 80 Distance to Processing platform, miles f 2 4 3 4 Months to Start f 10 14 18 22 p10 10 10 10 10 Months to Build & Ready ML 12 12 12 12 p90 18 18 18 18

Year (5 year period ending) 1990

ML p90 325 375

Introduction of D&RA Company A

13 15

Figure 1. Corporate Performance Improvements using Decision and Risk Analysis after SPE 65144, Jonkman et al and Schroder & Co. Inc.3

14


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Start Middle

Model Detail

Simplified

Detailed

sC e iv at n r te w l A Fe

ed r ide y s n on a

End

M

SIAM-based Approach

Narrow

Broad

Classical Approach

Model Scope Figure 2. Comparison of Model Evolution over Duration of Investment

Parameter Gross Rock Volume Oil Price Average Porosity Saturation Facilities Cost Recovery Factor Rig Cost

Uncertainty

Impact on NPV

Example Action Buy more seismic Hedge with futures Take more core Different rock model Cheaper Steel Supply Competitive Bid Build simulator model

Net:Gross

More gamma logs

Reservoir Continuity

Survey Analogues

Fiscal Terms

Renegotiate PSC

Figure 3. Identification of which uncertainties have biggest impact on NPV

SPE 71414


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Decision and/or export to Portfolio Scenario and Decision Analysis Monte Carlo Simulation

Drilling Model G&G Model

Export Model

Processing Facility Model

Production Model

Costs

Economics

Revenues

Taxes & Royalties

Sensitivity Analysis

Scheduling

SIAM - Stochastic Integrated Asset Model

Uncertainty Estimates, Calibration, Simple Surrogates

Classical Models Figure 4. Schematic of SIAM-based Evaluation System

Well Target Optimization

Model Resolution

Fine

M am re t S

Coarse Well

lin

r go i l ROil e od lack

e

m Co

Well Work-over Planning

p.

Field Gathering System Evaluation

B

Reservoir Model Scope

Field

Figure 5. Technical Scalability for Different Decisions

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Step 1. Run SIAM over range of viable inputs to create complete PDF of output

SPE 71414

Step 2. Identify input parameters of the runs corresponding to key percentiles

Cumulative Probability

95%

50%

5% Economic Metric

Step 3. Run parameters through complete

Step 4. Use more accurate results to calibrate

models to calculate more accurate results

initial PDF

95%

95%

50%

50% Calibrated PDF

5%

5%

Figure 6. Calibration of SIAM

x Low outcomes

High outcomes

Expected OOIP

NPV PDF without Flexibilty

NPV PDF with Flexibilty

x 0 Expected NPV without Flexibility

Expected NPV with Flexibility Value of Flexibility

Figure 7. Real Options Thinking Can Create Value Through Flexibility