Reidar Brumer Bratvold

Value of Information in the Oil and Gas Industry: Past, Present, and Future

This paper (SPE 110378) was accepted for presentation at the SPE Annual Technical Conference and Exhibition, Anaheim, California, USA, 11–14 November 2007, and revised for publication. Original manuscript received for review 10 August 2007. Revised manuscript received for review 10 March 2009. Paper peer approved 30 March 2009. Summary An important task that petroleum engineers and geoscientists undertake is to produce decision-relevant information. Some of the most important decisions we make concern what type and what quality of information to produce. When decisions are fraught with geologic and market uncertainties, this information gathering may such forms as seismic surveys, core and well test analyses, reservoir simulations, market analyses, and price forecasts—which the industry spends billions of US dollars each year. Yet, considerably less time and resources are expended on assessing the profitability or value of this information. Why is that? This paper addresses how to make value-of-information (VOI) analysis more accessible and useful by discussing its past, present, and future. On the basis of a survey of SPE publications, we provide an overview of the use of VOI in the oil and gas industry, focusing on how the analysis was carried out and for which types of decisions VOI analysis has been performed. We highlight areas in which VOI methods have been used successfully and identify important challenges. We then identify and discuss the possible causes for the limited use of VOI methods and suggest ways to increase the use of this powerful analysis tool.

Improving Investment Decisions Using a Stochastic Integrated Asset Model

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s).

From Uncertainty Quantification to Decision Making in the Oil and Gas Industry

In this paper, we present the findings of a large (N = 494) survey of oil and gas professionals that addressed the following two questions: Has uncertainty quantification improved in the oil and gas industry over the last five years? Has this improvement translated into improved decision making? Our results suggest that the answer to the first question in an unequivocal “yes,” but that the answer to the second is qualified “no.” How could this be? Uncertainty quantification is not an end unto itself; removing or even reducing uncertainty is not the goal. Rather, the objective is to make a good decision, which in many cases requires the assessment of the relevant uncertainties. The oil and gas industry seems to have lost sight of this goal in its good-faith effort to provide decision makers with a richer understanding of the possible outcomes flowing from major decisions. The industry implicitly believes that making good decisions merely requires more information. To counter this, we present a decision-focused uncertainty quantification framework, which we hope, in combination with our survey results, will aid in the innovation of better decision-making tools and methodologies.

I would rather be vaguely right than precisely wrong: A new approach to decision making in the petroleum exploration and production industry

This article 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, comprehensive, and rigorous models of all the components that contribute to an investment decision evaluation. Furthermore, we suggest that such an approach, even if feasible, is not desirable. Instead, we propose the concept of a holistic and probabilistic approach embedded in a decision-support system. This holistic approach has two major components. One is a technology component that integrates a variety of evaluation and decision-making tools. The second component is a modeling philosophy that fully recognizes the breadth and magnitude of uncertainty. It involves trading off some technical precision and detail for a more complete, accurate, and rigorous assessment of the impacts of uncertainty on the investment decision-making process. The main elements of such a system are simplified component models for each domain; a Monte Carlo simulation engine; and a 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 forward plans to mitigate and/or exploit uncertainties. Further applications are for the optimization of development plans, real options valuation, and the generation of consistent, risked cash flows for input to portfolio analysis. The application of such a system results in a true value-driven focus to the work of multidisciplinary asset teams through its ability to integrate the technical and business aspects of decisions.

Two-Factor Oil-Price Model and Real Option Valuation: An Example of Oilfield Abandonment

We discuss the two-factor oil-price model in valuation and analysis of flexible investment decisions. In particular, we will discuss the real options formulation of a typical oilfield-abandonment problem and will apply the least-squares Monte Carlo (LSM) simulation approach for calculation of project value. In this framework, the two-factor oil-price model will go a long way in the analysis of decisions and value creation. We also propose an implied method for estimation of parameters and state variables of the two-factor price process. The method is based on implied volatility of option on futures, the shape of the forward curve, and the implicit relationship between model parameters.

Uncertainty vs. variability: what's the difference and why is it important?

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). Uncertainty 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.

Value Creation with Multi-Criteria Decision Making in Geosteering Operations

Drilling costs in the petroleum industry have continued to escalate over the past decade. This trend has increased operators’ attempts to access the largest possible hydrocarbon resources with the lowest achievable costs in order to meet expected investment returns. To accomplish this, multiple well objectives are set prior to the start of drilling operations. Then, in many cases, a geosteering approach is implemented to help operators achieve these objectives. While the geosteering approach has a number of clear strengths and benefits, the efficiency of the current approach of using geosteering to optimize multiple well objectives is still questionable. A comprehensive review of geosteering case histories discussed in SPE papers has been performed. Many of the field cases discussed in these papers include multiple well objectives. The listed objectives are often conflicting and expressed in different measures. However, none of the cases from the reviewed literature have presented or discussed a systematic approach for dealing with multiple objectives in geosteering contexts. Although many will argue that the petroleum industry has adopted best-in-class technologies to support faster and better decisions, making real-time well placement decisions to meet all objectives is no simple task. It is impossible to optimize conflicting objectives at the same time without trading off the achievement of one objective against another. Without a well-structured approach, decision makers are likely to make judgments about the relative importance of each objective based on previous experience or on approximate methods. Research shows that such decision-making approaches are unlikely to identify optimal courses of action. In this paper, we develop and illustrate a consistent multi-criteria decision-making process adapted to operational geosteering decisions. Using this process, we demonstrate the impact of different criteria, and combinations of criteria, on the resulting well trajectories and well final placements. We present a case study that applies multicriteria decision-making (MCDM) methodologies to geosteering operations. The proposed method can assist the geosteering teams (GST) in their analysis and evaluation of multiple criteria for making better and more informed decisions. We believe that the method developed and presented in this paper will provide a consistent and transparent guideline for making geosteering decisions using multiple objectives. Employing this approach can assist operators in optimizing well placement while balancing trade-offs among objectives according to the organizations’ strategic and operational preferences.

A Game Theoretic Approach to Conflicting and Evolving Stakeholder Preferences in the E&P Industry

Summary In the high-risk E&P industry, the profit of each stakeholder depends on the strategies of all. The optimal choice for one player may not be optimal for other players, who may opt to prevent it. These characteristics suggest using game theory to model decision situations in the E&P industry. In a business dominated by joint ventures and tight governmental regulation, an understanding of the interests and influence of all stakeholders is particularly important. Although the E&P industry is accustomed to developing complex economic models to obtain insight into the commercial attractiveness of joint ventures, the influence of other stakeholders is often ignored. Each stakeholder must decide how much to invest, in which sequence to make decisions, whether to make decisions before or after the other stakeholders, which decisions to make before vs. after technological and organizational uncertainty is resolved, whether to accept or block the designation of one stakeholder as dominant, and whether to accept or block hub placement within one of multiple oil fields. A joint development program of a portfolio of gas fields and its gas processing and export facility has been analyzed using tools from game theory. The focus is on which infrastructure to develop and when. Players’ preferences are functions of equity stakes and expected reserve sizes of the prospects. The analysis provides insight into the preferred development options of the individual players, how preferences change as uncertainty gets resolved, and how much individual players are to gain or lose if certain investment decisions are made. The analysis allows a player (1) to identify under what conditions its objectives are aligned with fellow players, and (2) to quantify the maximum amount it can pay to gain support from its fellow players.

Sell Spot or Sell Forward? Analysis of Oil-Trading Decisions With the Two-Factor Price Model and Simulation

Summary In oil and gas markets, the relationships between the spot and futures prices reveal important opportunities for value creation. When oil prices are in contango (i.e., when futures prices are higher than the expected future spot prices), it may be profitable for a trader to hold oil in storage and enter into a futures contract instead of selling oil in the spot market. The decision to either sell oil in the spot market or use the storage to sell oil in the future is usually challenging because the future spot prices and futures prices are uncertain. In this paper, we discuss the storage trading decisions by use of a realistic example, and we propose an analysis methodology on the basis of a two-factor price process for modeling spot and futures oil prices. The dynamic decision problem, sell spot or sell forward, is analyzed with a forward dynamic optimization algorithm and the least-squares Monte Carlo simulation.

Valuation of swing contracts by Least Square Monte Carlo simulation

This paper was accepted for presentation at the 2010 Asia Pacific Oil & Gas Conference and Exhibition held in Brisbane, Queensland, Australia, 18–20 October 2010, and revised for publication. Original manuscript received for review 2 December 2010. Revised paper received for review 2 August 2011. Paper peer approved 15 September 2011 as SPE paper 133044. Summary Natural gas and electricity are commonly traded through swing contracts that enable the buyer to exploit changes in market price or market demand by varying the quantity they receive from the producer (seller). The producer is assured of selling a minimum quantity at a fixed price, but must be able to meet the variable demand from the buyer. The flexibility of such contracts enables both parties to mitigate the risks and exploit the opportunities that arise from uncertainty in production, demand, price, and so on. But how valuable are they? Traditional net present value (NPV), based on expected values, cannot value this flexibility, and the traditional options/valuation techniques could not model the complexity of the terms of such contracts. Taking gas contracts as an example, this paper seeks to (a) raise awareness of how flexibility creates value for both parties and (b) show how least-squares Monte Carlo (LSM) simulation can be used to quantify its value in dollar terms, from the perspective of both producer and buyer. Because the value of flexibility arises from the ability it gives to respond to fluctuations (e.g., in commodity prices), a useful model of swing contracts needs to reflect the nature of these fluctuations.