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

Bratvold, Reidar Brumer; Bickel, J. Eric; Lohne, Hans Petter

doi:10.2523/110378-ms

Cited by 9 publications

(13 citation statements)

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“…Value of information (VoI) is a typical area of risk management where mitigation actions can be implemented by using options to obtain additional information and added values can be created through new decisions based on the new information (Dias 2004;Bratvold et al 2009). VoI with uncertainties is usually characterized by expected VoI and a simple rule applys, i.e., the expected VoI is the difference between the expected value with information and that without information (Demirmen 2001;Cunningham et al 2008).…”

Section: Decision Tree Vs Monte Carlo Simulation For Quantifying Valmentioning

confidence: 99%

Beyond Expected Value: Integrated Project Valuation for Decision Making Under Uncertainty

2013

EAGE Annual Conference &Amp; Exhibition Incorporating SPE Europec

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Many decisions in petroleum exploration and development have to be made with significant uncertainties. Stochastic simulations are frequently used to capture the consequences of a decision in relation to the uncertain variables within reservoirs, infrastructures and economics. The results of stochastic simulations are often used in the following risk management process to optimize decisions. A big challenge in this process is to frame the outputs of stochastic simulations in a manner which makes sense to the decision-maker and provides him with clear judgemental insights into the problem and its related uncertainties. Traditional decision-tree analysis provides a good way of formulating and structuring decision problems, but one of its disadvantages is that only the expected value is used at a decision node. Most of the decision issues involve both continuous and discrete uncertainties, which can only be fully evaluated using distribution functions. By combining the decision-tree approach with stochastic simulation the term of expected value can be extended to a distribution function. Distribution functions can provide more details than expected values for understanding the influence of relevant uncertainties in the context of a full risk spectrum. Depending on the decision-maker's risk preference, decisions can be made based on not only the expected value but also upside or downside. A simple example on Value of Information is used to demonstrate the combination of decision-tree approach and stochastic simulation, where the value of information realized by a decision is quantified by a distribution function through Monte Carlo simulation rather than an expected value. A real decision situation concerning infill drilling in a North Sea field is presented to show the procedure of stochastic modeling of a decision tree in the framework of integrated project valuation. Important decisions along the time horizon include scheduling of drilling campaign, pilot or side-track drilling of adjacent prospects, and installing of a new template.

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Section: Decision Tree Vs Monte Carlo Simulation For Quantifying Valmentioning

confidence: 99%

Beyond Expected Value: Integrated Project Valuation for Decision Making Under Uncertainty

2013

EAGE Annual Conference &Amp; Exhibition Incorporating SPE Europec

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“…The approximate EVOI under approximation A is the expected value with information less the expected value without information (Bratvold et al 2009…”

Section: Discrete Approximationmentioning

confidence: 99%

Discretization, Simulation, and the Value of Information

Bickel

2011

SPE Annual Technical Conference and Exhibition

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Most decision analyses include continuous uncertainties (e.g., oil in place, oil price, or porosity). Analysts are frequently concerned with how to best structure, compute, and communicate decision models under these circumstances. While decision trees are well suited for discrete random variables with a few possibilities, they become unmanageable for a large number of outcomes. To address this concern, analysts frequently use discrete approximations such as Swanson's Mean. In this case, one approximates a continuous probability distribution by weighting the P10-P50-P90 fractiles by 0.30-0.40-0.30. Unfortunately, this method, and others like it, significantly underestimate the mean, variance, and skewness of most distributions-especially the lognormal, where its use is common. In this paper, we compare different discretizations within the context of a value of information problem and document the degree of error induced. We find that the best discretization is dependent on the decision context, which is difficult to specify in advance. In addition, we contrast the use of discrete approximations to Monte Carlo simulation, which many view as being more accurate. One must keep in mind, however, that simulation induces sampling error, while discretizations induce approximation error. The question is how many Monte Carlo (MC) trials it takes such that these two errors are equivalent. We find that it takes thousands, perhaps tens of thousands, of MC trials to provide better results than simple discretization methods. This is quite important if one is using MC in connection with a model that takes a long time to compute a single realization.

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“…Where the optimal policy is sensitive to changes in the probability of any chance node, further insight can be obtained by calculating the value of information (Bratvold et al, 2007). Moreover, different alternatives could be optimal, depending on how much the decision-maker values one unit of a given attribute relative to the others' units.…”

Section: Probabilistic Analysismentioning

confidence: 99%

Bayesian Decision Networks for Optimal Placement of Horizontal Wells

Rajaieyamchee

Bratvold

Badreddine

2010

SPE EUROPEC/EAGE Annual Conference and Exhibition

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A systematic decision analytic methodology for providing insight into horizontal well placement decisions is presented. These decisions require access to real-time data and to input from subject-experts on the team and from the decision-maker. The available information and outcomes of alternatives are associated with uncertainty. Furthermore, the decision-maker may have multiple objectives, such as future productions, wellbore configuration, and drilling costs. The methodology uses Bayesian decision networks, also known as influence diagrams, to frame, analyze, and support operational decisions.Influence diagrams are compact graphical representations of decisions and are based on decision and probability theories. They rigorously illustrate the interrelationships among decisions, key uncertainties, and value functions. Furthermore, they allow incorporating sensor readings, sub-surface model outputs, and experts' knowledge, which enables probabilistic inference and decision support in real-time contexts. Finally, the influence diagrams models may be used to calculate value of information and of control.In this paper, we focus on supporting well placement decisions involving multiple objectives. We explore the feasibility and suitability of using two potential extensions of the influence diagrams: (1) multiple-attribute utility influence diagram and (2) multiple-objective influence diagram. Using the methodology, the example shows that both extensions have the potential to improve the decision-making process within the multi-disciplinary team. They can provide further insight into decisions by ranking the value of information for key uncertainties from the perspective of different sub-groups of the drilling team.Following a systematic decision analytic methodology, in particular, decision analysis cycle, and applying influence diagrams in drilling operations would create value by reducing losses (materials and drilling/completion processes' downtime) and increasing future production through optimized well placement while drilling.

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

Cited by 9 publications

References 0 publications

Beyond Expected Value: Integrated Project Valuation for Decision Making Under Uncertainty

Beyond Expected Value: Integrated Project Valuation for Decision Making Under Uncertainty

Discretization, Simulation, and the Value of Information

Bayesian Decision Networks for Optimal Placement of Horizontal Wells

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