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Unformatted text preview: cular state in a series of identical experiments. However, given its
emphasis on repeated experiments, the frequentist concept is unsuitable for modelling
under the conditions of risk and uncertainty present in the majority of investment
decision problems. As such, the subjective probabilities used in decision analysis are
founded upon a quite a different conceptual base. As indicated in Chapter 2, to a
subjectivist, probability represents an observer’s degree of belief that a system will
adopt a particular state. There is no presumption of an underlying series of experiments. The observer need only be going to observe the system on one occasion.
Moreover, subjective probabilities encode something about the observer of the
system, not the system itself. The justification for using subjective probabilities in
decision analysis does not just rest on the case that frequentist probabilities are
inappropriate but also in the principles of consistency that the Bayesians suggest
should be embodied in rational decisionmaking (for a full discussion see French,
1989 p31). Accepting the rationale that subjective probability estimates should be
used for investment decisionmaking under conditions of uncertainty however does
cause problems since, partly because of their subjective nature, there is no formula in
the decision analysis literature for generating these probabilities. Therefore, analysts
typically use their judgement, extrapolate from historical data or, for example when
estimating recoverable reserves for a particular field, use the results achieved in other
97 similar plays to guide their predictions. Traditionally analysts used singlevalue
probability estimates to express the degree of risk and uncertainty relating to the
uncertain parameters. More popular now is to generate subjective probability estimates using risk analysis which adds the dimension of simulation to decision
analysis.
The following section draws on the prescriptive decision analysis literature first to
provide a brief overview of the main concepts of risk analysis and then to indicate the
impact of risk analysis on investment decisionmaking in the upstream. It is important to recognise that risk analysis is a special case of decision analysis that uses
techniques of simulation. Often in the literature, the terms are used interchangeably
leading to confusion when comparing accounts.
5.4 RISK ANALYSIS
Simulation as a means of risk analysis in decisionmaking was first applied to
petroleum exploration investments in 1960 (Grayson, 1960). The technique can be
applied to any type of calculation involving random variables. It can be used to
answer technical questions such as (“What is the volume of recoverable reserves of
hydrocarbons in this acreage?”) and economic ones such as (“What is the probability
that the NPV of this prospect will exceed the target of $x million?”) (Bailey et al., in
press). The main concepts of risk analysis using simulation will now be presented
before its applicability to the upstream is examined.
Risk analysis based on Monte Carlo simulation is a technique whereby the risk and
uncertainty encompassing the main projected variables in a decision problem are
described...
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 Summer '14
 The Land

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