# The lowest possible outcome is 112m and 5 of the

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Unformatted text preview: Results from the Monte Carlo simulation But in fact there is a range of possible outcomes and a chance of very different results. For example, the so-called p10 value, or forecast with 10% possibility of occurrence, (see table 5.4) is \$27 million, so 10% of the cases run in the simulation gave values less than \$27 million. The lowest possible outcome is \$-112M and 5% of the cases, or trials, gave negative NPVs. On the other hand, the p90 was \$223 million, so 10% of the trials gave values greater than \$223 million (Bailey et al., in press). For this particular field, there is a small, but not zero (that is, approximately 4%) chance of losing money. The decision would probably still be to go ahead, but the Monte Carlo analysis, by revealing the wider picture, gives the decision-makers greater comfort that their decision has taken everything into account. Using risk analysis in investment appraisal has a number of advantages. Firstly and most importantly, it allows the analyst to describe risk and uncertainty as a range and distribution of possible values for each unknown factor, rather than a single, discrete average or most likely value. Consequently when Monte Carlo simulation is used to generate a probability distribution of NPV, Newendorp (1996 p375) believes that: “The resulting profit distribution will reflect all the possible values of the variable.” This is a slightly dubious claim since the resulting profit distribution will not contain every possible value of NPV. It will only include those that the decision-maker or analyst feels are likely to occur. There is always the possibility of “acts of God” or “train wrecks” (see section 5.7 and for a full discussion refer to Spencer and Morgan, 1998). However, it is certainly true, that in generating probabilistic output, the decision-maker is more likely to capture the actual value in the predicted range. 100 Secondly, risk analysis allows the analyst to identify those factors that have the most significant effect on the resulting values of profit. The analyst can then use sensitivity analysis to understand the impact of these factors further. There are several ways this sensitivity analysis can be carried out and the reader is referred to Singh and Kinagi (1987) for a full discussion. Implementing risk analysis using Monte Carlo simulations has limitations and presents a number of challenges. Firstly, Monte Carlo simulations do not allow for any managerial flexibility. This can be overcome by running simulations for several scenarios (Galli et al., 1999). Gutleber et al. (1995) present a case study where simulations were carried out to compare three deals involving an oil company and local government. Murtha (1997) provides many references to practical applications of this procedure. Secondly, whilst geologists intuitively expect to find a correlation between, for example, hydrocarbon saturation and porosity, this is not acknowledged explicitly in the literature nor are analysts given any guidance concerning how to model such relationships. Goodwin and Wright (1991 pp153-157) describe types of depe...
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