# 12 a 9 step approach to investment appraisal in the

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Unformatted text preview: utions assigned to the economic factors and changing the nature of the dependencies between the variables. 7. Using influence diagrams draw the decision tree. 8. For each reserve case, recombine the chance of success estimated in step 1 and the economic values generated in step 6, through a decision tree analysis to generate EMVs. 9. Use option theory via decision tree analysis and assess the impact on the EMV. Figure 5.12: A 9 Step approach to investment appraisal in the upstream oil and gas industry Next, the geologist performs a probabilistic analysis of reserves using Monte Carlo techniques. The following formula is used to generate the estimate of the volume of hydrocarbons recoverable from an underground prospect: Recoverable reserves = gross rock volume * net pay/gross pay * porosity * hydrocarbon saturation * recovery efficiency * formation volume factor, 119 where, gross rock volume (GRV) is the total volume of the “container” mapped out by the geologists; net/gross is the proportion of the container that is reservoir rock (for example, sand) as opposed to non-reservoir rock (shale); porosity is a measure of the fluid storage space (or pores) in the reservoir rock, as opposed to sand grains; hydrocarbon saturation is the proportion of fluid in the pore spaces that is hydrocarbons as opposed to water; recovery efficiency is the proportion of hydrocarbons in the reservoir that engineers can actually get out; and, formation volume factor describes the change in volume of hydrocarbons as they flow from the pressure and temperature of the subsurface to the surface (Bailey et al., in press). The geologists, based on limited data, draw probability distributions for each of these variables. In an ideal world, the individual distributions would be entirely data driven – based on data derived from many porosity measurements, for example. But, in practice, the data available are often minimal. The geologists will suggest the shape of the curve that is consistent with the small amount of data available. Geologists often, for instance, draw analogies between the porosity of the rocks being examined and the porosity of rocks from a similar previously exploited area (Bailey et al., in press). As indicated above in section 5.4, the shape of the distributions to be used is a contentious issue. The distributions can vary enormously and they will be chosen to fit different circumstances. A triangular distribution, for instance, might be chosen for porosity if the experts were confident that they knew the minimum, most likely and maximum porosities. A lognormal distribution might seem most appropriate for GRV, indicating that experts think that there is a slightly higher chance of very high values than of very low. Once each variable has been assigned a distribution type, any dependencies between the parameters are modeled. Section 5.4 explained that due to the lack of prescription in the literature, this is another difficult task for the geologist. Geologists usually pre...
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