Abstract
The oil industry regularly applies decision tree analysis for the evaluation of single decisions involving discrete uncertainty. Decision processes, such as the evaluation of appraisal drilling or a flexible oilfield development, that require the resolution of continuous uncertainty and the evaluation of multiple sequential decisions, are difficult to analyse using conventional decision tree methods and require the application of alternative techniques.
This paper presents a solution to the problem using a branching Monte Carlo method that is derived from option theory. This method can be applied to a large class of problems involving an indefinite number of uncertain variables and multiple sequential decisions.
An example of the method is presented for the evaluation of a multi-well appraisal program and it is shown that this method can be used for optimal appraisal planning. The paper proposes the application of a resource learning factor, which is a measure of the resource uncertainty reduction from gaining additional information. This learning factor can be derived from historical records of many oil companies.
Further, the paper compares the active option of appraisal with the passive option of waiting for prices to increase that is more commonly considered in the option literature. It is shown that where an active resource discovery process is followed the active option dominates the passive price option.
