Another Look at Layered Prospects Available to Purchase

Often a drilling prospect has multiple horizons, each with its chance of success and its volumetric estimate of reserves. A proper evaluation of these prospects acknowledges the multiplicity of possible outcomes, ranging from total failure to total success. If the layers are independent, it is simple to assign probabilities to these outcomes. Whether one seeks a simple mean value or a more sophisticated Monte Carlo simulation, the independence assumption gives a straightforward procedure for estimating aggregate reserves.

When the layers are dependent, however, the aggregation problem becomes more subtle: the success or failure of one layer alters the chance of success of other layers, and the corresponding probabilities of the various combinations of successes are more difficult to calculate. Moreover, the rules of conditional probability, notably consequences of Bayes’ Theorem, provide challenges to those who assign estimates for the revised values. Even in the case of two layers, some estimators incorrectly assign these values by failing to quantify correctly their interdependence. These issues were addressed by Murtha¹ , in which a method was proposed to overcome the pitfalls of conditional probability. Then, at the SPE 2000 ATCE, Delfiner²  and Stabell³  offered alternative procedures for handling dependence.

We propose a new Monte Carlo simulation method, which is simple to describe for two layers, is based on tested concepts, can be shown to yield desired conditional probabilities, readily extends to any number of layers, and, most importantly, always honors Bayes’ Theorem.

As in the earlier paper¹ , the generic form of this model can be applied to prospects in adjoining fault blocks, step-outs, and other forms of multiple targets that should be regarded as a package when ranking prospects.