Abstract
Geological properties are likely to be shared for different prospects in a common basin. If, for example, source rock is found in one location, this increases the chance of finding it in other locations. Other geological characteristics will have similar correlation structures. When developing a drilling plan, these correlations should be considered because they can affect the optimal drilling sequence.
Modeling and accounting for these dependencies pose a significant challenge. The strength and structure of the dependencies must be assessed and specified through the use of conditional probabilities or correlations. Even for a relatively simple exploration plan, the resulting decision tree may have hundreds or even thousands of endpoints.
The sequential exploration problem with geological dependence has been addressed by several authors in the past few years. In this paper we extend this work by (1) simplifying the structure and model of the problem through the use of Influence Diagrams (Bayesian Decision Networks) and (2) using stochastic reservoir models to assess the dependence structure and strength. We also illustrate how to use the Maximum Entropy principle to construct joint probabilities when we have incomplete information about the correlation structure. Using Bayesian Decision Networks informed by stochastic reservoir models simplifies the modeling of exploration plans with a realistic number of wells.
Applying well known geostatistical techniques to aid in the assessment of the geological dependence structure and strength makes the approach more accessible to geoscientists wanting to account for these dependencies in their plans. Similarly, using Bayesian Decision Networks with their relatively simple and transparent structure will better facilitate the understanding and communication of the impacts of dependencies on the exploration plan. Given the typical exploration well cost, an optimal exploration program will in most cases generate significant savings.
