Strategies for petroleum exploration based on Bayesian Networks: a case study

The paper presents a new approach for modeling important geological elements, such as reservoir, trap and source, in a unified statistical model. This joint modeling of these geological variables is useful for reliable prospect evaluation, and provides a framework for consistent decision making under uncertainty. A Bayesian Network, involving different kinds of dependency structures, is used to model the correlation within the various geological elements, and to couple the elements. Based on the constructed network, an optimal sequential exploration strategy is established via dynamic programming. This strategy is useful for selecting the first prospect to explore, and which decisions to make next, depending on the outcome of the first well. A risk neutral decision maker will continue exploring new wells as long as the expected profit is positive. The model and choice of exploration strategy is tailored to a case study represented by five prospects in a salt basin, but it will also be useful for other contexts. For the particular case study we show how the strategy clearly depends on the exploration and development cost, and the expected volumes and recovery factors. The most lucrative prospect tends to be selected first, but the sequential decisions depend on the outcome of the exploration well in this first prospect.