Strategies for Petroleum Exploration on the Basis of Bayesian Networks: A Case Study


The paper presents a new approach for modeling important geologicalelements, such as reservoir, trap, and source, in a unified statistical model.This joint modeling of these geological variables is useful for reliableprospect evaluation, and provides a framework for consistent decision makingunder uncertainty. A Bayesian network (BN), involving different kinds ofdependency structures, is used to model the correlation within the variousgeological elements and to couple the elements. On the basis of the constructednetwork, an optimal sequential exploration strategy is established with dynamicprogramming (DP). This strategy is useful for selecting the first prospect toexplore and for making the decisions that should follow, depending on theoutcome of the first well. A risk-neutral decision maker will continueexploring new wells as long as the expected profit is positive. The model andchoice of exploration strategy are tailored to a case study represented by fiveprospects in a salt basin, but they will also be useful for other contexts. Forthe particular case study, we show how the strategy clearly depends on theexploration and development cost and the expected volumes and recovery factors.The most lucrative prospect tends to be selected first, but the sequentialdecisions depend on the outcome of the exploration well in this firstprospect.