Quantifying and reducing uncertainty in play mapping by using information entropy and Bayesian inference

Abstract Conventional exploration for oil and gas is based on the play concept, which puts a prospect into the regional geological context and allows selection of which areas or licences to choose and how to rank prospects. This is traditionally done by mapping the critical elements that constitute a play such as source, reservoir and seal, and then stacking them, which results in a mosaic of segments with similar common risk. However, this method does not distinguish between areas that have little data and therefore high uncertainties from areas with identified risks and therefore less uncertainty. This is the case because uncertainties are not appropriately taken into account in play element mapping. We propose an innovative method that quantifies and visualizes uncertainties of individual play elements using the principle of information entropy. The play fairway maps generated by this method can easily be updated by using principles of Bayesian inference, which in turn allow value of information calculations. With this approach, it can be estimated how much additional well or seismic information will impact play size, yet-to-find volumes, and prospect ranking.