Basin models are used to gain insights about a petroleum system, and to simulate geological processes required to form oil and gas accumulations. The focus of such simulations is usually on charge and timing-related issues, although uncertainty analysis about a wider range of parameters is becoming more common. Bayesian networks (BNs) are useful for decision making in geological prospect analysis and exploration. In this paper we propose a framework for merging these two methodologies: by doing so, we explicitly account for dependencies between the geological elements. The probabilistic description of the BN is trained by using multiple scenarios of Basin and Petroleum Systems Modelling (BPSM). A range of different input parameters are used for total organic content, heat flow, porosity and faulting to span a full categorical design for the BPSM scenarios. Given the consistent BN for trap, reservoir and source attributes, we demonstrate important decision-making applications, such as evidence propagation and the value of information.
Supplementary material: Tables and figures of analyses and data are available at: www.geolsoc.org.uk/SUP18607.