Building Bayesian networks from basin-modelling scenarios for improved geological decision making
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.