%0 Generic %A Martinelli, Gabriele %A Eidsvik, Jo %A Sinding-Larsen, Richard %A Rekstad, Sara %A Mukerji, Tapan %D 2016 %T Building Bayesian networks from basin-modelling scenarios for improved geological decision making %U https://geolsoc.figshare.com/articles/dataset/Building_Bayesian_networks_from_basin-modelling_scenarios_for_improved_geological_decision_making/3453296 %R 10.6084/m9.figshare.3453296.v1 %2 https://ndownloader.figshare.com/files/5422238 %2 https://ndownloader.figshare.com/files/5422241 %2 https://ndownloader.figshare.com/files/5422244 %2 https://ndownloader.figshare.com/files/5422247 %2 https://ndownloader.figshare.com/files/5422250 %2 https://ndownloader.figshare.com/files/5422253 %2 https://ndownloader.figshare.com/files/5422256 %2 https://ndownloader.figshare.com/files/5422259 %2 https://ndownloader.figshare.com/files/5422262 %2 https://ndownloader.figshare.com/files/5422265 %2 https://ndownloader.figshare.com/files/5422268 %2 https://ndownloader.figshare.com/files/5422271 %2 https://ndownloader.figshare.com/files/5422274 %2 https://ndownloader.figshare.com/files/5422277 %K BN %K Petroleum Systems Modelling %K Bayesian networks %K BPSM scenarios %K prospect analysis %K petroleum system %K Supplementary material %K building Bayesian networks %K gas accumulations %K heat flow %K Basin models %K evidence propagation %K gain insights %K form oil %K source attributes %K input parameters %K uncertainty analysis %K Geology %X

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.

%I Geological Society of London