10.6084/m9.figshare.3453296.v1
Gabriele Martinelli
Gabriele
Martinelli
Jo Eidsvik
Jo
Eidsvik
Richard Sinding-Larsen
Richard
Sinding-Larsen
Sara Rekstad
Sara
Rekstad
Tapan Mukerji
Tapan
Mukerji
Building Bayesian networks from basin-modelling scenarios for improved geological decision making
Geological Society of London
2016
BN
Petroleum Systems Modelling
Bayesian networks
BPSM scenarios
prospect analysis
petroleum system
Supplementary material
building Bayesian networks
gas accumulations
heat flow
Basin models
evidence propagation
gain insights
form oil
source attributes
input parameters
uncertainty analysis
Geology
2016-06-21 11:17:37
Dataset
https://geolsoc.figshare.com/articles/dataset/Building_Bayesian_networks_from_basin-modelling_scenarios_for_improved_geological_decision_making/3453296
<p>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.
</p> <p><strong>Supplementary material:</strong> Tables and figures of analyses and data are available at: <a href="http://www.geolsoc.org.uk/SUP18607">www.geolsoc.org.uk/SUP18607</a>.
</p>