We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.

We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.

We use the following reference publication: "Reservoir characterization and lithostratigraphic division of the Mount Simon Sandstone (Cambrian): Implications for estimations of geologic sequestration storage capacity" by Cristian R. Medina and John A. Rupp. The paper describes the calculation of such capacity using geological, petrophysical and fluid properties. We model the storage capacity (defined as an explicit formula in the paper) using TKRISK's deterministic node.

This example shows how one can build and calibrate a model based on both data and expert knowledge while propagating uncertainty through the nodes of the PGM. This demonstrates the versatility of Bayesian network to model risk for a variety of engineering applications.