Research Output
Evolved Bayesian Network models of rig operations in the Gulf of Mexico
  The operation of drilling rigs is highly expensive. It is therefore important to be able to identify and analyse factors affecting rig operations. We investigate the use of two Genetic Algorithms, K2GA and ChainGA, to induce a Bayesian Network model for the real world problem of Rig Operations Management. We sample from a unique dataset derived from the commercial market intelligence databases assembled by ODS-Petrodata Ltd. We observe a trade-off between K2GA, which finds significantly better scoring networks on our dataset, and ChainGA, which uses only one quarter of the computation time. We analyse the best structures produced from an industry standpoint and conclude by outlining a few potential applications of the models to support rig operations.

  • Date:

    31 July 2010

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/cec.2010.5586021

  • Cross Ref:

    10.1109/cec.2010.5586021

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Fournier, F. A., McCall, J., Petrovski, A., & Barclay, P. J. (2010). Evolved Bayesian Network models of rig operations in the Gulf of Mexico. In IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/cec.2010.5586021

Authors

Keywords

Drilling, Bayesian methods, Petroleum, Data models, Databases, Geology, Availability

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