F.A. Fournier

Publications (2)0 Total impact

• Conference Proceeding: Application of evolutionary algorithms to learning evolved Bayesian Network models of rig operations in the Gulf of Mexico

  F.A. Fournier, Yanghui Wu, J. McCall, A. Petrovski, P.J. Barclay
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  ABSTRACT: The operation of drilling rigs is highly expensive. It is therefore important to be able to identify and analyse variables affecting rig operations. We investigate the use of Genetic Algorithms and Ant Colony Optimisation to induce a Bayesian Network model for the real world problem of Rig Operations Management and confirm the validity of our previous model. We explore the relative performances of different search and scoring heuristics and consider trade-offs between best network score and computation time from an industry standpoint. Finally, we analyse edge-discovery statistics over repeated runs to explain observed differences between the algorithms.
  Computational Intelligence (UKCI), 2010 UK Workshop on; 10/2010
• Conference Proceeding: Evolved Bayesian Network models of rig operations in the gulf of Mexico

  F.A. Fournier, J. McCall, A. Petrovski, P.J. Barclay
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  ABSTRACT: 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.
  Evolutionary Computation (CEC), 2010 IEEE Congress on; 08/2010