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

Fournier, Fran�ois A.; Wu, Yanghui; McCall, John; Petrovski, Andrei; Barclay, Peter J.


Fran�ois A. Fournier

Yanghui Wu

John McCall

Andrei Petrovski


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.

Presentation Conference Type Conference Paper (Published)
Conference Name 2010 UK Workshop on Computational Intelligence (UKCI)
Start Date Sep 8, 2010
End Date Sep 10, 2010
Online Publication Date Nov 9, 2010
Publication Date 2010
Deposit Date Apr 13, 2022
Publisher Institute of Electrical and Electronics Engineers
Series ISSN 2162-7657
Book Title 2010 UK Workshop on Computational Intelligence (UKCI)
ISBN 978-1-4244-8774-5
Keywords Bayesian methods, Drilling, Petroleum, Optimization, Data models, Industries, Runtime
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