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Evolved Bayesian Network models of rig operations in the Gulf of Mexico

Fournier, Francois A.; McCall, John; Petrovski, Andrei; Barclay, Peter J.

Authors

Francois A. Fournier

John McCall

Andrei Petrovski



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.

Presentation Conference Type Conference Paper (Published)
Conference Name 2010 IEEE Congress on Evolutionary Computation (CEC)
Start Date Jul 18, 2010
End Date Jul 23, 2010
Online Publication Date Sep 27, 2010
Publication Date 2010-07
Deposit Date Apr 11, 2022
Publisher Institute of Electrical and Electronics Engineers
Book Title IEEE Congress on Evolutionary Computation
ISBN 978-1-4244-6909-3
DOI https://doi.org/10.1109/cec.2010.5586021
Keywords Drilling, Bayesian methods, Petroleum, Data models, Databases, Geology, Availability
Public URL http://researchrepository.napier.ac.uk/Output/2863245