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

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

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.

Citation

Fournier, F. A., McCall, J., Petrovski, A., & Barclay, P. J. (2010, July). Evolved Bayesian Network models of rig operations in the Gulf of Mexico. Presented at 2010 IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain

• 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