Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
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Abstract
This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.
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Bio-inspired, Colony algorithms, Data sets, Decision-tree algorithm, Hybrid particles, Rule induction, Data mining, Decision trees, Intelligent systems, Mud logging, Oil wells, Petroleum industry, Well drilling
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English
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5579 LNAI, p. 301-310.
