Serapião, Adriane B. S. [UNESP]Mendes, José Ricardo P.2014-05-272014-05-272009-11-09Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5579 LNAI, p. 301-310.0302-97431611-3349http://hdl.handle.net/11449/71234This 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.301-310engBio-inspiredColony algorithmsData setsDecision-tree algorithmHybrid particlesRule inductionData miningDecision treesIntelligent systemsMud loggingOil wellsPetroleum industryWell drillingTrabalho apresentado em evento10.1007/978-3-642-02568-6_31WOS:000269972300031Acesso aberto2-s2.0-7035063309969978143431898600000-0001-9728-7092