Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
| Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
|---|---|
| Data(s) |
27/05/2014
27/05/2014
09/11/2009
|
| Resumo |
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. |
| Formato |
301-310 |
| Identificador |
http://dx.doi.org/10.1007/978-3-642-02568-6_31 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5579 LNAI, p. 301-310. 0302-9743 1611-3349 http://hdl.handle.net/11449/71234 10.1007/978-3-642-02568-6_31 WOS:000269972300031 2-s2.0-70350633099 |
| Idioma(s) |
eng |
| Relação |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Direitos |
closedAccess |
| Palavras-Chave | #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 |
| Tipo |
info:eu-repo/semantics/conferencePaper |
