Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

Detalhes bibliográficos
Autor(a) principal: Serapião, Adriane B. S. [UNESP]
Data de Publicação: 2009
Outros Autores: Mendes, José Ricardo P.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-642-02568-6_31
http://hdl.handle.net/11449/71234
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.
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spelling Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithmBio-inspiredColony algorithmsData setsDecision-tree algorithmHybrid particlesRule inductionData miningDecision treesIntelligent systemsMud loggingOil wellsPetroleum industryWell drillingThis 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.UNESP/IGCE/DEMAC, C.P. 178, Rio Claro (SP) CEP 13506-900UNICAMP/FEM/DEP, C.P. 6122, Campinas (SP) CEP 13081-970UNESP/IGCE/DEMAC, C.P. 178, Rio Claro (SP) CEP 13506-900Universidade Estadual Paulista (Unesp)Universidade Estadual de Campinas (UNICAMP)Serapião, Adriane B. S. [UNESP]Mendes, José Ricardo P.2014-05-27T11:24:02Z2014-05-27T11:24:02Z2009-11-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject301-310http://dx.doi.org/10.1007/978-3-642-02568-6_31Lecture 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/11449/7123410.1007/978-3-642-02568-6_31WOS:0002699723000312-s2.0-7035063309969978143431898600000-0001-9728-7092Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:32Zoai:repositorio.unesp.br:11449/71234Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:45:26.363950Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
title Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
spellingShingle Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
Serapião, Adriane B. S. [UNESP]
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
title_short Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
title_full Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
title_fullStr Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
title_full_unstemmed Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
title_sort Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
author Serapião, Adriane B. S. [UNESP]
author_facet Serapião, Adriane B. S. [UNESP]
Mendes, José Ricardo P.
author_role author
author2 Mendes, José Ricardo P.
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Estadual de Campinas (UNICAMP)
dc.contributor.author.fl_str_mv Serapião, Adriane B. S. [UNESP]
Mendes, José Ricardo P.
dc.subject.por.fl_str_mv 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
topic 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
description 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.
publishDate 2009
dc.date.none.fl_str_mv 2009-11-09
2014-05-27T11:24:02Z
2014-05-27T11:24:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 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
6997814343189860
0000-0001-9728-7092
url http://dx.doi.org/10.1007/978-3-642-02568-6_31
http://hdl.handle.net/11449/71234
identifier_str_mv 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
10.1007/978-3-642-02568-6_31
WOS:000269972300031
2-s2.0-70350633099
6997814343189860
0000-0001-9728-7092
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 301-310
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128854063054848

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