Artificial immune systems for classification of petroleum well drilling operations

Detalhes bibliográficos
Autor(a) principal: Serapiao, Adriane B. S.
Data de Publicação: 2007
Outros Autores: Mendes, Jose R. P., Miura, Kazuo
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-540-73922-7_5
http://hdl.handle.net/11449/24785
Resumo: This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.
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spelling Artificial immune systems for classification of petroleum well drilling operationspetroleum engineeringmud-loggingartificial immune systemclassification taskThis paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.UNESP, IGCE, DEMAC, BR-13506900 Rio Claro, SP, BrazilUNESP, IGCE, DEMAC, BR-13506900 Rio Claro, SP, BrazilSpringerUniversidade Estadual Paulista (Unesp)Serapiao, Adriane B. S.Mendes, Jose R. P.Miura, Kazuo2014-02-26T17:00:46Z2014-05-20T14:15:58Z2014-02-26T17:00:46Z2014-05-20T14:15:58Z2007-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject47-58http://dx.doi.org/10.1007/978-3-540-73922-7_5Artificial Immune Systems, Proceedings. Berlin: Springer-verlag Berlin, v. 4628, p. 47-58, 2007.0302-9743http://hdl.handle.net/11449/2478510.1007/978-3-540-73922-7_5WOS:000250107800005Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengArtificial Immune Systems, Proceedings0,295info:eu-repo/semantics/openAccess2021-10-23T21:44:17Zoai:repositorio.unesp.br:11449/24785Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:27:36.423566Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Artificial immune systems for classification of petroleum well drilling operations
title Artificial immune systems for classification of petroleum well drilling operations
spellingShingle Artificial immune systems for classification of petroleum well drilling operations
Serapiao, Adriane B. S.
petroleum engineering
mud-logging
artificial immune system
classification task
title_short Artificial immune systems for classification of petroleum well drilling operations
title_full Artificial immune systems for classification of petroleum well drilling operations
title_fullStr Artificial immune systems for classification of petroleum well drilling operations
title_full_unstemmed Artificial immune systems for classification of petroleum well drilling operations
title_sort Artificial immune systems for classification of petroleum well drilling operations
author Serapiao, Adriane B. S.
author_facet Serapiao, Adriane B. S.
Mendes, Jose R. P.
Miura, Kazuo
author_role author
author2 Mendes, Jose R. P.
Miura, Kazuo
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Serapiao, Adriane B. S.
Mendes, Jose R. P.
Miura, Kazuo
dc.subject.por.fl_str_mv petroleum engineering
mud-logging
artificial immune system
classification task
topic petroleum engineering
mud-logging
artificial immune system
classification task
description This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.
publishDate 2007
dc.date.none.fl_str_mv 2007-01-01
2014-02-26T17:00:46Z
2014-05-20T14:15:58Z
2014-02-26T17:00:46Z
2014-05-20T14:15:58Z
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/10.1007/978-3-540-73922-7_5
Artificial Immune Systems, Proceedings. Berlin: Springer-verlag Berlin, v. 4628, p. 47-58, 2007.
0302-9743
http://hdl.handle.net/11449/24785
10.1007/978-3-540-73922-7_5
WOS:000250107800005
url http://dx.doi.org/10.1007/978-3-540-73922-7_5
http://hdl.handle.net/11449/24785
identifier_str_mv Artificial Immune Systems, Proceedings. Berlin: Springer-verlag Berlin, v. 4628, p. 47-58, 2007.
0302-9743
10.1007/978-3-540-73922-7_5
WOS:000250107800005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Artificial Immune Systems, Proceedings
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 47-58
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
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_ 1808129204733083648

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