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dc.contributor.authorSerapiao, Adriane B. S.-
dc.contributor.authorMendes, Jose R. P.-
dc.contributor.authorMiura, Kazuo-
dc.contributor.authorNunesDeCastro, L.-
dc.contributor.authorVonZuben, F. J.-
dc.contributor.authorKnidel, H.-
dc.date.accessioned2014-02-26T17:00:46Z-
dc.date.accessioned2014-05-20T14:15:58Z-
dc.date.accessioned2016-10-25T17:39:10Z-
dc.date.available2014-02-26T17:00:46Z-
dc.date.available2014-05-20T14:15:58Z-
dc.date.available2016-10-25T17:39:10Z-
dc.date.issued2007-01-01-
dc.identifierhttp://dx.doi.org/10.1007/978-3-540-73922-7_5-
dc.identifier.citationArtificial Immune Systems, Proceedings. Berlin: Springer-verlag Berlin, v. 4628, p. 47-58, 2007.-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/11449/24785-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/24785-
dc.description.abstractThis 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.en
dc.format.extent47-58-
dc.language.isoeng-
dc.publisherSpringer-
dc.sourceWeb of Science-
dc.subjectpetroleum engineeringpt
dc.subjectmud-loggingpt
dc.subjectartificial immune systempt
dc.subjectclassification taskpt
dc.titleArtificial immune systems for classification of petroleum well drilling operationsen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP, IGCE, DEMAC, BR-13506900 Rio Claro, SP, Brazil-
dc.description.affiliationUnespUNESP, IGCE, DEMAC, BR-13506900 Rio Claro, SP, Brazil-
dc.identifier.doi10.1007/978-3-540-73922-7_5-
dc.identifier.wosWOS:000250107800005-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofArtificial Immune Systems, Proceedings-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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