Artificial immune systems for classification of petroleum well drilling operations
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
26/02/2014
20/05/2014
26/02/2014
20/05/2014
01/01/2007
|
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. |
Formato |
47-58 |
Identificador |
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 |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
Artificial Immune Systems, Proceedings |
Direitos |
closedAccess |
Palavras-Chave | #petroleum engineering #mud-logging #artificial immune system #classification task |
Tipo |
info:eu-repo/semantics/conferencePaper |