Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorFonseca, Tiago C.-
Autor(es): dc.creatorMendes, Jose Ricardo P.-
Autor(es): dc.creatorSerapiao, Adriane B. S. [UNESP]-
Autor(es): dc.creatorGuilherme, Ivan R. [UNESP]-
Autor(es): dc.creatorKovalerchuk, B.-
Data de aceite: dc.date.accessioned2022-02-22T00:07:07Z-
Data de disponibilização: dc.date.available2022-02-22T00:07:07Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2006-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/195863-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/195863-
Descrição: dc.descriptionBit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.-
Descrição: dc.descriptionUniv Estadual Campinas, FEM, DEP, CP 6052, Campinas, SP, Brazil-
Descrição: dc.descriptionUNESP, IGCE, DEMAC, Rio Claro, SP, Brazil-
Descrição: dc.descriptionUNESP, IGCE, DEMAC, Rio Claro, SP, Brazil-
Formato: dc.format152-+-
Idioma: dc.languageen-
Publicador: dc.publisherActa Press Anaheim-
Relação: dc.relationProceedings Of The Second Iasted International Conference On Computational Intelligence-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectNeural Networks-
Palavras-chave: dc.subjectARX model-
Palavras-chave: dc.subjectpetroleum wells drilling-
Palavras-chave: dc.subjectRate of Penetration-
Palavras-chave: dc.subjectand drilling performance-
Título: dc.titleApplication of ARX neural networks to model the Rate of Penetration of petroleum wells drilling-
Aparece nas coleções:Repositório Institucional - Unesp

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