Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/195863 |
Resumo: | Bit 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. |
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Repositório Institucional da UNESP |
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Application of ARX neural networks to model the Rate of Penetration of petroleum wells drillingNeural NetworksARX modelpetroleum wells drillingRate of Penetrationand drilling performanceBit 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.Univ Estadual Campinas, FEM, DEP, CP 6052, Campinas, SP, BrazilUNESP, IGCE, DEMAC, Rio Claro, SP, BrazilUNESP, IGCE, DEMAC, Rio Claro, SP, BrazilActa Press AnaheimUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Fonseca, Tiago C.Mendes, Jose Ricardo P.Serapiao, Adriane B. S. [UNESP]Guilherme, Ivan R. [UNESP]Kovalerchuk, B.2020-12-10T18:05:49Z2020-12-10T18:05:49Z2006-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject152-+Proceedings Of The Second Iasted International Conference On Computational Intelligence. Anaheim: Acta Press Anaheim, p. 152-+, 2006.http://hdl.handle.net/11449/195863WOS:000243777100027Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings Of The Second Iasted International Conference On Computational Intelligenceinfo:eu-repo/semantics/openAccess2021-10-23T12:19:08Zoai:repositorio.unesp.br:11449/195863Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:18:22.120082Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
title |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
spellingShingle |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling Fonseca, Tiago C. Neural Networks ARX model petroleum wells drilling Rate of Penetration and drilling performance |
title_short |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
title_full |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
title_fullStr |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
title_full_unstemmed |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
title_sort |
Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling |
author |
Fonseca, Tiago C. |
author_facet |
Fonseca, Tiago C. Mendes, Jose Ricardo P. Serapiao, Adriane B. S. [UNESP] Guilherme, Ivan R. [UNESP] Kovalerchuk, B. |
author_role |
author |
author2 |
Mendes, Jose Ricardo P. Serapiao, Adriane B. S. [UNESP] Guilherme, Ivan R. [UNESP] Kovalerchuk, B. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Fonseca, Tiago C. Mendes, Jose Ricardo P. Serapiao, Adriane B. S. [UNESP] Guilherme, Ivan R. [UNESP] Kovalerchuk, B. |
dc.subject.por.fl_str_mv |
Neural Networks ARX model petroleum wells drilling Rate of Penetration and drilling performance |
topic |
Neural Networks ARX model petroleum wells drilling Rate of Penetration and drilling performance |
description |
Bit 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. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-01-01 2020-12-10T18:05:49Z 2020-12-10T18:05:49Z |
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 |
Proceedings Of The Second Iasted International Conference On Computational Intelligence. Anaheim: Acta Press Anaheim, p. 152-+, 2006. http://hdl.handle.net/11449/195863 WOS:000243777100027 |
identifier_str_mv |
Proceedings Of The Second Iasted International Conference On Computational Intelligence. Anaheim: Acta Press Anaheim, p. 152-+, 2006. WOS:000243777100027 |
url |
http://hdl.handle.net/11449/195863 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings Of The Second Iasted International Conference On Computational Intelligence |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
152-+ |
dc.publisher.none.fl_str_mv |
Acta Press Anaheim |
publisher.none.fl_str_mv |
Acta Press Anaheim |
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_ |
1808129050040860672 |