Machine learning methods applied to drilling rate of penetration prediction and optimization - A review
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.petrol.2019.106332 http://hdl.handle.net/11449/189525 |
Resumo: | Drilling wells in challenging oil/gas environments implies in large capital expenditure on wellbore's construction. In order to optimize the drilling related operation, real-time decisions making have been put in place, so that prediction of rate of penetration (ROP) with accuracy is essential. Despite many efforts (theoretical and experimental) throughout the years, modeling the ROP as a mathematical function of some key variables is not so trivial, due to the highly non-linearity behavior experienced. Therefore, several researches in the recent years have been proposing to use data-driven models from artificial intelligence field for ROP prediction and optimization. This paper presents an extensive review of the literature on ROP prediction, especially, with machine learning techniques, as well as how these models can be used to optimize the drilling activities. The ROP models are classified as traditional models (based on physics-models), statistical models (e.g. multiple regression), or machine learning methods. This review enables to see that machine learning techniques can potentially outperform in terms of ROP-prediction accuracy on top of traditional or statistical models. Throughout this work, an extensive analysis of different ways of obtaining ROP models is carried out, concluding with different strategies adopted in literature to perform data-driven model optimization. Despite the saving potential which can be achieved with real-time optimization based on data-driven ROP models, it is noticeable that there is a lack of implementation of those techniques in the industry, as per literature review. To take a step forward in real implementations, the petroleum industry must be aware that yet no rule of thumb already exists on this specific area, but still, good and very reasonable results can be achieved by following the best practices identified in this review. In addition, the modern practices of machine learning provide promising guidelines for implementing projects in oil and gas industry. |
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Machine learning methods applied to drilling rate of penetration prediction and optimization - A reviewArtificial intelligenceData-driven modelDrilling optimizationMulti-objective optimizationROPDrilling wells in challenging oil/gas environments implies in large capital expenditure on wellbore's construction. In order to optimize the drilling related operation, real-time decisions making have been put in place, so that prediction of rate of penetration (ROP) with accuracy is essential. Despite many efforts (theoretical and experimental) throughout the years, modeling the ROP as a mathematical function of some key variables is not so trivial, due to the highly non-linearity behavior experienced. Therefore, several researches in the recent years have been proposing to use data-driven models from artificial intelligence field for ROP prediction and optimization. This paper presents an extensive review of the literature on ROP prediction, especially, with machine learning techniques, as well as how these models can be used to optimize the drilling activities. The ROP models are classified as traditional models (based on physics-models), statistical models (e.g. multiple regression), or machine learning methods. This review enables to see that machine learning techniques can potentially outperform in terms of ROP-prediction accuracy on top of traditional or statistical models. Throughout this work, an extensive analysis of different ways of obtaining ROP models is carried out, concluding with different strategies adopted in literature to perform data-driven model optimization. Despite the saving potential which can be achieved with real-time optimization based on data-driven ROP models, it is noticeable that there is a lack of implementation of those techniques in the industry, as per literature review. To take a step forward in real implementations, the petroleum industry must be aware that yet no rule of thumb already exists on this specific area, but still, good and very reasonable results can be achieved by following the best practices identified in this review. In addition, the modern practices of machine learning provide promising guidelines for implementing projects in oil and gas industry.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)São Paulo State University (Unesp) School of Engineering Department of Energy, GuaratinguetáUniversidade Federal do Espírito Santo - Department of Engineering and Technology – Petroleum Engineering Programs (UFES/DETEC)International Institute for Applied System Analysis (IIASA/ASA)Montanuniversität Leoben - Department Petroleum Engineering - Chair of Drilling and Completion Engineering (MUL/DPE/CDC)São Paulo State University (Unesp) School of Engineering Department of Mechanics, GuaratinguetáSão Paulo State University (Unesp) School of Engineering Department of Energy, GuaratinguetáSão Paulo State University (Unesp) School of Engineering Department of Mechanics, GuaratinguetáCAPES: 001Universidade Estadual Paulista (Unesp)Universidade Federal do Espírito Santo (UFES)International Institute for Applied System Analysis (IIASA/ASA)Montanuniversität Leoben - Department Petroleum Engineering - Chair of Drilling and Completion Engineering (MUL/DPE/CDC)Barbosa, Luís Felipe F.M. [UNESP]Nascimento, AndreasMathias, Mauro Hugo [UNESP]de Carvalho, João Andrade [UNESP]2019-10-06T16:43:28Z2019-10-06T16:43:28Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.petrol.2019.106332Journal of Petroleum Science and Engineering, v. 183.0920-4105http://hdl.handle.net/11449/18952510.1016/j.petrol.2019.1063322-s2.0-850708794139074899537066812Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Petroleum Science and Engineeringinfo:eu-repo/semantics/openAccess2024-07-01T20:32:29Zoai:repositorio.unesp.br:11449/189525Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:49:45.832163Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
title |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
spellingShingle |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review Barbosa, Luís Felipe F.M. [UNESP] Artificial intelligence Data-driven model Drilling optimization Multi-objective optimization ROP |
title_short |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
title_full |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
title_fullStr |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
title_full_unstemmed |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
title_sort |
Machine learning methods applied to drilling rate of penetration prediction and optimization - A review |
author |
Barbosa, Luís Felipe F.M. [UNESP] |
author_facet |
Barbosa, Luís Felipe F.M. [UNESP] Nascimento, Andreas Mathias, Mauro Hugo [UNESP] de Carvalho, João Andrade [UNESP] |
author_role |
author |
author2 |
Nascimento, Andreas Mathias, Mauro Hugo [UNESP] de Carvalho, João Andrade [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal do Espírito Santo (UFES) International Institute for Applied System Analysis (IIASA/ASA) Montanuniversität Leoben - Department Petroleum Engineering - Chair of Drilling and Completion Engineering (MUL/DPE/CDC) |
dc.contributor.author.fl_str_mv |
Barbosa, Luís Felipe F.M. [UNESP] Nascimento, Andreas Mathias, Mauro Hugo [UNESP] de Carvalho, João Andrade [UNESP] |
dc.subject.por.fl_str_mv |
Artificial intelligence Data-driven model Drilling optimization Multi-objective optimization ROP |
topic |
Artificial intelligence Data-driven model Drilling optimization Multi-objective optimization ROP |
description |
Drilling wells in challenging oil/gas environments implies in large capital expenditure on wellbore's construction. In order to optimize the drilling related operation, real-time decisions making have been put in place, so that prediction of rate of penetration (ROP) with accuracy is essential. Despite many efforts (theoretical and experimental) throughout the years, modeling the ROP as a mathematical function of some key variables is not so trivial, due to the highly non-linearity behavior experienced. Therefore, several researches in the recent years have been proposing to use data-driven models from artificial intelligence field for ROP prediction and optimization. This paper presents an extensive review of the literature on ROP prediction, especially, with machine learning techniques, as well as how these models can be used to optimize the drilling activities. The ROP models are classified as traditional models (based on physics-models), statistical models (e.g. multiple regression), or machine learning methods. This review enables to see that machine learning techniques can potentially outperform in terms of ROP-prediction accuracy on top of traditional or statistical models. Throughout this work, an extensive analysis of different ways of obtaining ROP models is carried out, concluding with different strategies adopted in literature to perform data-driven model optimization. Despite the saving potential which can be achieved with real-time optimization based on data-driven ROP models, it is noticeable that there is a lack of implementation of those techniques in the industry, as per literature review. To take a step forward in real implementations, the petroleum industry must be aware that yet no rule of thumb already exists on this specific area, but still, good and very reasonable results can be achieved by following the best practices identified in this review. In addition, the modern practices of machine learning provide promising guidelines for implementing projects in oil and gas industry. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T16:43:28Z 2019-10-06T16:43:28Z 2019-12-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.petrol.2019.106332 Journal of Petroleum Science and Engineering, v. 183. 0920-4105 http://hdl.handle.net/11449/189525 10.1016/j.petrol.2019.106332 2-s2.0-85070879413 9074899537066812 |
url |
http://dx.doi.org/10.1016/j.petrol.2019.106332 http://hdl.handle.net/11449/189525 |
identifier_str_mv |
Journal of Petroleum Science and Engineering, v. 183. 0920-4105 10.1016/j.petrol.2019.106332 2-s2.0-85070879413 9074899537066812 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Petroleum Science and Engineering |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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 |
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1808128569660932096 |