Machine learning methods applied to drilling rate of penetration prediction and optimization - A review

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dc.contributor.authorBarbosa, Luís Felipe F.M. [UNESP]
dc.contributor.authorNascimento, Andreas
dc.contributor.authorMathias, Mauro Hugo [UNESP]
dc.contributor.authorde Carvalho, João Andrade [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal do Espírito Santo (UFES)
dc.contributor.institutionInternational Institute for Applied System Analysis (IIASA/ASA)
dc.contributor.institutionMontanuniversität Leoben - Department Petroleum Engineering - Chair of Drilling and Completion Engineering (MUL/DPE/CDC)
dc.date.accessioned2019-10-06T16:43:28Z
dc.date.available2019-10-06T16:43:28Z
dc.date.issued2019-12-01
dc.description.abstractDrilling 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.en
dc.description.affiliationSão Paulo State University (Unesp) School of Engineering Department of Energy, Guaratinguetá
dc.description.affiliationUniversidade Federal do Espírito Santo - Department of Engineering and Technology – Petroleum Engineering Programs (UFES/DETEC)
dc.description.affiliationInternational Institute for Applied System Analysis (IIASA/ASA)
dc.description.affiliationMontanuniversität Leoben - Department Petroleum Engineering - Chair of Drilling and Completion Engineering (MUL/DPE/CDC)
dc.description.affiliationSão Paulo State University (Unesp) School of Engineering Department of Mechanics, Guaratinguetá
dc.description.affiliationUnespSão Paulo State University (Unesp) School of Engineering Department of Energy, Guaratinguetá
dc.description.affiliationUnespSão Paulo State University (Unesp) School of Engineering Department of Mechanics, Guaratinguetá
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdCAPES: 001
dc.identifierhttp://dx.doi.org/10.1016/j.petrol.2019.106332
dc.identifier.citationJournal of Petroleum Science and Engineering, v. 183.
dc.identifier.doi10.1016/j.petrol.2019.106332
dc.identifier.issn0920-4105
dc.identifier.lattes9074899537066812
dc.identifier.scopus2-s2.0-85070879413
dc.identifier.urihttp://hdl.handle.net/11449/189525
dc.language.isoeng
dc.relation.ispartofJournal of Petroleum Science and Engineering
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectArtificial intelligence
dc.subjectData-driven model
dc.subjectDrilling optimization
dc.subjectMulti-objective optimization
dc.subjectROP
dc.titleMachine learning methods applied to drilling rate of penetration prediction and optimization - A reviewen
dc.typeArtigo
unesp.author.lattes9074899537066812

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Artigos - Energia - FEG
Artigos - Engenharia Mecânica - FEG