Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models

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dc.contributor.authorTamoto, Hugo
dc.contributor.authorContreras, Rodrigo Colnago [UNESP]
dc.contributor.authorSantos, Franciso Lledo dos
dc.contributor.authorViana, Monique Simplicio
dc.contributor.authorGioria, Rafael dos Santos
dc.contributor.authorCarneiro, Cleyton de Carvalho
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionFaculty or Architecture and Engineering
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2023-07-29T14:00:40Z
dc.date.available2023-07-29T14:00:40Z
dc.date.issued2023-01-01
dc.description.abstractThe shear slowness well-log is a fundamental feature used in reservoir modeling, geomechanics, elastic properties, and borehole stability. This data is indirectly measured by well-logs and assists the geological, petrophysical, and geophysical subsurface characterization. However, the acquisition of shear slowness is not a standard procedure in the well-logging program, especially in mature fields that have a limited logging scope. In this research, we propose to develop machine learning models to create synthetic shear slowness well-logs to fill this gap. We used standard well-log features such as natural gamma-ray, density log, neutron porosity, resistivity logs, and compressional slowness as input data to train the models, and successfully predicted a synthetic shear slowness well-log. Additionally, we created five supervised models using Neural Networks, AdaBoost, XGBoost, and CatBoost algorithms. Among all models created, the neural network algorithm provided the most optimized model, using multi-layer perceptron architecture reaching impressive scores as R 2 of 0.9306, adjusted R 2 of 0.9304, and MSE less than 0.0694.en
dc.description.affiliationUniversity of São Paulo Polytechnic School Department of Mining and Petroleum Engineering, SP
dc.description.affiliationSão Paulo State University Institute of Biosciences Letters and Exact Sciences São José do Rio Preto, SP
dc.description.affiliationMato Grosso State University Faculty or Architecture and Engineering, MT
dc.description.affiliationFederal University of São Carlos Computing Department, SP
dc.description.affiliationUnespSão Paulo State University Institute of Biosciences Letters and Exact Sciences São José do Rio Preto, SP
dc.format.extent115-130
dc.identifierhttp://dx.doi.org/10.1007/978-3-031-23492-7_11
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13588 LNAI, p. 115-130.
dc.identifier.doi10.1007/978-3-031-23492-7_11
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85148062356
dc.identifier.urihttp://hdl.handle.net/11449/249038
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.subjectForecasting Time-series
dc.subjectMachine learning
dc.subjectRegression models
dc.subjectSynthetic well-logs
dc.titleSynthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Modelsen
dc.typeTrabalho apresentado em evento
unesp.author.orcid0000-0001-8719-9419[1]
unesp.author.orcid0000-0003-4003-7791[2]
unesp.author.orcid0000-0002-7718-8203[3]
unesp.author.orcid0000-0002-2960-8293[4]
unesp.author.orcid0000-0001-8715-5125[5]
unesp.author.orcid0000-0002-4032-200X[6]

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Artigos - Petrologia e Metalogenia - IGCE