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

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFaculty or Architecture and Engineering-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.creatorTamoto, Hugo-
Autor(es): dc.creatorContreras, Rodrigo Colnago-
Autor(es): dc.creatorSantos, Franciso Lledo dos-
Autor(es): dc.creatorViana, Monique Simplicio-
Autor(es): dc.creatorGioria, Rafael dos Santos-
Autor(es): dc.creatorCarneiro, Cleyton de Carvalho-
Data de aceite: dc.date.accessioned2025-08-21T22:20:23Z-
Data de disponibilização: dc.date.available2025-08-21T22:20:23Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-23492-7_11-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249038-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249038-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionUniversity of São Paulo Polytechnic School Department of Mining and Petroleum Engineering, SP-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Letters and Exact Sciences São José do Rio Preto, SP-
Descrição: dc.descriptionMato Grosso State University Faculty or Architecture and Engineering, MT-
Descrição: dc.descriptionFederal University of São Carlos Computing Department, SP-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Letters and Exact Sciences São José do Rio Preto, SP-
Formato: dc.format115-130-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectForecasting Time-series-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectRegression models-
Palavras-chave: dc.subjectSynthetic well-logs-
Título: dc.titleSynthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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