P-wave velocity log simulation for petroelastic inversion using gardner's equation, neural networks and ensemble methods based on trees: a case study of the Santos basin, Brazil

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dc.creatorCaique Pinheiro de Carvalho
dc.creatorJéssica Lia Santos da Costa
dc.creatorTobias Fonte-Boa
dc.creatorTiago Amâncio Novo
dc.creatorMaria José Campos de Oliveira
dc.creatorFernanda Moura Costa
dc.date.accessioned2026-02-13T21:44:19Z
dc.date.issued2024-06-20
dc.description.sponsorshipOutra Agência
dc.identifier.doi10.22564/brjg.v42i2.2315
dc.identifier.issn2764-8044
dc.identifier.urihttps://hdl.handle.net/1843/1663
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofBrazilian Journal of Geophysics
dc.rightsAcesso aberto
dc.subjectAprendizagem de máquina
dc.subjectRedes neurais artificiais
dc.subjectEquação de Gardner
dc.subjectPropriedades petrofísicas
dc.subjectPerfilagem de poços
dc.subjectBrasil
dc.subject.otherWell logging
dc.subject.otherArtificial neural networks
dc.subject.otherMachine learning
dc.subject.otherLinear regression
dc.titleP-wave velocity log simulation for petroelastic inversion using gardner's equation, neural networks and ensemble methods based on trees: a case study of the Santos basin, Brazil
dc.typeArtigo de periódico
local.citation.epage15
local.citation.issue2
local.citation.spage1
local.citation.volume42
local.creator.Latteshttp://lattes.cnpq.br/8955625542421457
local.creator.Latteshttp://lattes.cnpq.br/8176978471786960
local.creator.Latteshttp://lattes.cnpq.br/0711275437074965
local.description.resumoOne of the main tools for reservoir characterization is analyzing well log data. The importance of such methods stems from petrophysical property estimation, such as porosity, which is very important to the oil and gas industry. In scenarios where data are hard to collect, data loss and technical failures during the acquisition impose an extra challenge. Thus, mathematical and petrophysical models are good candidates to fill information gaps in the well log dataset. In such a way, the rock petroelastic and petrophysical properties can be successfully estimated. Several studies correlate the velocity of compressional waves (VP ) to other basic well data. In this study, we used the Gardner’s equation and Machine Learning methods such as Neural Networks, Random Forest and Gradient Boosting regressions to generate VP logs. We used real-world data acquired from twenty wells of the pre-salt formation from Santos Basin in Brazil to train and test the Machine Learning methods and evaluated the data estimated by those models using statistical metrics. We calculated the acoustic impedance from the estimated logs and used it to create a prior model for a petroelastic inversion, which allowed us to estimate the natural logarithm of the acoustic impedance for a seismic volume. The Machine Learning methods presented less errors between estimated and measured velocities when compared to Gardner’s equation.
local.identifier.orcidhttps://orcid.org/0000-0003-3335-3462
local.identifier.orcidhttps://orcid.org/0000-0003-3091-3727
local.identifier.orcidhttps://orcid.org/0000-0002-2105-9154
local.identifier.orcidhttps://orcid.org/0000-0002-1999-862X
local.identifier.orcidhttps://orcid.org/0000-0003-0114-2564
local.identifier.orcidhttps://orcid.org/0000-0002-9564-6466
local.publisher.countryBrasil
local.publisher.departmentIGC - DEPARTAMENTO DE GEOLOGIA
local.publisher.initialsUFMG
local.subject.cnpqCIENCIAS EXATAS E DA TERRA
local.url.externahttps://sbgf.org.br/revista/index.php/rbgf/article/view/2315/1958

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