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

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Tamoto, Hugo ; Contreras, Rodrigo Colnago ; dos Santos, Franciso Lledo ; Viana, Monique Simplicio ; Gioria, Rafael dos Santos ; Carneiro, Cleyton de Carvalho ; Rutkowski, L ; Scherer, R ; Korytkowski, M ; Pedrycz, W ; Tadeusiewicz, R ; Zurada, JM

• Tipo de documento: Artigo Científico
• Fonte: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I; v. 13588, p. 16-pg., 2023-01-01.

Resumo

The 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 thewell-logging program, especially in mature fields that have a limited logging scope. In this research, we propose to developmachine 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 multilayer perceptron architecture reaching impressive scores as R-2 of 0.9306, adjusted R-2 of 0.9304, and MSE less than 0.0694. (AU)

• Processo FAPESP: 22/05186-4 - Aprimorando os Sistemas de Autenticação Biométrica por Voz: Robustez Mediante Disfonias de Curta Duração
• Beneficiário: Rodrigo Colnago Contreras
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico