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

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Tamoto, Hugo
Contreras, Rodrigo Colnago [UNESP]
Santos, Franciso Lledo dos
Viana, Monique Simplicio
Gioria, Rafael dos Santos
Carneiro, Cleyton de Carvalho

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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 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.



Forecasting Time-series, Machine learning, Regression models, Synthetic well-logs

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13588 LNAI, p. 115-130.