Abstract
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.
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Acknowledgments
This study was financed in part by the São Paulo Research Foundation (FAPESP), process #2022/05186-4, and by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil" (CAPES) - Finance Code 001.
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Tamoto, H., Contreras, R.C., Santos, F.L.d., Viana, M.S., Gioria, R.d.S., Carneiro, C.d.C. (2023). Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_11
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