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Date
2026-03-01
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Abstract
This chapter explores the application of machine learning (ML) techniques as an efficient alternative for permeability estimation, leveraging well log data to model complex nonlinear relationships. Various ML methods are discussed and compared, including artificial neural networks (ANNs) and ensemble techniques such as stochastic gradient boosting (SGB). A case study demonstrates the impact of feature selection, hyperparameter tuning, and model evaluation metrics on permeability prediction performance. The findings emphasize that SGB models offer a strong balance between accuracy and computational efficiency, significantly outperforming conventional techniques. Future research directions are highlighted, focusing on integrating physics-informed ML models, uncertainty quantification, transfer learning, and real-time permeability prediction for enhanced reservoir management and characterization.
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Book title
Advances and Applications of Machine Learning in Fluid Flow Problems