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Permeability and pore pressure prediction from well logs using machine learning: A study in the Niger delta
Earth Sciences Department, Federal University of Petroleum Resources, 330102, Effurun, Nigeria.
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 2590–2611.
Article DOI: 10.30574/ijsra.2024.13.2.2376
Publication history:
Received on 26 October 2024; revised on 04 December 2024; accepted on 06 December 2024
Abstract:
Machine learning provides a robust method for characterizing reservoirs in the Niger Delta. This study applied machine learning techniques to well log data to predict permeability and pore pressure. Feature selection identified depth, density, velocity, and porosity as critical variables, while resistivity and neutron porosity (NPHI) showed strong correlations with pore pressure (correlation coefficients: 0.5–1.0). Random forest and gradient boosting emerged as the most effective models, achieving R-squared scores above 0.99 for both permeability and pore pressure predictions. This corresponded to a root mean squared error (RMSE) under 20,000, indicating a precise fit between predicted and actual values. Although the Decision Tree model also performed well (R-squared > 0.99), further optimization could improve its RMSE and generalization. These results highlight the potential of machine learning to enhance reservoir characterization and inform decision-making in oil and gas exploration and production. Accurate predictions of reservoir properties can optimize operations and reduce uncertainties. Future work could expand these findings by integrating additional data, such as 3D seismic information, and applying the models to diverse geological settings. This would improve the robustness and transferability of predictions, enabling more comprehensive reservoir analysis.
Keywords:
Permeability; Pore Pressure; Well Logs; Machine Learning; Random Forest Model; Gradient Boosting Model; Decision Tree Model; R-squared; Root Mean Squared Error
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
