Akbari Ali, Ranjbar Ali, Kazemzadeh Yousef, Martyushev Dmitriy A
Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
Department of Oil and Gas Technologies, Perm National Research Polytechnic University, Perm, Russia, 614990.
Sci Rep. 2025 Aug 14;15(1):29846. doi: 10.1038/s41598-025-13982-5.
Accurate estimation of water saturation (Sw) is essential for optimizing oil recovery strategies and is a key component in petrophysical analyses of hydrocarbon reservoirs. Traditional Sw estimation approaches often face limitations due to idealized assumptions, dependency on core-derived parameters, and geological heterogeneity. In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters-Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature-were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. To improve model performance, a Gaussian outlier removal technique was applied to eliminate anomalous data points. The models were rigorously validated using multiple training/testing data splits and ten independent runs to ensure statistical reliability. Among the tested models, SVM achieved the highest accuracy, with R values of 0.9952 (test) and 0.9962 (train) and RMSE values of 0.002 (test) and 0.001 (train). These results demonstrate that ML-particularly SVM-offers a robust and accurate alternative for Sw estimation, supporting more effective reservoir evaluation and oil recovery optimization.
准确估算含水饱和度（Sw）对于优化采油策略至关重要，并且是油气藏岩石物理分析的关键组成部分。传统的Sw估算方法由于理想化假设、对岩心衍生参数的依赖以及地质非均质性等因素，常常面临局限性。在本研究中，利用一个由30660个独立数据点组成的综合数据集来开发用于Sw预测的机器学习（ML）模型。九个测井参数——深度（DEPT）、高温中子孔隙度、真电阻率、计算伽马射线、能谱伽马射线、井径、纵波声波传播时间、体积密度和温度——被用作输入特征，以训练和测试五种ML算法：线性回归、支持向量机（SVM）、随机森林、最小二乘提升和贝叶斯方法。为了提高模型性能，应用了高斯离群值去除技术来消除异常数据点。使用多个训练/测试数据划分和十次独立运行对模型进行了严格验证，以确保统计可靠性。在测试的模型中，SVM取得了最高的准确率，测试集的R值为0.9952，训练集的R值为0.9962，测试集的均方根误差（RMSE）值为0.002，训练集的RMSE值为0.001。这些结果表明，机器学习——尤其是SVM——为Sw估算提供了一种强大且准确的替代方法，有助于更有效地进行油藏评价和采油优化。
