在碳化合物储库中使用机器学习进行增强的水和度估计
Ali Akbari1, Ali Ranjbar2, Yousef Kazemzadeh3
1Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran. aliakbaripetroleum@gmail.com.
Scientific reports
|August 15, 2025
概括
机器学习模型,特别是支持矢量机 (SVM),使用井日志数据准确预测水和 (Sw). 这种方法提高了储量评估,并优化了石油回收策略.
科学领域:
- 石化物理学 石化物理学
- 机器学习 机器学习
- 储水库工程 储水库工程
背景情况:
- 准确的水和 (Sw) 估计对于碳化合物储量分析和石油回收优化至关重要.
- 传统的Sw估计方法由于假设,核心数据依赖性和地质复杂性而存在局限性.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于精确预测水和 (Sw).
- 使用全面的井日志数据集,评估各种ML算法的性能.
主要方法:
- 使用了30660个数据点的数据集,并使用了9个井日志参数.
- 训练并测试了五种ML算法:线性回归,支持向量机 (SVM),随机森林,最小平方提升和贝叶斯方法.
- 应用了高斯异常值去除和严格的模型可靠性的验证技术.
主要成果:
- 支持矢量机 (SVM) 证明了最高的准确性,达到0.9952 (测试) 和0.9962 (列车) 的R2值.
- SVM模型产生了0.002 (测试) 和0.001 (列车) 的低根平均平方误差 (RMSE) 值.
结论:
- 机器学习,特别是SVM,为水和 (Sw) 估计提供了强大而准确的方法.
- 开发的ML模型支持增强储库评估和优化石油回收.
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