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在地下储存系统中使用机器学习分析和预测的相对透性

Alireza Amjadi¹, Shahin Kord²

¹Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran. alireza.amjadi73@gmail.com.

Scientific reports

|October 29, 2025

概括

机器学习模型准确地预测地下储存气 (UHS) 的相对透性. 高斯过程回归 (GPR) 显示出最佳性能,为优化能源基础设施的传统方法提供了数据驱动的替代方案.

科学领域:

能源科学能源科学
地质科学是地球科学.
计算科学计算科学

背景情况:

地下储存 (UHS) 对能源安全和可持续能源基础设施至关重要.
(H2) 相对透性是影响气体注入和取出过程中UHS效率的关键因素.
现有的实证模型在UHS中难以准确地表示复杂的水相互作用.

研究的目的:

采用和评估各种机器学习 (ML) 技术来预测UHS中的H2相对透性.
将ML模型的性能与传统的实证方法进行比较.
确定最有效的ML方法来优化UHS性能.

主要方法:

使用了130个实验点的数据集,其中包括气体和度,多孔度,盐度和差压等变量.
训练并评估了六种ML模型:多项回归,多层感知器 (MLP),高斯过程回归 (GPR),内核回归 (KRR),随机森林回归 (RFR) 和梯度增强回归 (GBR).

主要成果:

高斯过程回归 (GPR) 显示出最高的预测准确度,达到R2为0.9356,RMSE为0.0280,MAE为0.0178.
与传统的经验方法相比,ML模型在预测H2相对透性方面表现优越.
该研究证实了数据驱动方法在提高UHS系统优化方面的潜力.

结论:

机器学习为预测UHS中的H2相对透性提供了一个强大而准确的工具.
这些发现支持采用数据驱动的战略,以提高地下储存的效率和可靠性.
未来的工作应该扩大数据集,并整合诸如歇斯底里和气体混合等物理现象,以进一步完善模型.

关键词:

高斯过程回归的高斯过程回归.储存气的储存方式机器学习机器学习相对透性的相对透性.地下储存系统是地下储存系统.

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