使用数据驱动的地下储存技术建模气-盐水表面张力:专注于耗尽的气体储
Mohammed Sofian1, Abdulazeez Abdulraheem1, Talal Al Shafloot1,2
1Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, 31261 Dhahran, Saudi Arabia.
ACS omega
|February 9, 2026
概括
机器学习模型准确地预测地下储存的表面张力. 这加快了对耗尽的气体储备进行评估,以寻找高效的清洁能源解决方案.
科学领域:
- 地质科学 地质科学
- 储能 储能 储能 储能 储能 储能
- 计算化学的计算化学
背景情况:
- (H2) 是一个重要的清洁能源载体,但大规模储存具有挑战性.
- 在耗尽的气体储中储存H2是有希望的,但气-盐水接口的表面张力 (ST) 影响效率.
- 准确的ST预测对于优化地下形成中的H2储存至关重要.
研究的目的:
- 开发和评估用于预测ST的机器学习 (ML) 模型.
- 在储条件下,在甲混合物和盐水之间模拟ST.
- 为了减少需要昂贵和耗时的实验性ST测量.
主要方法:
- 使用了1050个实验性ST测量的数据集.
- 经过训练和测试的自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN).
- 在ANN中使用贝叶斯规范化和模型不可知解释技术.
主要成果:
- 所有的ML模型都表现出强大的预测性能 (R2>0.96),表现优于经验相关性.
- 级联前向反向传播神经网络 (CFBN) 实现了最高的训练精度 (R2 = 0.9919).
- 前进神经网络 (FNN) 显示了最佳测试性能 (R2 = 0.9849),而ANFIS产生了最低的MAPE.
结论:
- ML模型提供了一种有效和准确的方法来预测地下储存中的ST.
- 密度差异被确定为对ST预测最有影响的特征.
- 这些预测工具可以加速评估和部署气储存站点.
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