Akbari Ali, Maleki Mehdi, Kazemzadeh Yousef, Ranjbar Ali
Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
Sci Rep. 2025 Apr 21;15(1):13718. doi: 10.1038/s41598-025-98613-9.
Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N), methane (CH), and carbon dioxide (CO) lead to contamination, reduced purity, and increased purification costs. Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (K) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. Various ML models-including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs)-were employed to quantify K as a function of pressure (P) and displacement velocity (U). Among these methods, RF outperformed the others, achieving an R of 0.9965 for test data and 0.9999 for training data, with RMSE values of 0.023 and 0.001, respectively. The findings highlight the potential of ML-driven approaches in optimizing UHS operations by enhancing predictive accuracy, reducing computational costs, and mitigating hydrogen contamination risks.
储氢是确保可持续能源转型的一项关键技术。在枯竭的油气藏、含水层和盐穴中进行地下储氢（UHS）提供了一种可行的大规模解决方案。然而，氢气在诸如氮气（N）、甲烷（CH）和二氧化碳（CO）等缓冲气体中的扩散会导致污染、纯度降低以及净化成本增加。现有的预测氢气扩散系数（K）的实验和数值方法通常受到高成本、冗长的处理时间以及在动态储层条件下精度不足的限制。本研究通过将实验数据与先进的机器学习（ML）技术相结合来对氢气扩散进行建模，从而应对这些挑战。采用了各种ML模型，包括随机森林（RF）、最小二乘提升（LSBoost）、贝叶斯回归、线性回归（LR）、人工神经网络（ANN）和支持向量机（SVM），以将K量化为压力（P）和位移速度（U）的函数。在这些方法中，RF的表现优于其他方法，测试数据的R值为0.9965，训练数据的R值为0.9999，均方根误差（RMSE）值分别为0.023和0.001。研究结果凸显了ML驱动方法在通过提高预测精度、降低计算成本和减轻氢气污染风险来优化地下储氢操作方面的潜力。
