Prediction of interfacial wetting behavior of H2/mineral/brine; implications for H2 geo-storage

• Published in: Journal of energy storage Vol. 72; p. 108567
• Main Authors: Kohzadvand, Kamyab, Kouhi, Maryam Mahmoudi, Barati, Ali, Omrani, Sina, Ghasemi, Mehdi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30-11-2023
• Subjects: Contact angle; Least square support vector machine; Machine learning; Underground hydrogen storage; Underground hydrogen storage; Least square support vector machine; Contact angle; Machine learning
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2023.108567

Underground hydrogen storage (UHS) is a suitable option for large-scale and long-term storing of hydrogen (H2), which is considered a highly promising energy carrier, offering an efficient solution to replace fossil fuels and achieve net-zero carbon emission targets. The successful storage and withdrawal of H2 are highly dependent on the wettability of the H2/mineral/brine system. Despite the importance of the interfacial wetting behavior of H2/mineral/brine in sedimentary formations, no accurate and generalized model has been developed yet for a fast and valid prediction of the contact angle. In the context, three intelligent models, namely multi-layer perceptron (MLP), radial basis function (RBF), and least square support vector machine (LSSVM) have been proposed to accurately predict the contact angle of H2/mineral/brine systems. Another primary objective is to thoroughly scrutinize the extent to which various parameters influence the contact angle. Results of models approved their capability in validly predicting the contact angle for a wide range of mineral types, considering the effects of thermodynamic conditions, brine salinity, cushion gases, surface coating, and surface roughness. The results also demonstrated that the LSSVM model outperforms the MLP and RBF models with coefficients of determination (R2), root mean square error (RMSE) and average absolute relative deviation (AARD) of 0.9942, 1.6133 and 1.6924 %, respectively, indicating higher accuracy and reliability. Moreover, sensitivity analysis confirmed that the rock mineralogies, in particular the percentage of quartz and calcite, followed by the presence or absence of cushion gas, as well as surface roughness/contamination exerted a greater influence on predicting the contact angle within the H2/mineral/brine system.
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•Three intelligent models, namely MLP, RBF, and LSSVM models, were used to predict the contact angle of H2/mineral/brine from the perspective of the underground hydrogen storage (UHS) process.•The LSSVM model outperformed the MLP and RBF models with high accuracy over the experimental dataset.•The most influential factors on the contact angle of H2/mineral/brine systems were rock mineralogy and surface roughness/contamination.