Interfacial Tension–Temperature–Pressure–Salinity Relationship for the Hydrogen–Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning

Sina Omrani, Mehdi Ghasemi, Mrityunjay Singh, Saeed Mahmoodpour, Tianhang Zhou, Masoud Babaei, Vahid Niasar

Research output: Contribution to journalArticlepeer-review

Abstract

Hydrogen (H 2) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H 2. Nevertheless, successful execution and long-term storage and withdrawal of H 2 necessitate a thorough understanding of the physical and chemical properties of H 2 in contact with the resident fluids. As capillary forces control H 2 migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H 2 and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H 2-brine systems under a wide range of thermodynamic conditions (298-373 K temperatures and 1-30 MPa pressures) and NaCl salinities (0-5.02 mol·kg -1). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H 2-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl 2, and MgCl 2. Our results show that H 2-brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca 2+ can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination (R 2) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.

Original languageEnglish
Pages (from-to)12680-12691
Number of pages12
JournalLangmuir
Volume39
Issue number36
Early online date31 Aug 2023
DOIs
Publication statusPublished - 12 Sept 2023

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