Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System at 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 (H2) underground storage has attained considerable attention as a potentially efficient strategy for the large-scale storage of H2. Nevertheless, successful execution and long-term storage and withdrawal of H2 necessitates a thorough understanding of the physical and chemical properties of H2 in contact with the resident fluids. As capillary forces control H2 migration and trapping in a subsurface environment, quantifying interfacial tension (IFT) between H2 and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a dataset for IFT of H2-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
H2-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared to the experimental data. In addition, the e↵ect of cation type was investigated for brines containing NaCl, KCl, CaCl2, and MgCl2. Our results show that H2-brine IFT reduces with increasing temperature under any pressure condition, while higher NaCl salinity increases IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca2+ can increase IFT values more than others, i.e. up to 12% with respect to KCl. In the last step, the predicted IFT dataset was used to provide a reliable correlation using Machine Learning (ML). Three white-box ML approaches of 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
(R2) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%,
respectively.
Original languageEnglish
JournalLangmuir
Publication statusAccepted/In press - 15 Aug 2023

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