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

doi:10.1021/acs.langmuir.3c01424

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 journal › Article › peer-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 H₂in 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 language	English
Pages (from-to)	12680-12691
Number of pages	12
Journal	Langmuir
Volume	39
Issue number	36
Early online date	31 Aug 2023
DOIs	https://doi.org/10.1021/acs.langmuir.3c01424
Publication status	Published - 12 Sept 2023

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title = "Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System at Reservoir Conditions; Integration of Molecular Dynamics and Machine Learning",

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 forH2-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.",

author = "Sina Omrani and Mehdi Ghasemi and Mrityunjay Singh and Saeed Mahmoodpour and Tianhang Zhou and Masoud Babaei and Vahid Niasar",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Published by American Chemical Society",

year = "2023",

month = sep,

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doi = "10.1021/acs.langmuir.3c01424",

language = "English",

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TY - JOUR

T1 - Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System at Reservoir Conditions; Integration of Molecular Dynamics and Machine Learning

AU - Omrani, Sina

AU - Ghasemi, Mehdi

AU - Singh, Mrityunjay

AU - Mahmoodpour, Saeed

AU - Zhou, Tianhang

AU - Babaei, Masoud

AU - Niasar, Vahid

PY - 2023/9/12

Y1 - 2023/9/12

N2 - 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 forH2-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.

AB - 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 forH2-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.

U2 - 10.1021/acs.langmuir.3c01424

DO - 10.1021/acs.langmuir.3c01424

M3 - Article

C2 - 37650690

SN - 0743-7463

VL - 39

SP - 12680

EP - 12691

JO - Langmuir

JF - Langmuir

IS - 36

ER -

Interfacial Tension-Temperature-Pressure-Salinity Relationship for the Hydrogen-Brine System at Reservoir Conditions; Integration of Molecular Dynamics and Machine Learning

Abstract

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