Machine Learning-Based Interfacial Tension Equations for (H₂ + CO₂)-Water/Brine Systems over a Wide Range of Temperature and Pressure

Abstract

Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H2-brine interfacial tension (IFT) is a crucial parameter in structural trapping in underground geological locations and gas–water two-phase flow in subsurface porous media. On the other hand, cushion gas, such as CO2, is often co-injected with H2 to retain reservoir pressure. Therefore, it is imperative to accurately predict the (H2 + CO2)-water/brine IFT under UHS conditions. While there have been a number of experimental measurements on H2-water/brine and (H2 + CO2)-water/brine IFT, an accurate and efficient (H2 + CO2)-water/brine IFT model under UHS conditions is still lacking. In this work, we use molecular dynamics (MD) simulations to generate an extensive (H2 + CO2)-water/brine IFT databank (840 data points) over a wide range of temperature (from 298 to 373 K), pressure (from 50 to 400 bar), gas composition, and brine salinity (up to 3.15 mol/kg) for typical UHS conditions, which is used to develop an accurate and efficient machine learning (ML)-based IFT equation. Our ML-based IFT equation is validated by comparing to available experimental data and other IFT equations for various systems (H2-brine/water, CO2-brine/water, and (H2 + CO2)-brine/water), rendering generally good performance (with R 2 = 0.902 against 601 experimental data points). The developed ML-based IFT equation can be readily applied and implemented in reservoir simulations and other UHS applications.

Atomic-Scale Insights into the Phase Behavior of Carbon Dioxide and Water from 313 to 573 K and 8 to 30 MPa

“… 34 Furthermore, molecular-scale perspective has contributed to developing machine learning-based models to estimate the IFT. 35 As illustrated in these examples, a better description of CO 2 + H 2 O systems from atomic-scale perspective would improve the modeling and interpretation of fundamental properties of CCUS.…”

“…Molecular dynamics (MD) simulations have uncovered the mechanism behind the CO 2 –H 2 O IFT (interfacial tension) changes by utilizing descriptors of the molecular arrangementsuch as the number of hydrogen bonds and the orientation angle of moleculesin the interfacial region and the bulk region. − Structural information at the microscopic level, including radial distribution function (RDF), is closely related to transport properties (e.g., refs − ), and it has been applied for estimation and interpretation of dynamical properties; the viscosity of supercritical water formulated as a function of the number of hydrogen bonds and diffusivity of supercritical CO 2 formulated based on RDF . Furthermore, molecular-scale perspective has contributed to developing machine learning-based models to estimate the IFT . As illustrated in these examples, a better description of CO 2 + H 2 O systems from atomic-scale perspective would improve the modeling and interpretation of fundamental properties of CCUS.…”

The Influence of CH4 and CO2 on the Interfacial Tension of H2–Brine, Water–H2–Rock Wettability, and Their Implications on Geological Hydrogen Storage

“…More recently, Xie et al employed MD simulations to generate an extensive data set for building a machine learning model to predict IFT of H 2 +CO 2 –brine over a wide range of reservoir conditions.…”