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

Large-scale underground hydrogen storage (UHS) plays a vital role in energy transition. H-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 CO, is often co-injected with H to retain reservoir pressure. Therefore, it is imperative to accurately predict the (H + CO)-water/brine IFT under UHS conditions. While there have been a number of experimental measurements on H-water/brine and (H + CO)-water/brine IFT, an accurate and efficient (H + CO)-water/brine IFT model under UHS conditions is still lacking. In this work, we use molecular dynamics (MD) simulations to generate an extensive (H + CO)-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 (H-brine/water, CO-brine/water, and (H + CO)-brine/water), rendering generally good performance (with = 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.