Enhanced Prediction of CO2-Brine Interfacial Tension at Varying Temperature Using a Multibranch-Structure-Based Neural Network Approach

Langmuir. 2025 Jan 14. doi: 10.1021/acs.langmuir.4c03366. Online ahead of print.

Abstract

Interfacial tension (IFTC-B) between CO2 and brine depends on chemical components in multiphase systems, intricately evolving with a change in temperature. In this study, we developed a convolutional neural network with a multibranch structure (MBCNN), which, in combination with a compiled data set containing measurement data of 1716 samples from 13 available literature sources at wide temperature and pressure ranges (273.15-473.15 K and 0-70 MPa), was used to quantitatively explore the correlation of various chemical components with IFTC-B at varying temperature, aiming to achieve accurate predictions of IFTC-B under complex conditions. Our multibranch neural network analysis yielded some important insights: (1) Leveraging the convolutional and multibranch structure, MBCNN effectively mitigates the adverse effects of sparse matrices resulting from the absence of certain basic components, exhibiting higher prediction accuracy particularly for low IFTC-B scenarios (MAE = 0.47, and R2 = 0.9921) than other AI models. (2) The multibranch structure allows MBCNN to additionally capture the interattribute relationship between temperature and each chemical component. Such interattribute relationships are quantitatively correlated with IFTC-B, demonstrating that varying temperature significantly influences the dependence of IFTC-B on chemical components in gas and brine by causing the variation in their solubility. Specifically, the ratio of IFTC-B to the molality of monovalent cations (Na+ and K+) and bivalent cations (Ca2+ and Mg2+) in brine, as well as to the mole fraction of non-CO2 components (CH4 and N2) in the gas phase, varies with increasing temperature, approximately following a quadratic function. (3) By formulating the effect of each attribute on IFTC-B and quantifying their respective weight, we derived a new piecewise function for predicting IFTC-B at three temperature intervals (T ≤ 293.15 K, 293.15 K < T ≤ 324.4 K, and T > 324.4 K), with high prediction performance (MAE = 2.3672, R2 = 0.9263) across a wide temperature range in saline aquifers.