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

Interfacial tension (IFT) 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 IFT at varying temperature, aiming to achieve accurate predictions of IFT 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 IFT 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 IFT, demonstrating that varying temperature significantly influences the dependence of IFT on chemical components in gas and brine by causing the variation in their solubility. Specifically, the ratio of IFT 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 IFT and quantifying their respective weight, we derived a new piecewise function for predicting IFT 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.