Comparative Analysis of Four Neural Network Models on the Estimation of CO₂-Brine Interfacial Tension

During the CO₂ injection of geological carbon sequestration and CO₂-enhanced oil recovery, the contact of CO₂ with underground salt water is inevitable, where the interfacial tension (IFT) between gas and liquid determines whether the projects can proceed smoothly. In this paper, three traditional neural network models, the wavelet neural network (WNN) model, the back propagation (BP) model, and the radical basis function model, were applied to predict the IFT between CO₂ and brine with temperature, pressure, monovalent cation molality, divalent cation molality, and molar fraction of methane and nitrogen impurities. A total of 974 sets of experimental data were divided into two data groups, the training group and the testing group. By optimizing the WNN model (I_WNN), a most stable and precise model is established, and it is found that temperature and pressure are the main parameters affecting the IFT. Through the comparison of models, it is found that I_WNN and BP models are more suitable for the IFT evaluation between CO₂ and brine.