Saline aquifer storage is considered to be a promising method of carbon dioxide (CO) mitigation. The CO-brine interfacial tension (IFT) and the caprock wettability under reservoir temperature and pressure conditions are essential for storage capacity estimation. In this study, the CO-brine (NaCl + KCl) IFTs were obtained by using the pendant drop method under 298-373 K temperature, 3-15 MPa pressure, and 1.0-4.9 mol·kg salinity. A detailed analysis of the relationship of IFT with temperature, pressure, and salinity was conducted. In addition, an empirical equation was developed to estimate the CO-brine IFTs in a wide range of temperatures, pressures, and salt molality. The contact angles (CAs) of brine on quartz, Berea Sandstone, and limestone surfaces in the presence of supercritical, liquid, and gaseous CO were measured by using the sessile drop method, and the wettability alteration of the rock surfaces in the presence of supercritical CO was systematically investigated. According to the results, the CO-brine IFTs increased with salinity and temperature and decreased with pressure until reaching a plateau. For a CO-mixed brine system, a linear relationship between the IFT increase (Δγ) and molality was observed. The CAs of the different rock samples varied with temperature and pressure. However, all the three rock samples became less water-wet when the CO phase state changed from subcritical to supercritical.
During the CO 2 injection of geological carbon sequestration and CO 2enhanced oil recovery, the contact of CO 2 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 CO 2 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 CO 2 and brine.
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