Estimation of CO2-Brine Interfacial Tension Based on an Advanced Intelligent Algorithm Model: Application for Carbon Saline Aquifer Sequestration.

The emission reduction of the main greenhouse gas, CO2, can be achieved via carbon capture, utilization, and storage (CCUS) technology. Geological carbon storage (GCS) projects, especially CO2 storage in deep saline aquifers, are the most promising methods for meeting the net zero emission goal. The safety and efficiency of CO2 saline aquifer storage are primarily controlled by structural and capillary trapping, which are significantly influenced by the interactions between fluid and solid phases in terms of the interfacial tension (IFT) between the injected CO2 and brine at the reservoir site. In this study, a model based on the random forest (RF) model and the Bayesian optimization (BO) algorithm was developed to estimate the IFT between the pure and impure gas-brine binary systems for application to CO2 saline aquifer sequestration. Then three heuristic algorithms were applied to validate the accuracy and efficiency of the established model. The results of this study indicate that among the four mixed models, the Bayesian optimized random forest model fits the experimental data with the smallest root-mean-square error (RMSE = 1.7705) and mean absolute percentage error (MAPE = 2.0687%) and a high coefficient of determination (R2 = 0.9729). Then the IFT values predicted via this model were used as an input parameter to estimate the CO2 sequestration capacity of saline aquifers at different depths in the Tarim Basin of Xinjiang, China. The burial depth had a limited influence on the CO2 storage capacity.

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.

Measurement and estimation of CO2-brine interfacial tension and rock wettability under CO2 sub- and super-critical conditions.

Saline aquifer storage is considered to be a promising method of carbon dioxide (CO2) mitigation. The CO2-brine interfacial tension (IFT) and the caprock wettability under reservoir temperature and pressure conditions are essential for storage capacity estimation. In this study, the CO2-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-1 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 CO2-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 CO2 were measured by using the sessile drop method, and the wettability alteration of the rock surfaces in the presence of supercritical CO2 was systematically investigated. According to the results, the CO2-brine IFTs increased with salinity and temperature and decreased with pressure until reaching a plateau. For a CO2-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 CO2 phase state changed from subcritical to supercritical.