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The interfacial properties for surfactants at the supercritical CO2-water (C-W) interface at temperatures above 80°C have very rarely been reported given limitations in surfactant solubility and chemical stability. These limitations, along with the weak solvent strength of CO2, make it challenging to design surfactants that adsorb at the C-W interface, despite the interest in CO2-in-water (C/W) foams (also referred to as macroemulsions). Herein, we examine the thermodynamic, interfacial and rheological properties of the surfactant C12-14N(EO)2 in systems containing brine and/or supercritical CO2 at elevated temperatures and pressures. Because the surfactant is switchable from the nonionic state to the protonated cationic state as the pH is lowered over a wide range in temperature, it is readily soluble in brine in the cationic state below pH 5.5, even up to 120°C, and also in supercritical CO2 in the nonionic state. As a consequence of the affinity for both phases, the surfactant adsorption at the CO2-water interface was high, with an area of 207Å(2)/molecule. Remarkably, the surfactant lowered the interfacial tension (IFT) down to ∼5mN/m at 120°C and 3400 psia (23MPa), despite the low CO2 density of 0.48g/ml, indicating sufficient solvation of the surfactant tails. The phase behavior and interfacial properties of the surfactant in the cationic form were favorable for the formation and stabilization of bulk C/W foam at high temperature and high salinity. Additionally, in a 1.2 Darcy glass bead pack at 120°C, a very high foam apparent viscosity of 146 cP was observed at low interstitial velocities given the low degree of shear thinning. For a calcium carbonate pack, C/W foam was formed upon addition of Ca(2+) and Mg(2+) in the feed brine to keep the pH below 4, by the common ion effect, in order to sufficiently protonate the surfactant. The ability to form C/W foams at high temperatures is of interest for a variety of applications in chemical synthesis, separations, materials science, and subsurface energy production.

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CO2-enhanced oil recovery (EOR) is an important development method for the third oil recovery stage, which occupies a certain position in carbon capture, utilization, and storage (CCUS). CO2–EOR has two kinds of displacement states in the reservoir, namely, miscible displacement and immiscible displacement, and the recovery of miscibility is far better than that of immiscible flooding. Minimum miscible pressure (MMP) plays a crucial role in whether the CO2–oil system can achieve miscibility, so accurate MMP prediction is required to formulate the reservoir development plan. Traditional methods such as slim tube experiments are expensive and time-consuming. Empirical formulas perform slightly inferiorly in terms of accuracy and range of use. In recent years, machine learning, which uses more, has improved in accuracy, but the performance of this prediction still needs to be further optimized. The work used a stacking approach, one of the ensemble models, to filter and fuse several basic machine learning models to further improve the regression effect of MMP data. First, the correlation analysis and variance inflation factor of the MMP data in the dataset are carried out, and the redundant data are excluded for the correlation and collinearity problems. A total of 147 pretreated MMP data were then regressed using 7 baseline models, whose results were preliminarily screened and combined with empirical formula data to form a new dataset. After that, the final output result is obtained through a stacking model and evaluated. In addition to fitting curves, the results of the Stacking model demonstrate the improvement of the stacking model in MMP prediction from three aspects: mean absolute error (MAE), root-mean-square error (RMSE), and decision coefficient (R 2).