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Geological carbon dioxide (CO 2) sequestration in deep formations involves displacing resident fluids from porous media, and is considered an unstable process due to the unfavorable mobility ratio between the resident and displacing fluid. Although various chemicals have been added to improve the CO 2 sweep efficiency, this often results in pollution to the environment and the formation. Here, a novel green foam was prepared to enhance CO 2 sequestration. The stabilization mechanisms and rheological properties of the CO 2 foam with cellulose nanofibrils (CNFs) and camellia oleifera saponin (COS) were explored. Furthermore, the pore-scale behaviors of the CO 2 foams were investigated using a micromodel. The results demonstrated that the abundant hydrogen bonds between CNFs and COS molecules led to the formation of tight bubble film which could stably encapsulate CO 2 and inhibit its diffusion. The interlacement and entanglement of CNFs endowed liquid phase with high viscosity, which restrained liquid drainage and improved the interfacial viscoelasticity. The pore-scale behaviors showed that the higher viscoelasticity and stability caused large numbers of small bubbles to group together, providing greater flow resistance to control the mobility of the foam. The CNF/COS foam significantly improved the CO 2 saturation in aquifers and oil reservoirs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solubility of CO 2 -N 2 gas mixture is modeled using a large data bank. • Two white-box approaches (GMDH and GEP) are used for modeling. • The results of proposed models are compared to those of equations of state. • GMDH shows the best performance among all the applied models. • Leverage approach demonstrated that the data are valid and modeling is statistically correct. The solubility of CO 2 -N 2 gas mixtures in water is important for CO 2 /flue gas sequestration in aquifer. Having precise thermodynamic measurements as well as credible prediction models to utilize in innovative carbon capture and storage (CCS) systems is critical. In this study, two simple-to-use white-box models, including Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH) models, have been developed using 289 experimental data to predict the solubility of CO 2 -N 2 gas mixtures in aqueous solutions. Four tuned equations of state (EOSs), namely Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), Zudkevitch-Joffe (ZJ), and Redlich-Kwong (RK), as well as the outcomes of the GEP and GMDH, were compared. The results show that the tuned EOSs perform much better than the untuned EOSs. The results show that the GMDH model has the best predictive performance such that the obtained values of root mean square error (RMSE) and coefficient of determination (R2) are 0.000564 and 0.9792, respectively. It should be noted that the GEP model also has acceptable accuracy, with RMSE and R2 values of 0.00081 and 0.9465, respectively. The SRK model obtained the best outcomes among the EOSs for the solubility of the CO 2 -N 2 gas mixture in aqueous solution, with RMSE and R2 values of 0.00128 and 0.9561, respectively. The results also show that the solubility of CO 2 in aqueous solutions is much higher than N 2 , and increasing the pressure increases the solubility of CO 2 and N 2 in aqueous solutions, while increasing the CO 2 content increases and decreases the solubility of CO 2 and N 2 , respectively. Group error analysis also shows that the developed models have less error in low values of temperature, pressure, and CO 2 content. Finally, in order to validate the results of the GMDH and GEP models, the leverage technique has been utilized, which illustrated that 95% of the data are in the valid region, thus, the developed models are statistically reliable. The findings of this study can help for better understanding the solubility process of CO 2 and N 2 in water to overcome thermodynamic and environmental challenges. [ABSTRACT FROM AUTHOR]