Modeling of Brine/CO2/Mineral Wettability Using Gene Expression Programming (GEP): Application to Carbon Geo-Sequestration

Carbon geo-sequestration (CGS), as a well-known procedure, is employed to reduce/store greenhouse gases. Wettability behavior is one of the important parameters in the geological CO2 sequestration process. Few models have been reported for characterizing the contact angle of the brine/CO2/mineral system at different environmental conditions. In this study, a smart machine learning model, namely Gene Expression Programming (GEP), was implemented to model the wettability behavior in a ternary system of CO2, brine, and mineral under different operating conditions, including salinity, pressure, and temperature. The presented models provided an accurate estimation for the receding, static, and advancing contact angles of brine/CO2 on various minerals, such as calcite, feldspar, mica, and quartz. A total of 630 experimental data points were utilized for establishing the correlations. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented GEP model accurately predicted the wettability behavior under various operating conditions and a few data points were detected as probably doubtful. The average absolute percent relative error (AAPRE) of the models proposed for calcite, feldspar, mica, and quartz were obtained as 5.66%, 1.56%, 14.44%, and 13.93%, respectively, which confirm the accurate performance of the GEP algorithm. Finally, the investigation of sensitivity analysis indicated that salinity and pressure had the utmost influence on contact angles of brine/CO2 on a range of different minerals. In addition, the effect of the accurate estimation of wettability on CO2 column height for CO2 sequestration was illustrated. According to the impact of wettability on the residual and structural trapping mechanisms during the geo-sequestration of the carbon process, the outcomes of the GEP model can be beneficial for the precise prediction of the capacity of these mechanisms.