Applying SVM framework for modeling of CO2 solubility in oil during CO2 flooding

Abstract

CO2 solubility is one of the most important parameters that affects CO2 flooding, because gas dissolution into crude oil results in oil swelling, viscosity reduction, IFT reduction, oil mobilization, and oil recovery improvement. Therefore, a better understanding of CO2 solubility mechanisms and its influence on physical properties of crude oil are essential to any effective CO2 flooding process. In this study, Least-Square Support Vector Machine (LSSVM) as a newly established soft computing algorithm is applied for developing a new correlative model for CO2 solubility in both dead and live oil systems. CO2 solubility in dead oil is basically affected by the oil saturation pressure (Ps), oil specific gravity (γ), oil molecular weight (MW), and reservoir temperature (T). Moreover, the impact of bubble point pressure is considered in constructing the LSSVM model for the live oil. A number of statistical quality measures are utilized to assess and demonstrate the superior capability of the newly developed LSSVM model in comparison with the previous empirically derived correlations. The average absolute relative deviation (AARD) and coefficient of determination (R2) of 2.2783% and 0.9933 for the dead oil system, and 1.7432% and 0.9958 for the live oil system, respectively, verify the acceptable accuracy and efficient performance of the proposed LSSVM model over a wide range of dataset used in this study within the range of the used databank. However, the impact of CO2 liquefaction pressure is ignored, the LSSVM model gives the best result. In conclusion, it is worth mentioning that the proposed LSSVM model can serve as an accurate correlative tool for fast and effective estimation of CO2 solubility in both dead and live crude oils.