A rigorous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: Application to gas injection processes

Abstract

Determination of interfacial tension (IFT) between the reservoir crude oil and the injecting gas as well as the minimum miscibility pressure (MMP) are the keys for successful gas injection process for enhanced oil recovery (EOR) in the matured oil fields. In this study, a novel supervised learning method called least square support vector machine (LSSVM) was developed to estimate IFT of paraffin-CO2 system. Besides, the MMP of the same system is estimated using the same model by using the vanishing interfacial tension (VIT) technique. The IFT was assumed to be an explicit function of pressure, temperature and molecular weight of paraffin, which was considered as the basis of the proposed model. The results showed that the proposed model is able to predict the IFT values with an average absolute percentage relative error of 4.7%. The highest relative error for estimation of MMP was found to be only 6.79%. Also, relevancy factor showed that pressure has the largest impact on the IFT of paraffin-CO2 systems. At the end, the Leverage approach demonstrated that the proposed model is statistically valid and acceptable and only 3.8% of the data points were out of the applicability domain of the model.