Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding

Abstract Flooding fuel-generated CO2 into oil reservoirs will lead to two advantages in both enhanced oil recovery (EOR) and reduction in atmospheric emission of carbon dioxide. The main factor for determination of the possibilities to EOR by e.g. CO2 injection, in particular miscible case, into a specific oil reservoir is the CO2-oil minimum miscibility pressure (MMP). In this communication, we present the utilization of a soft-computing technique - gene expression programming (GEP) - for developing a new symbolic equation to pursue our objective. In other words, this work presents a new approach to predict both pure and impure CO2-oil MMP in live reservoir oil systems. The parameters of the new model involve the molecular weight of C5+ fraction in crude oil, reservoir temperature, the mole percentage ratio of volatile to intermediate components of oil and critical temperature. A comprehensive error investigation is done to discuss accuracy of the recently proposed MMP model. Additionally, the results obtained by the new GEP-based model are compared with most widely-used empirically derived correlations available in the literature to show the superiority of the model. The results obtained in this study are encouraging and can propose accurate and efficient solutions for the case of CO2-oil MMP of pure and impure components in live oil systems.