On the determination of CO2–crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods

Abstract

In addition to reducing carbon dioxide (CO2) emission, the high oil recovery efficiency achieved by CO2 injection processes makes CO2 injection a desirable enhance oil recovery (EOR) technique. Minimum miscibility pressure (MMP) is an important parameter in successful designation of any miscible gas injection process such as CO2 flooding; therefore, its accurate determination is of great importance. The current experimental techniques for determining MMP are expensive and time-consuming. In this study, multi-gene genetic programming has been combined with constrained multivariable search methods, and a simple empirical model has been developed which provides a reliable estimation of MMP in a wide range of reservoirs, injection gases and crude oil systems. The experimental data for developing the proposed correlation consists of 270 data points from twenty-six authenticated literature sources. This model utilizes reservoir temperature, molecular weight of C5+, volatile (N2 and C1) to intermediate (H2S, CO2, C2, C3, C4) ratio and pseudo critical temperature of the injection gas as input parameters. Both statistical and graphical error analyses have been employed to evaluate the accuracy and validity of the proposed model compared to the pre-existing correlations. The results showed that the new model provides an average absolute relative error of 11.76%. Moreover, the relevancy factor indicated that the reservoir temperature has the greatest impact on the minimum miscibility pressure.