Use of genetic algorithm to predict minimum miscibility pressure (MMP) between flue gases and Oil in design of flue gas injection project

Abstract

There is an increasing global awareness of the detrimental effects of industrial flue gases on the environment. As a consequence, much emphasis is being placed to harness flue gases that contain high CO2 concentrations and sequester them in suitable geological formations. A plausible means of sequestrating flue gases is to inject them into petroleum reservoirs while also enhancing oil recovery. An a priori understanding of the pressure at which various flue gas components, notably CO2, become miscible with reservoir fluids is critical to the design and implementation of a flue gas injection project. A new genetic algorithm (GA)-based correlation has been developed to estimate the flue gas-oil MMP. In developing this correlation, the GA software developed in our earlier work has been modified to account for various component gases in the flue gas stream. The correlation estimates the MMP as a function of the injected gas solvency in the oil. The solvency, in turn, is related to critical properties of the injected gas. The correlation has been successfully validated against published experimental data and several correlations in the literature. It yielded the best match with an average error of 4.7% and a standard deviation of 6.3%, followed by Sebastian et al. correlation with 13.1% error and 22.0% standard deviation and Alston et al. correlation with 14.1% error and 43.2% standard deviation. An advantage of the GA-based correlation over other correlations is that it can be used for gas mixtures with higher N2 concentrations (tested up to 20 mole%) and with non-CO2 component concentrations of H2S, N2, SOx, O2, and C1-C4 up to 78 mole% with a higher accuracy. Equally important, it could be a useful tool when experimental data are not available and/or when developing an optimal and economical laboratory program to estimate the flue gas-oil MMP.