A Novel Hybrid Particle Swarm Optimization-Simulated Annealing Approach for CO2-Oil Minimum Miscibility Pressure (MMP) Prediction

Abstract

Summary Minimum miscibility pressure (MMP) is a key parameter for miscible displacement technique during tertiary recovery from petroleum reservoirs. Accurate determination of MMP with respect to the reservoir fluid, rock and thermodynamic conditions is of utmost importance to evaluate the gas injection process, particularly in terms of gas-oil miscibility and operational viability. As experimental techniques for the MMP determination are usually expensive and time-consuming, it is essential to develop a comprehensive, robust and reliable correlation with a wide range of applicability. In this article, we applied a novel Hybrid Particle Swarm Optimization-Simulated Annealing method (HPSOSA) to develop a new correlation to predict CO2-Oil MMP in a wide range of thermodynamic and reservoir conditions using all experimental MMP data by Slim Tube technique available in the open literature. The correlation is able to predict CO2-Oil MMP reliably, satisfying the expected physical trends of MMP as a function of the correlation variables. Moreover, it matches more with the experimental data compared with recently proposed correlations available in the literature. Both average absolute relative error (AARE) and standard deviation (SD) of error obtained from HPSOSA predictions are remarkably low confirming its accuracy and robustness.