Development of a Response Surface Based Model for Minimum Miscibility Pressure (MMP) Correlation of CO2 Flooding

Abstract

AbstractCorrelations are commonly used to predict CO2 multiple contact miscibility (MMP) since such correlations are generally inexpensive and easy to use. In this study, we used a novel approach based upon four dimensionless scaling groups commonly used for hydrocarbon phase behavior modeling (reduced temperature and acentric factors for light and heavy pseudo components) as well as multivariate regression analysis based on response surface methodology to develop an MMP correlation for a broad range of reservoir oils. Applying the response surface method and multivariate regression analysis made it possible to quantify and rank the effect of each one of the mentioned dimensionless groups on the predicted MMP value. Since reservoir temperature is one of the main parameters, slim tube simulations were performed at four different reservoir temperatures (90, 150, 180, and 220 °F) for all of the fluid models. Based on the results from these simulations, and by performing multi-variate regression analysis, MMP values were correlated using a response surface based on linear, quadratic, and third degree equations. Our new MMP correlation takes into account the important equation-of-state properties for heavy- and light-oil components as well as temperature. Predicted MMP values from the new correlation were compared with previously published MMP correlations and found to have a lower average error.