Precise model for estimating CO2—oil minimum miscibility pressure

Abstract

The CO2—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for CO2 injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the CO2—oil MMP. Knowledge of the CO2—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of CO2 injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating CO2—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “CO2—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the CO2—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for CO2—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265).