An Accurate Prediction of CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation Algorithm (ACE)

Abstract

AbstractMiscible gas injection nowadays becomes an imperative enhanced oil recovery (EOR) approach for increasing oil recovery. Due to the massive cost associated with this approach a high degree of accuracy is required for predicting the outcome of the process. Such accuracy includes, the preliminary screening parameters for gas miscible displacement; the "minimum miscibility pressure" (MMP) and the availability of the gas. All conventional and stat-of-the-art MMP measurement methods are either time consuming or decidedly cost demanding. Therefore, in order to address the immediate industry demands a nonparametric approach (ACE) is employed in this study to estimate an important parameter MMP. ACE algorithm correlates optimal transforms of a set of predictors with an optimal response transform. Finally, the proposed model has produced a maximum linear effect between these transformed variables. More than 100 MMP data points are considered both from the relevant published literature and experimental work. The test data points also MMP measurements that are experimentally obtained for Kuwaiti crude Oil. The proposed model is validated using detailed statistical analysis and it reveals that the results are more reliable than the existing correlations for pure CO2 injection to enhance oil recovery. In addition to its accuracy, the ACE approach is more powerful, quick and can handle a huge data.