Prediction of CO2 Minimum Miscibility Pressure MMP Using Machine Learning Techniques

Abstract

SummaryMinimum Miscibility Pressure (MMP) is a key design parameter for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, MMP value is used to tune compositional models along with proper fluid description. In general, CO2 and Hydrocarbon gases are the most common gases used for (or screened for) gas injection processes and due to recent focus to screen for the coupling of CO2-sequestration and CO2-EOR projects. As CO2-Oil phase behavior is quite different than the hydrocarbon gas-oil phase behavior, researchers developed specialized correlations for CO2. Therefore, there is a need for a tool with expanded range capabilities for MMP for CO2 gas streams. The only known measurement technique for MMP that is coherent with its definition is the use of a Slim-Tube which also restricts the amount of data available even though there are other alternative techniques presented over the last 3 decades which all suffer from various limitations. Since correlations are inexpensive one of the inexpensive and easy ways to calculate the MMP, therefore there have been several correlations developed in past based on correlative physics [9], [18], [24], [28], [50], [80], [82] and phase behavior properties of the oil - CO2 mixture [3], [5], [44]. This paper present two separate approaches to calculating the MMP of oil during pure CO2 injection, (1) Analytical correlation where the correlation coefficients were tuned using linear SVM [39], [67] and 2) using a hybrid method (combination of random forest regression [11] and proposed correlation) which very nicely captures the dynamic behavior of CO2. The model takes the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and reservoir temperature as input parameters. Based on statistical analysis and cross-plots we showed that the performance of the final proposed method is superior to all the leading correlations [9], [18], [24], [28], [50], [80], [82] and supervised machine learning [55] methods considered in this work [10], [11], [14], [15], [39], [67]. The proposed model works for the widest spectrum of MMP from 1000 to 4900 psia which cover the entire range oils within the scope of CO2 EOR based on the screening criteria [54], [75].