Abstract
Abstract Precise estimation of minimum miscibility pressure (MMP) is advantageous for CO 2 -based EOR applications and CO 2 capture, sequestration and utilization. However, experimental methods, including slim tube tests, are costly and time-consuming. In addition, conventional empirical correlations generally have a limited range of applicability. Hence, in this research, four types of databased machine learning techniques, namely categorical boosting, extreme gradient boosting, group method of data handling (GMDH) and genetic programming (GP), were used for MMP estimation. To train the model, a dataset with more than 910 rows of data and multiple features, including molecular weight of light, CO 2 stream impurities, intermediate and heavy hydrocarbon components, as well as mole fractions of different elements, was fed to the models. All the models could accurately predict the MMP in various conditions with R 2 of 0.89–0.99. Among all methods, XGBoost had the highest accuracy, and GP had the lowest. To shed some light on the models' performance, sensitivity analysis and feature importance were performed for the proposed models, and the most impactful features were identified in each model. During the final phase of the research, the model's effectiveness and applicability were compared to four well-established MMP correlations from the literature. The developed model in the current research performed substantially better (up to 35 %). The vast datasets used in this study resulted in models with a much broader range of applicability than most previous experimental and empirical methods.