Machine learning methods for predicting CO2 solubility in hydrocarbons
Yun-Seok Yang, Binshan Ju, LU Guang-zhong, Yingsong Huang
Abstract
The application of carbon dioxide (CO2) in enhanced oil recovery (EOR) has increased significantly, in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies, we trained and predicted the solubility using four machine learning models: support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP). Among four models, the XGBoost model has the best predictive performance, with an R2 of 0.9838. Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained models were compared with existing models, demonstrating higher accuracy and applicability of our models. The developed machine learning-based models provide a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons, which may contribute to the advancement of CO2-related applications in the petroleum industry.