Abstract
The diffusion coefficient (DC) of CO2 in brine is a key parameter in geological carbon sequestration and CO2-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10−9 m2/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R2, RMSE, and MAE. The RF model demonstrated the best performance, with an R2 of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R2 of 0.925, and XGBoost achieved an R2 of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO2 diffusion in brine, providing a robust, data-driven framework for optimizing CO2-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications.