Machine-learning-based prediction of oil recovery factor for experimental CO₂-Foam chemical EOR: Implications for carbon utilization projects

Enhanced oil recovery (EOR) using CO₂ injection is promising with economic and environmental benefits as an active climate-change mitigation approach. Nevertheless, the low sweep efficiency of CO₂ injection remains a challenge. CO₂-foam injection has been proposed as a remedy, but its laboratory screening for specific reservoirs is costly and time-consuming. In this study, machine-learning models are employed to predict oil recovery factor (ORF) during CO₂-foam flooding cost-effectively and accurately. Four models, including general regression neural network (GRNN), cascade forward neural network with Levenberg–Marquardt optimization (CFNN-LM), cascade forward neural network with Bayesian regularization (CFNN-BR), and extreme gradient boosting (XGBoost), are evaluated based on experimental data from previous studies. Results demonstrate that the GRNN model outperforms the others, with an overall mean absolute error of 0.059 and an R² of 0.9999. The GRNN model's applicability domain is verified using a Williams plot, and an uncertainty analysis for CO₂-foam flooding projects is conducted. The novelty of this study lies in developing a machine-learning-based approach that provides an accurate and cost-effective prediction of ORF in CO₂-foam experiments. This approach has the potential to significantly reduce screening costs and time required for CO₂-foam injection, making it a more viable carbon utilization and EOR strategy.