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 po-tential to significantly reduce screening costs and time required for CO2-foam injection, making it a more viable carbon utilization and EOR strategy.

Automated summary

Machine-learning models are employed in this study to predict oil recovery factor (ORF) during CO2-foam flooding cost-effectively and accurately. The GRNN model outperforms others with an overall mean absolute error of 0.059 and an R2 of 0.9999. This study's novelty lies in developing a machine-learning-based approach for an accurate and cost-effective prediction of ORF in CO2-foam experiments.