Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs

Abstract

Accurate prediction of CO₂ storage mass and cumulative oil production is critical in the context of combining subsurface carbon capture with enhanced oil recovery (CCS-EOR). This study introduces a novel committee machine-learning Gaussian Process Regression (CML-GPR) model, which integrates three well-established machine-learning algorithms—Random Forest (RF), Multi-Layer Extreme Learning Machine (MELM), and Generalized Regression Neural Network (GRNN). The combination of these models leverages the strengths of each algorithm: RF captures nonlinear relationships, MELM enhances computational efficiency, and GRNN provides smooth, generalized predictions. By integrating these complementary techniques, the CML-GPR model demonstrates significant improvements in predictive accuracy over individual models, addressing limitations in their performance. The model predicts CO₂ storage mass and cumulative oil production based on nine key reservoir input variables, including depth, porosity, and CO₂ injection rate, among others. Utilizing a large dataset of 21,193 data points from reservoir simulations, a Mahalanobis distance-based outlier detection method further refines the input data quality. The CML-GPR model achieves root mean square error (RMSE) values of 0.49 million metric tons for CO₂ storage mass and 13.68 million barrels for cumulative oil production, significantly outperforming individual models. The CML-GPR model provides a robust tool for optimizing CO₂ storage capacity and oil recovery, with practical implications for real-world reservoir management, ensuring more efficient and reliable CCS-EOR operations. This study represents a pioneering advancement in predictive modeling, offering valuable insights for optimizing both CO₂ storage and enhanced oil recovery in complex reservoirs.