Multi-output machine learning prediction of CO₂ enhanced oil recovery and geological storage performance
DOI:
https://doi.org/10.31699/IJCPE.2026.1.7Keywords:
CO2-EOR; CO2-Storage; CCUS; Subsurface Storage; Geological Field Data; Machine LearningAbstract
Carbon dioxide-enhanced oil recovery (CO₂-EOR) is one of the best commercial carbon capture, utilization, and storage (CCUS) technologies, enhancing oil recovery and achieving safe geological storage. However, data density and computational costs hinder conventional reservoir simulations. This research presents a multi-output machine learning model to simultaneously predict both incremental oil production and CO₂ storage volume. A selected global dataset of 173 projects was compiled, preprocessed, and optimized. Among the evaluated algorithms (random forest, gradient boosting, and random forest chain), gradient boosting achieved the best predictive performance, with a weighted coefficient of determination (R²) of 0.722. Model analysis showed that permeability, depth, and miscibility strongly influence oil recovery (R² for gradient boosting = 0.87), while the predictability of storage volume is low (R² for gradient e boosting = 0.5), which is significantly affected by reservoir depth and porosity. This study highlights the potential of machine learning as a rapid screening tool for evaluating carbon capture, utilization, and storage projects, identifying the performance of key factors, and emphasizing the need for standardized storage reporting to improve prediction accuracy.
Received on 18/01/2026
Received in Revised Form on 14/03/2026
Accepted on 14/03/2026
Published on 30/03/2026
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