Journal
APPLIED ENERGY
Volume 314, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.118985
Keywords
CO2 storage; Carbon capture and storage; Machine learning; XGBoost; Saline aquifers
Categories
Funding
- Korea Institute of Energy Technol-ogy Evaluation and Planning (KETEP) - Korea gov-ernment (MOTIE) [20212010200010]
- Korea Institute of Energy Technology Evaluation & Planning (KETEP) [20212010200010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this study, machine learning models were developed to predict the efficiency of carbon dioxide sequestration in saline formations, and the XGBoost model achieved the highest accuracy. This research provides a valuable and viable template for predicting the CO2 trapping index in other saline formations and can be used as a tool for assessing the uncertainty of carbon storage projects.
Carbon dioxide storage in underground saline aquifers is considered a promising technique for decreasing atmospheric CO2 emissions. The CO2 residual and solubility in deep saline aquifers are crucial processes for improving CO2 storage security. In this study, the trapping efficiency of CO2 sequestration in saline formations was predicted by developing three supervised machine learning (ML)-based models: random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR). A diverse field-scale simulation database of 1509 samples were collected from the literature, and the proposed models were examined for training and testing. To verify the prediction accuracy of the three ML models, the prediction results were analysed and compared using graphical and statistical indicators. From the prediction results, the proposed ML models were ranked based on their accuracy: XGBoost > RF > SVR. The XGBoost-based predictive model achieved an extremely low root mean square error (RMSE = 0.0041) and high correlation factor (R-2 = 0.9993) for both residual and solubility trapping efficiency. However, RF and SVR exhibited RMSEs of 0.0243 and 0.074 and R-2 values of 0.9781 and 0.9284, respectively. Furthermore, the applicability of the XGBoost model was validated and only 15 suspected data points were detected across the entire database. Therefore, the proposed model can be a valuable and viable template for predicting the CO2 trapping index in other saline formations worldwide. Utimately, the XGBoost model has been tested against reservoir simulation models in comprehensive blind testing and may be used as a robust screening and process planning tool for the uncertainty assessment of carbon storage projects.
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