Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers

Abstract

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 (R2 = 0.9993) for both residual and solubility trapping efficiency. However, RF and SVR exhibited RMSEs of 0.0243 and 0.074 and R2 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.