Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Abstract

Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

Physics-Based Proxy Modeling of CO2 Sequestration in Deep Saline Aquifers

“…Mineral trapping offers the advantage of an increased storage capacity and CO 2 immobilization for a long period [66]. Even though mineralization takes a long time, the trapping process can be sped up by finding optimal parameters, including the system pressure and temperature, brine composition, pH, sweep efficiency, reservoir formation, mineral trapping kinetics, and stress impact on the caprock [68][69][70][71][72]. Figure 7 illustrates the amount of carbon dioxide mineralized throughout time.…”