Abstract
Deep saline formations are considered as potential sites for geological carbon storage (GCS). To better understand the CO2 trapping mechanism in saline aquifers, it is necessary to develop robust tools to evaluate CO2 trapping efficiency. This paper introduces the application of Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF) to predict CO2 trapping efficiency in saline formations. First, the uncertainty variables, including geologic parameters, petrophysical properties, and other physical characteristics data were utilized to create a training dataset. A total of 101 reservoir simulation samples were then performed, and the residual trapping, solubility trapping, and cumulative CO2 injection were collected. The predicted results indicate that three machine learning (ML) models that evaluate performance from high to low: GPR, SVM, and RF can be selected to predict the CO2 trapping efficiency in deep saline formations. The GPR model has an excellent CO2 trapping prediction efficiency with the highest correlation factor (R2 = 0.992) and lowest root mean square error (RMSE = 0.00491). The accuracy and stability of the GPR models were verified for an actual reservoir in offshore Vietnam. The predictive models obtained a good agreement between the simulated field and the predicted trapping index. These findings indicate that the GPR ML models can support the numerical simulation as a robust predictive tool for estimating the performance of CO2 trapping in the subsurface.
Developing machine learning models to predict CO2 trapping performance in deep saline aquifers
