Application of machine learning to predict CO2 trapping performance in deep saline aquifers

Deep saline formations are considered potential sites for geological carbon storage. 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. In total, 101 reservoir simulations were then performed, and residual trapping, solubility trapping, and cumulative CO2 injection were analyzed. The predicted results indicated 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 had an excellent CO2 trapping prediction efficiency with the highest correlation factor (R2 = 0.992) and the lowest root mean square error (RMSE = 0.00491). Also, the predictive models obtained good agreement between the simulated field and 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. [Display omitted] •This paper introduces three machine learning methods for predicting carbon storage in saline aquifers.•The prediction performance from high to low is Gaussian Process Regression, Support Vector Machine, and Random forest.•Gaussian Process Regression achieves a good agreement between the predicted results and field-scale simulation.•Blind testing is necessary for preventing overfitting in machine learning models.•Machine learning methods could support the numerical simulation as a fast predictive tool for carbon geological storage.