In Geological Carbon Sequestration (GCS), mineralization is a secure carbon dioxide (CO2) trapping mechanism to prevent possible leakage at later stage of the GCS project. Modeling of the mineralization during GCS relies on numerical reservoir simulation, but the computational cost is prohibitively high due to the complex physical processes. Therefore, deep learning (DL) models can be used as a computationally cheaper and at the same time, reliable alternative to the conventional numerical simulators.

In this work, we have developed a DL approach to effectively predict the dissolution and precipitation of various important minerals, including Anorthite, Kaolinite, and Calcite during CO2 injection into deep saline aquifers. We established a reservoir model to simulate the process of geological CO2 storage. About 750 simulations were performed in order to generate a comprehensive dataset for training DL models. Fourier Neural Operator (FNO) models were trained on the simulated dataset, which take the reservoir and well properties along with time information as input and predict the precipitation and dissolution of minerals in space and time scales. During the training process, root-mean-squared-error (RMSE) was chosen as the loss function to avoid overfitting. To gauge prediction performance, we applied the trained model to predict the concentrations of different mineral on the test dataset, which is 10% of the entire dataset, and two metrics, including the average absolute percentage error (AAPE) and the coefficient of determination (R2) were adopted. The R2 value was found to be around 0.95 for calcite model, 0.94 for Kaolinite model, and 0.93 for Anorthite model. The R2 was calculated for all trainable points from the predictions and ground truth. On the other hand, the average AAPE for all the mappings was calculated around 1%, which demonstrates that the trained model can effectively predict the temporal and spatial evolution of the mineral concentrations. The prediction CPU time (0.2 seconds/case) by the model is much lower than that of the physics-based reservoir simulator (3600 seconds/case). Therefore, the proposed method offers predictions as accurate as our physics-based reservoir simulations, while provides a huge saving of computation time. To the authors' best knowledge, prediction of the precipitation and dissolution of minerals in a supervised learning approach using the simulation data has not been studied before in the literature. The DL models developed in this study can serve as a computationally faster alternative to conventional numerical simulators to assess mineralization trapping in GCS projects especially for the mineral trapping mechanism.

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