Deep Neural Networks for Fast Prediction of Flow and Geomechanical Effects during Geologic CO2 Storage

Summary Numerical simulation is commonly used to predict the response of geologic storage formations to carbon dioxide (CO2) injection, including rock deformation and CO2 plume migration. It provides a useful tool for site screening and implementing standard field development workflows, such as well location optimization. However, coupled physics models are computationally demanding, rendering them impractical for tasks that involve thousands of geologic and development scenarios. We study the feasibility of using deep learning models to significantly reduce the computational overhead associated with simulating and quantifying the geomechanical risks of CO2 storage (i.e., ground surface displacement and subsurface rock failure). Using simulated data, we train a U-Net convolutional neural network to learn a mapping from well locations and spatially distributed model parameters to the simulation outputs of interest, including plastic strain and CO2 saturation that exhibit strong nonlinearity and sharp gradients in space-time. Our proposed architecture maps different well locations to the corresponding pressure fields, CO2 saturation, vertical displacement, and plastic strain. The U-Net model can be adopted as an efficient tool for the site screening and identification of injection well locations to minimize geomechanical risks. The results show that the U-Net model can dramatically reduce the computational cost of well placement workflows by replacing coupled physics simulation with a fast proxy model that can be used to predict the geomechanical risk associated with different well locations and injection strategies. The developed framework can be used to reduce the computational cost of coupled-physics modeling and facilitate its application to decision-making workflows and field management.