A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage Available to Purchase

Simulating CO₂ storage under geomechanical risks frequently involves substantial computational costs due to the coupling between multiphase flow and geomechanics. Implementing standard workflows, such as well location optimization, with such coupled physics models can significantly increase the computational overhead and make the models impractical to use. 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. The proposed approach leverages deep learning-based surrogate modeling to significantly enhance the efficiency of coupled flow-geomechanics simulations for identifying suitable injection well locations for CO₂ storage. Using simulated data, we train a U-Net convolutional neural network to learn a mapping between well locations s and spatially distributed model parameters (permeability) to the simulation outputs of interest. Once trained with a fixed set of model input parameters, the U-Net model can map different well location scenarios to the corresponding pressure fields, CO₂ saturation, and geomechanical outputs, including vertical displacement and plastic strain. The U-Net model is subsequently adopted as an efficient tool to replace the coupled flow-geomechanics simulation needed for identification of injection well locations to minimize geomechanical risks. We report preliminary results showing that the trained U-Net model can predict pressure and saturation fields from well locations, with all the other inputs remaining consistent with the simulation model used in the training. We investigate the performance of the network under different assumptions and for estimating different flow and geomechanical outputs. The results show that the U-Net model can drastically 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 location and injection strategies. The developed framework can be used to improve the computational demand of coupled-physics modeling and facilitate its application to decision-making workflows and field management.