Deep-Learning-Assisted Multiobjective Optimization of Geological CO2 Storage Performance Under Geomechanical Risks

In geological CO₂ storage, designing the optimal well-control strategy for CO₂ injection to maximize CO₂ storage while minimizing the associated geomechanical risks is not trivial. This challenge arises because of pressure buildup, CO₂ plume migration, the highly nonlinear nature of geomechanical responses to rock/fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations.

In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: Maximize total CO₂ storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints.

The geomechanical risks are driven primarily by injection-induced pressure buildup, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep-learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions.

The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes.

A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO₂ storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome.

The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO₂ storage optimization workflow to mitigate the risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO₂ storage operation.

This abstract is taken from paper SPE 220850 by Fangning Zheng, Martin Ma, Hari Viswanathan, and Rajesh Pawar, Los Alamos National Laboratory; Birendra Jha, University of Southern California; and Bailian Chen, Los Alamos National Laboratory. The paper has been peer reviewed and is available as Open Access in SPE Journal on OnePetro.