Fourier Neural Operator based surrogates for $CO_2$ storage in realistic geologies
This work addresses the need for rapid decision-making in carbon capture and storage (CCS) technologies, offering a scalable solution for realistic 3D systems, though it is incremental as it applies an existing FNO method to a new domain with specific improvements.
The study tackled the problem of expensive simulations for CO2 storage site selection by developing a Fourier Neural Operator (FNO) based surrogate model, achieving O(10^5) computational acceleration with minimal accuracy loss for real-time simulation of CO2 plume migration in realistic geologies.
This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $CO_2$ storage sites often necessitates expensive and involved simulations of $CO_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO_2$ plume migration. The model is trained on a comprehensive dataset generated from realistic subsurface parameters and offers $O(10^5)$ computational acceleration with minimal sacrifice in prediction accuracy. We also explore super-resolution experiments to improve the computational cost of training the FNO based models. Additionally, we present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites. This novel framework, based on NVIDIA's Modulus library, will allow rapid screening of sites for CCS. The discussed workflows and strategies can be applied to other energy solutions like geothermal reservoir modeling and hydrogen storage. Our work scales scientific machine learning models to realistic 3D systems that are more consistent with real-life subsurface aquifers/reservoirs, paving the way for next-generation digital twins for subsurface CCS applications.