Abstract: This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $\textrm{CO}_2$ storage sites often necessitates expensive and involved simulations of $\textrm{CO}_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolution simulation of $\textrm{CO}_2$ plume migration. The model is trained on a comprehensive dataset generated from realistic subsurface parameters and offers $\mathcal{O}(10^5)$ 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 real life systems, paving the way for next-generation digital twins for subsurface CCS applications.