Quantifying the Uncertainty of the 3D Evolution of CO2-Mass and Pressure Plumes for Site Screening Applications with Physics-Informed Fourier Neural Operators

Summary Identification of the most suitable set of target storage sites in terms of capacity and containment early in the project phase is one of the key elements for successful carbon dioxide (CO2) sequestration. There is a need for rapid approximate models that can forecast the migration of CO2-mass and pressure plumes. One family of surrogate models that has proved effective for simulating CO2 plume migration is Fourier neural operator (FNO)-based techniques. We discuss the development of two FNO architectures, standard FNO (3DFNO) and U-Net augmented FNO (U-FNO), to serve as fast simulation engines in a CO2 storage site screening platform using deep neural networks and operator learning. We also discuss various physics embedding training strategies (e.g., mass conservation) for improving the reliability of these artificial intelligence simulations. We provide computational results in 3D plume-migration evolution problems in terms of training costs and prediction accuracy for the 3DFNO and U-FNO protocols. We focus our investigations on layered geology problems typical of the screening stage. In inference mode, both models offer O(105) computational acceleration with respect to conventional reservoir simulation subject to an acceptable level of compromise in accuracy. While U-FNO incurs approximately 50% higher computational costs compared with 3DFNO, it improves the prediction accuracy. We demonstrate that U-FNO yields more accurate predictions than 3DFNO in layered systems for CO2-mass accumulation and CO2-rich phase (gas) saturation, while 3DFNO yields more accurate predictions for evolution of pressure change. The platform over which the FNO engines are deployed is designed for predicting the 3D spatiotemporal evolution of CO2-mass accumulation, gas phase saturation, and pressure in arbitrarily layered systems. The fast simulation platform enables Monte Carlo sampling to compute statistical distribution functions of plume-migration performance indicators. Fast FNO-based surrogate models thus enable a previously impossible Monte Carlo–based uncertainty quantification workflow for predicting CO2-mass plume-migration distance and post-injection pressure evolution.