Fluid Flow-based Deep Learning (FFDL) for geologic CO 2 Storage

Carbon capture and storage (CCS) is one of the few strategies for reducing CO 2 emissions by injecting it into deep geologic formations. The injection of CO 2 into heterogeneous rock formations triggers complex coupled flow and transport processes that are not trivial to describe and predict. Advanced numerical simulation is often used as a standard tool to predict the spatial-temporal evolution of CO 2 plume and induced pressure changes. However, numerical simulation is computationally demanding, limits the use of standard field management workflows, and hinders real-time analysis and decision-making for risk mitigation. Standard deep learning models provide powerful alternative prediction tools. However, they have important limitations, including lack of interpretability, extensive data needs, and physical inconsistency. To overcome these limitations, a Fluid Flow-based Deep Learning (FFDL) architecture is presented for spatial-temporal prediction of the injected CO 2 in storage formations. The architecture of FFDL consists of a physics-based encoder to construct physically meaningful latent variables, and a residual-based processor to predict the evolution of state variables. The FFDL model uses physical operators that serve as nonlinear activation functions and impose hard constraints to respect the general structure of the fluid flow equations. A comprehensive investigation of FFDL, based on a field-scale saline aquifer, is used to demonstrate its superior performance compared to standard deep learning models. The results show that FFDL exhibits strong generalization capability and provides more reliable and physically consistent predictions of CO 2 plume migration. The flexibility of FFDL makes it suitable for various applications, including decision-making, optimization, and inverse modeling.