Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators
This addresses the problem of scaling up CCS deployment for global decarbonization by reducing computational bottlenecks in reservoir modeling.
The authors tackled the challenge of computationally expensive modeling for carbon capture and storage (CCS) by introducing Nested Fourier Neural Operator (FNO), which speeds up flow prediction nearly 700,000 times compared to existing methods, enabling real-time high-resolution 3D CO2 storage modeling.
Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainties in evaluating storage opportunities, which can delay the pace of large-scale CCS deployment. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale. Nested FNO produces forecasts at different refinement levels using a hierarchy of FNOs and speeds up flow prediction nearly 700,000 times compared to existing methods. By learning the solution operator for the family of governing partial differential equations, Nested FNO creates a general-purpose numerical simulator alternative for CO2 storage with diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework enables unprecedented real-time modeling and probabilistic simulations that can support the scale-up of global CCS deployment.