Modeling Flow and Transport in Fractured Rock Using Machine Learning

Explore innovative approaches to modeling flow and transport in fractured rock systems through this 59-minute webinar from the SIAM Geosciences Webinar Series. Delve into the integration of computational physics, machine learning, and graph theory to revolutionize traditional modeling techniques for fractured subsurface environments. Discover how Dr. Gowri Srinivasan from Los Alamos National Lab utilizes compact graph representations and machine learning to efficiently capture micro-fracture information and accelerate models with fewer degrees of freedom. Gain insights into applications such as geologic carbon sequestration, hydraulic fracturing, and underground nuclear test detection. Understand the challenges of incorporating structural information in large-scale fractured systems and learn about the paradigm shift from computationally intensive high-fidelity models to coarse-scale graphs that preserve critical structural data.

Modeling Flow and Transport in Fractured Rock Using Machine Learning