Fracture Network

Fracture networks, representing interconnected cracks and fissures in geological formations, are crucial for understanding fluid flow and reactive transport in subsurface environments. Current research emphasizes using numerical models, including discrete fracture network (DFN) simulations coupled with machine learning techniques, to analyze how network topology and geochemical reactions interact to control fluid flow and mineral dissolution/precipitation. This work is particularly relevant to applications like geological carbon sequestration, where predicting the effectiveness of CO2 mineralization hinges on accurately modeling fluid pathways and reactive processes within these complex networks. Improved understanding of fracture network behavior will enhance the prediction and management of subsurface resources and environmental processes.