The modern energy economy often relies on the injection and withdrawal of fluids through fracture networks in rock. Whether for geothermal systems or the sequestration of anthropogenic waste (e.g. CO2), knowledge of the fracture system geometry and how it responds to perturbations in stress and fluid chemistry is crucial for safe and sustainable long-term management of a site. While many geophysical methods can locate and delineate fractures or faults, there is a need to extract physically measurable parameters that are directly linked to hydraulic and mechanical properties of fractures. I will present what we have learned from active and passive monitoring of fractures in the laboratory to illuminate and characterize fractures and an example of machine learning to identified changes in fluid saturation within a fracture set.
The modern energy economy often relies on the injection and withdrawal of fluids through fracture networks in rock. Whether for geothermal systems or the sequestration of anthropogenic waste (e.g. CO2), knowledge of the fracture system geometry and how it responds to perturbations in stress and fluid chemistry is crucial for safe and sustainable long-term management of a site. While many geophysical methods can locate and delineate fractures or faults, there is a need to extract physically measurable parameters that are directly linked to hydraulic and mechanical properties of fractures. I will present what we have learned from active and passive monitoring of fractures in the laboratory to illuminate and characterize fractures and an example of machine learning to identified changes in fluid saturation within a fracture set.