Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
This work addresses uncertainty in subsurface parameter estimation for applications like geothermal energy and carbon sequestration, representing an incremental improvement in multi-physics data integration.
The paper tackles the problem of characterizing subsurface fracture networks by developing a sequential inversion framework that integrates geophysical and flow data to constrain Discrete Fracture Networks, demonstrating its efficacy through a synthetic example.
Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties in the subsurface parameters, which need to be estimated from multiple diverse as well as fragmented data streams. In this paper, we present a non-intrusive sequential inversion framework, for integrating data from geophysical and flow sources to constraint subsurface Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained based on the flow data. The efficacy of this multi-physics based sequential inversion is demonstrated through a representative synthetic example.