Hybrid discrete fracture network inversion of hydraulic tomography data from a fractured-porous field site

Abstract

The accurate characterization of hydraulic conductivity heterogeneities in an aquifer is crucial for predicting flow and transport processes correctly. Hydraulic tomography (HT) experiments are often used to infer the hydraulically relevant features, but the correct inversion of the data remains a challenging task. We implemented a discrete fracture network (DFN) inversion approach that is expanded by considering a nonzero matrix permeability. The hybrid model allows the accurate characterization of fractured-porous sites by taking into account both matrix and fracture flow. This novel inversion algorithm is successfully applied to HT data acquired at a field site in Goettingen (Germany), and the results are compared with those of a standard travel time inversion. Furthermore, we validate the inversion results by using them as the underlying material parameters for simulating heat tracer experiments and comparing the modeled temperature responses with those of heat tracer tests actually conducted at the site. It is shown that the DFN ensemble predicts the thermal response of the experiments correctly for the two major fractures in terms of location, amplitude, and time-dependent behavior of the temperature anomaly, as long as the stochastic nature of the results is taken into account. We conclude that considering both matrix and fracture flow in a hybrid DFN inversion approach can lead to significant improvements in flow and transport modeling at fractured-porous sites.