Fractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models.Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat.The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing.For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded.For tracer tomography, a conservative tracer is injected Bun Pan Carts in different well levels and the depth-dependent breakthrough of the tracer is monitored.
A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics.The used Metropolis-Hastings-Green algorithm is refined Twin Upholstered Headboard by the simultaneous estimation of the measurement error’s variance, that is, the measurement noise.Based on the presented application to invert the two-dimensional cross-section between source and the receiver well, the hydraulic tomography reveals itself to be more suitable for reconstructing the original DFN.This is based on a probabilistic representation of the inverted results by means of fracture probabilities.