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Cite Details

Z. Zhou, D. Roubinet and D. M. Tartakovsky, "Thermal experiments for fractured rock characterization: theoretical analysis and inverse modeling", Water Resour. Res., vol. 57, no. 12, doi:10.1029/2021WR030608, pp. e2021WR030608, 2021


Field-scale properties of fractured rocks play crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross-borehole thermal experiments data. It is based on a particle-based heat-transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of heat-transfer model runs, and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to-be-estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding non-uniform prior information related to the DFN connectivity improves the inference of this parameter.

BibTeX Entry

author = {Z. Zhou and D. Roubinet and D. M. Tartakovsky},
title = {Thermal experiments for fractured rock characterization: theoretical analysis and inverse modeling},
year = {2021},
urlpdf = {},
journal = {Water Resour. Res.},
volume = {57},
number = {12},
doi = {10.1029/2021WR030608},
pages = {e2021WR030608}