Thermal experiments for fractured rock characterization: theoretical analysis and inverse modeling

TL;DR

This paper introduces an inversion method using deep neural network surrogates to estimate fracture density and fractal dimension from thermal borehole data, enhancing subsurface fracture characterization.

Contribution

It develops a novel inverse modeling approach combining particle-based heat transfer modeling with DNN surrogates for efficient parameter inference.

Findings

Fracture density is reliably estimated from thermal data.

Fractal dimension estimation is more challenging but improved with prior information.

The method accelerates fracture network characterization in subsurface studies.

Abstract

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.