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

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.

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• An overview of gradient descent optimization algorithms
• Generative Adversarial Networks
• Deep Learning using Rectified Linear Units (ReLU)
• Tune: A Research Platform for Distributed Model Selection and Training
• Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
• Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities
• Inferring transport characteristics in a fractured rock aquifer by combining single-hole GPR reflection monitoring and tracer test data
• Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena
• Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data
• Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification
• Learning on dynamic statistical manifolds