Title
Surrogate models of heat transfer in fractured rock and their use in parameter estimation
Author
Song, G. (TU Delft Applied Geology; China University of Petroleum - Beijing)
Roubinet, Delphine (CNRS/Université de Montpellier II)
Wang, Xiaoguang (Chengdu University of Technology)
Li, Gensheng (China University of Petroleum - Beijing)
Song, Xianzhi (China University of Petroleum - Beijing)
Tartakovsky, Daniel M. (Stanford University)
Date
2023
Abstract
Fracture distribution plays a significant role in the behavior of subsurface environments, affecting such activities as geothermal production, exploitation and management of groundwater resources, and long-term storage of nuclear waste and carbon dioxide. A key challenge in these and other applications is to estimate the fracture network properties from sparse and noisy observations. We evaluate the utility of cross-borehole thermal experiments for this task, using both physics-based particle-tracking (PBPT) heat-transfer approach and its deep neural network (DNN) surrogates. Synthetic data are provided by the PBPT simulations and used to train and test the DNN surrogates over a full range of the fracture network properties. We propose regionalized and step-by-step training techniques to reduce the computational cost of expensive PBPT forward solves over large ranges of the (to-be-estimated) parameters. Our numerical experiments suggest the feasibility of training a regionalized DNN surrogate over parameter ranges for which the PBPT solves are fast and extrapolating its predictions to parameter ranges with few additional data. We analyze the balance between computational cost and model accuracy, and provide both PBPT and DNN models for applications to others kinds of data.
Subject
DFN parameter inversion
Fractured rocks
Heat transfer
Particle tracking
Surrogate model
To reference this document use:
http://resolver.tudelft.nl/uuid:cc6471ea-5d59-45a4-90df-35b2274af624
DOI
https://doi.org/10.1016/j.cageo.2023.105509
Embargo date
2024-06-14
ISSN
0098-3004
Source
Computers & Geosciences: an international journal, 183
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2023 G. Song, Delphine Roubinet, Xiaoguang Wang, Gensheng Li, Xianzhi Song, Daniel M. Tartakovsky