Enhancing Effective Thermal Conductivity Predictions in Digital Porous Media Using Transfer Learning

Mohamed Elmorsy; Wael El-Dakhakhni; Benzhong Zhao

doi:10.69631/ipj.v2i3nr75

Authors

Mohamed Elmorsy Department of Civil Engineering, McMaster University, Hamilton, ON, Canad https://orcid.org/0000-0002-7983-6139
Wael El-Dakhakhni Department of Civil Engineering, McMaster University, Hamilton, ON, Canada; School of Computational Science & Engineering, McMaster University, Hamilton, ON, Canada https://orcid.org/0000-0001-8617-261X
Benzhong Zhao Department of Civil Engineering, McMaster University, Hamilton, ON, Canada https://orcid.org/0000-0003-1136-9957

DOI:

https://doi.org/10.69631/ipj.v2i3nr75

Keywords:

Digital Rock Physics, Machine learning, Geothermal energy, Effective thermal conductivity

Abstract

Porous media beneath the Earth’s surface, including aquifers, oil and gas reservoirs, and geothermal systems, play a crucial role in various natural resource management and environmental engineering applications. The study of their physical properties, particularly thermo-physical properties like effective thermal conductivity (ETC), is essential for enhancing the efficiency of subsurface engineering technologies including nuclear waste disposal, geothermal energy utilization, and underground thermal energy storage. Traditionally, determining ETC has relied on either simplified empirical models, which often lack accuracy, or sophisticated laboratory experiments, which are time-consuming and resource intensive. The advent of three-dimensional (3D) imaging technologies has enabled digital characterization of subsurface media, but direct numerical simulations of ETC remain computationally prohibitive. In response to these challenges, we introduce a novel machine learning framework that leverages transfer learning to enhance the prediction of ETC in digital rock samples. Our approach utilizes state-of-the-art convolutional neural networks (CNNs), pre-trained on extensive datasets, and applies them to various porous media samples, including Berea sandstone, Bentheimer sandstone, and Ketton limestone. By employing transfer learning, we demonstrate that our models can achieve high prediction accuracy with significantly reduced training time, computational power, and data requirements. This study highlights the potential of transfer learning to advance the efficiency and accuracy of digital rock analysis, offering a promising tool for the rapid and reliable characterization of subsurface properties.

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