No Cover Image

E-Thesis 639 views

Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media / JINLONG FU

Swansea University Author: JINLONG FU

  • E-Thesis – open access under embargo until: 8th February 2026

DOI (Published version): 10.23889/SUthesis.59592

Abstract

Porous media are ubiquitous in the natural environment and engineering, and typical ex-amples include rocks, soils, and concretes. Their transport properties (i.e. permeability, effective diffusivity, formation resistivity factor and thermal conductivity) usually exhibit strong uncertainty, due to t...

Full description

Published: Swansea 2021
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Li, Chenfeng ; Thomas, Hywel R.
URI: https://cronfa.swan.ac.uk/Record/cronfa59592
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-03-11T17:38:59Z
last_indexed 2022-03-17T04:28:52Z
id cronfa59592
recordtype RisThesis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-03-16T15:39:30.8056571</datestamp><bib-version>v2</bib-version><id>59592</id><entry>2022-03-11</entry><title>Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media</title><swanseaauthors><author><sid>f6cb2e8dae591077aaac65d054834fcf</sid><firstname>JINLONG</firstname><surname>FU</surname><name>JINLONG FU</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-03-11</date><abstract>Porous media are ubiquitous in the natural environment and engineering, and typical ex-amples include rocks, soils, and concretes. Their transport properties (i.e. permeability, effective diffusivity, formation resistivity factor and thermal conductivity) usually exhibit strong uncertainty, due to the intricacy, stochasticity and heterogeneity inherent in pore mi-crostructures. Although high-definition visualizations of opaque porous media can be derived from advanced microscopy imaging techniques (such as micro-CT), it remains a critical challenge to effectively link microstructural characteristics to transport properties. This thesis is devoted to improving the understanding of structure-property relationships of porous media, thereby predicting macroscopic transport properties from observable microstructure informatics. The research objective has been achieved through three interrelated modules: stochastic characterization, microstructure reconstruction and predictive model construction.To bridge the gaps between microstructural characteristics and transport properties, quan-titative characterization of porous media in explicit expression is the essential prerequisite, through which the stochastic morphology of complicated microstructures is reduced to a small set of descriptors related to macroscopic transport properties. A comprehensive review of statistical characterization of pore microstructures is presented, where a wide variety of morphological descriptors are collected through an extensive literature survey, to provide microstructural informatics from the global, local, geometrical and topological perspectives. Tortuosity of porous media is one of the key parameters to model transport properties, and it is systematically examined from the viewpoints of concept and evaluation method in this study. The correlations between geometrical and physical tortuosities are further analyzed, based on which phenomenological relations between them are established.With the limited availability of digital microstructures, the inherent stochasticity of porous media can rarely be captured by using a small number of samples. The complete compu-tational dataset should cover the representative samples with all possible configurations. Stochastic reconstruction of 3D pore microstructure is an effective way to provide large num-bers of samples of arbitrary size for analyzing transport properties of porous media. A novel method is presented to statistically characterize and reconstruct heterogeneous microstruc-tures through a deep neural network model, which can generate 3D pore microstructure samples by well preserving statistical equivalence, long-distance connectivity and transport properties. Besides, another new approach is developed to stochastically reconstruct 3D pore microstructures from 2D cross-sectional images through supervised machine learning, which can rapidly produce more realistic and accurate 3D microstructure samples compared with other three classical approaches.The digital microstructure of high-quality provides a high-fidelity framework for pore-scale simulations of fluid flows, permitting one to evaluate transport properties or explore specific physical phenomena. However, the results obtained from low-resolution images of pore microstructures are often compromised with significant errors, known as the resolution effect. The resolution effect on permeability evaluation from the lattice Boltzmann method is quantitatively investigated, and an error correction model is constructed to reduce/eliminate this resolution effect by identifying the primary error causes. The model uses correlated morphological descriptors to quantify the resolution effect and achieve error correction.What&#x2019;s more, the dependence of permeability on microstructural characteristics of porous media is fundamentally studied through feature selection and machine learning. The mor-phological descriptors significant to permeability are highlighted and selected through the performance-driven feature selection. In essence, the selected morphological descriptors provide a deep and interpretable insight into the underlying microstructure-permeability linkage. The machine learning-based permeability model is thus built by using the optimal subset of morphological descriptors as the feature data, and the prediction model exhibits excellent performances in predictive accuracy and general applicability.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Civil Engineering, Computational Engineering, Machine Learning, Porous Rocks, Fluid Dynamics</keywords><publishedDay>8</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-02-08</publishedDate><doi>10.23889/SUthesis.59592</doi><url/><notes>ORCiD identifier: https://orcid.org/0000-0003-2964-4777</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Li, Chenfeng ; Thomas, Hywel R.</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>Zienkiewicz PhD Scholarship; China Scholarship Council</degreesponsorsfunders><apcterm/><lastEdited>2022-03-16T15:39:30.8056571</lastEdited><Created>2022-03-11T17:34:34.1629116</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>JINLONG</firstname><surname>FU</surname><order>1</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2022-03-11T17:54:40.1745261</uploaded><type>Output</type><contentLength>8072340</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><embargoDate>2026-02-08T00:00:00.0000000</embargoDate><documentNotes>Copyright: The author, Jinlong Fu, 2021.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2022-03-16T15:39:30.8056571 v2 59592 2022-03-11 Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media f6cb2e8dae591077aaac65d054834fcf JINLONG FU JINLONG FU true false 2022-03-11 Porous media are ubiquitous in the natural environment and engineering, and typical ex-amples include rocks, soils, and concretes. Their transport properties (i.e. permeability, effective diffusivity, formation resistivity factor and thermal conductivity) usually exhibit strong uncertainty, due to the intricacy, stochasticity and heterogeneity inherent in pore mi-crostructures. Although high-definition visualizations of opaque porous media can be derived from advanced microscopy imaging techniques (such as micro-CT), it remains a critical challenge to effectively link microstructural characteristics to transport properties. This thesis is devoted to improving the understanding of structure-property relationships of porous media, thereby predicting macroscopic transport properties from observable microstructure informatics. The research objective has been achieved through three interrelated modules: stochastic characterization, microstructure reconstruction and predictive model construction.To bridge the gaps between microstructural characteristics and transport properties, quan-titative characterization of porous media in explicit expression is the essential prerequisite, through which the stochastic morphology of complicated microstructures is reduced to a small set of descriptors related to macroscopic transport properties. A comprehensive review of statistical characterization of pore microstructures is presented, where a wide variety of morphological descriptors are collected through an extensive literature survey, to provide microstructural informatics from the global, local, geometrical and topological perspectives. Tortuosity of porous media is one of the key parameters to model transport properties, and it is systematically examined from the viewpoints of concept and evaluation method in this study. The correlations between geometrical and physical tortuosities are further analyzed, based on which phenomenological relations between them are established.With the limited availability of digital microstructures, the inherent stochasticity of porous media can rarely be captured by using a small number of samples. The complete compu-tational dataset should cover the representative samples with all possible configurations. Stochastic reconstruction of 3D pore microstructure is an effective way to provide large num-bers of samples of arbitrary size for analyzing transport properties of porous media. A novel method is presented to statistically characterize and reconstruct heterogeneous microstruc-tures through a deep neural network model, which can generate 3D pore microstructure samples by well preserving statistical equivalence, long-distance connectivity and transport properties. Besides, another new approach is developed to stochastically reconstruct 3D pore microstructures from 2D cross-sectional images through supervised machine learning, which can rapidly produce more realistic and accurate 3D microstructure samples compared with other three classical approaches.The digital microstructure of high-quality provides a high-fidelity framework for pore-scale simulations of fluid flows, permitting one to evaluate transport properties or explore specific physical phenomena. However, the results obtained from low-resolution images of pore microstructures are often compromised with significant errors, known as the resolution effect. The resolution effect on permeability evaluation from the lattice Boltzmann method is quantitatively investigated, and an error correction model is constructed to reduce/eliminate this resolution effect by identifying the primary error causes. The model uses correlated morphological descriptors to quantify the resolution effect and achieve error correction.What’s more, the dependence of permeability on microstructural characteristics of porous media is fundamentally studied through feature selection and machine learning. The mor-phological descriptors significant to permeability are highlighted and selected through the performance-driven feature selection. In essence, the selected morphological descriptors provide a deep and interpretable insight into the underlying microstructure-permeability linkage. The machine learning-based permeability model is thus built by using the optimal subset of morphological descriptors as the feature data, and the prediction model exhibits excellent performances in predictive accuracy and general applicability. E-Thesis Swansea Civil Engineering, Computational Engineering, Machine Learning, Porous Rocks, Fluid Dynamics 8 2 2021 2021-02-08 10.23889/SUthesis.59592 ORCiD identifier: https://orcid.org/0000-0003-2964-4777 COLLEGE NANME COLLEGE CODE Swansea University Li, Chenfeng ; Thomas, Hywel R. Doctoral Ph.D Zienkiewicz PhD Scholarship; China Scholarship Council 2022-03-16T15:39:30.8056571 2022-03-11T17:34:34.1629116 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised JINLONG FU 1 Under embargo Under embargo 2022-03-11T17:54:40.1745261 Output 8072340 application/pdf E-Thesis – open access true 2026-02-08T00:00:00.0000000 Copyright: The author, Jinlong Fu, 2021. true eng
title Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
spellingShingle Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
JINLONG FU
title_short Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
title_full Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
title_fullStr Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
title_full_unstemmed Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
title_sort Stochastic Characterization, Microstructure Reconstruction and Transport Property Prediction for Porous Media
author_id_str_mv f6cb2e8dae591077aaac65d054834fcf
author_id_fullname_str_mv f6cb2e8dae591077aaac65d054834fcf_***_JINLONG FU
author JINLONG FU
author2 JINLONG FU
format E-Thesis
publishDate 2021
institution Swansea University
doi_str_mv 10.23889/SUthesis.59592
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
description Porous media are ubiquitous in the natural environment and engineering, and typical ex-amples include rocks, soils, and concretes. Their transport properties (i.e. permeability, effective diffusivity, formation resistivity factor and thermal conductivity) usually exhibit strong uncertainty, due to the intricacy, stochasticity and heterogeneity inherent in pore mi-crostructures. Although high-definition visualizations of opaque porous media can be derived from advanced microscopy imaging techniques (such as micro-CT), it remains a critical challenge to effectively link microstructural characteristics to transport properties. This thesis is devoted to improving the understanding of structure-property relationships of porous media, thereby predicting macroscopic transport properties from observable microstructure informatics. The research objective has been achieved through three interrelated modules: stochastic characterization, microstructure reconstruction and predictive model construction.To bridge the gaps between microstructural characteristics and transport properties, quan-titative characterization of porous media in explicit expression is the essential prerequisite, through which the stochastic morphology of complicated microstructures is reduced to a small set of descriptors related to macroscopic transport properties. A comprehensive review of statistical characterization of pore microstructures is presented, where a wide variety of morphological descriptors are collected through an extensive literature survey, to provide microstructural informatics from the global, local, geometrical and topological perspectives. Tortuosity of porous media is one of the key parameters to model transport properties, and it is systematically examined from the viewpoints of concept and evaluation method in this study. The correlations between geometrical and physical tortuosities are further analyzed, based on which phenomenological relations between them are established.With the limited availability of digital microstructures, the inherent stochasticity of porous media can rarely be captured by using a small number of samples. The complete compu-tational dataset should cover the representative samples with all possible configurations. Stochastic reconstruction of 3D pore microstructure is an effective way to provide large num-bers of samples of arbitrary size for analyzing transport properties of porous media. A novel method is presented to statistically characterize and reconstruct heterogeneous microstruc-tures through a deep neural network model, which can generate 3D pore microstructure samples by well preserving statistical equivalence, long-distance connectivity and transport properties. Besides, another new approach is developed to stochastically reconstruct 3D pore microstructures from 2D cross-sectional images through supervised machine learning, which can rapidly produce more realistic and accurate 3D microstructure samples compared with other three classical approaches.The digital microstructure of high-quality provides a high-fidelity framework for pore-scale simulations of fluid flows, permitting one to evaluate transport properties or explore specific physical phenomena. However, the results obtained from low-resolution images of pore microstructures are often compromised with significant errors, known as the resolution effect. The resolution effect on permeability evaluation from the lattice Boltzmann method is quantitatively investigated, and an error correction model is constructed to reduce/eliminate this resolution effect by identifying the primary error causes. The model uses correlated morphological descriptors to quantify the resolution effect and achieve error correction.What’s more, the dependence of permeability on microstructural characteristics of porous media is fundamentally studied through feature selection and machine learning. The mor-phological descriptors significant to permeability are highlighted and selected through the performance-driven feature selection. In essence, the selected morphological descriptors provide a deep and interpretable insight into the underlying microstructure-permeability linkage. The machine learning-based permeability model is thus built by using the optimal subset of morphological descriptors as the feature data, and the prediction model exhibits excellent performances in predictive accuracy and general applicability.
published_date 2021-02-08T04:17:01Z
_version_ 1763754138015891456
score 11.016235

Similar Items