No Cover Image

E-Thesis 209 views

A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability / SHAN ZHONG

Swansea University Author: SHAN ZHONG

  • E-Thesis under embargo until: 5th July 2026

DOI (Published version): 10.23889/SUThesis.67186

Abstract

Porous materials play a pivotal role in various natural and industrial processes, with their properties being significantly influenced by stress conditions. Understanding and accurately predicting these properties are crucial for optimizing processes such as oil and gas recovery.Considerable efforts h...

Full description

Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Li, C.
URI: https://cronfa.swan.ac.uk/Record/cronfa67186
first_indexed 2024-07-25T13:57:20Z
last_indexed 2024-11-25T14:19:40Z
id cronfa67186
recordtype RisThesis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2024-07-25T15:03:28.0651742</datestamp><bib-version>v2</bib-version><id>67186</id><entry>2024-07-25</entry><title>A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability</title><swanseaauthors><author><sid>028602f372d1fe48bb0d3cf5fa329c09</sid><firstname>SHAN</firstname><surname>ZHONG</surname><name>SHAN ZHONG</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-07-25</date><abstract>Porous materials play a pivotal role in various natural and industrial processes, with their properties being signi&#xFB01;cantly in&#xFB02;uenced by stress conditions. Understanding and accurately predicting these properties are crucial for optimizing processes such as oil and gas recovery.Considerable efforts have been made in researching the absolute permeability of porous media, encompassing experimental, numerical, empirical, and deep learning methods. How-ever, most research relies on rock samples analyzed in laboratory conditions, which do not accurately re&#xFB02;ect their operational environment. Consequently, the stress-sensitivity of permeability has emerged as a focal point of research.Additionally, numerous researchers have conducted laboratory experiments on this topic and have achieved promising results. Permeability typically follows a power law with respect to compressive stress, although the parameters may vary among different samples. These experiments are invariably time-consuming, and many interference factors cannot be entirely eliminated. With the advancement of Micro-CT technology, simulations based on digital rock samples have become an indispensable tool. There exists an extensive body of literature on the deformation of porous media through the creation of numerical models from digital rock samples. However, the research focused on simulating &#xFB02;uid &#xFB02;ow within porous media under varying stress conditions remains an area that requires substantial effort.This thesis endeavors to establish a comprehensive framework for investigating the behavior of porous materials under varying stress conditions, with the ultimate goal of achieving precise real-world property approximations. The thesis adopts a multi-faceted approach to tackle the challenges associated with the analysis of porous materials under stress. Initially, it reviews various methods for obtaining permeability in a succinct yet comprehensive manner. Subsequently, it introduces a Discrete Element Method (DEM) for modeling the shapes of real digital rock samples. The control parameters in&#xFB02;uencing the quality of the &#xFB01;nal discrete element model are quantitatively analyzed, demonstrating the method&#x2019;s ef&#xFB01;cacy in constructing discrete element models of porous media based on 3D digital images. Furthermore, the thesis calculates and presents the trends of several descriptors along with the deformation of porous media. Leveraging the permeability calculated under different deformations, and taking descriptors and 3D images as inputs, a neural network is shown to pro&#xFB01;ciently predict permeability across different deformation levels (with strain ranging from 0 to 0.03). Additionally, the polynomial interpolation method is employed to provide an explicit equation for calculating permeability from descriptors. The thesis then proceeds to validate an open-source lattice Boltzmann method based on Palabos for simulating multiphase &#xFB02;ow in porous media, including a quantitative analysis of key parameters to offer insights into their selection. Following this parameter selection, the relative permeability of the sphere-packing model is calculated, and the neural network is applied to forecast relative permeability in two-phase &#xFB02;ow within porous media.To further enhance this research, future endeavors are recommended. These include incorporating a more diverse range of porous media in the neural network&#x2019;s training dataset and developing empirical formulas and feature selection methods for greater reusability. Addi-tionally, the analysis of different calculation methods for descriptors lacking clear de&#xFB01;nitions,such as to rtuosity and fractal dimension, holds promise.In summary, this thesis establishes a foundational framework for investigating porous materials&#x2019; properties under varying stress, providing valuable insights and a solid base for future research in this critical &#xFB01;eld.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea University, Wales, UK</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Porous media, permeability, DEM, deep learning</keywords><publishedDay>5</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-07-05</publishedDate><doi>10.23889/SUThesis.67186</doi><url/><notes>A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information.</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Li, C.</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>China Scholarship Council, Zienkiewicz Center for Computational Engineering</degreesponsorsfunders><apcterm/><funders>China Scholarship Council, Zienkiewicz Center for Computational Engineering</funders><projectreference/><lastEdited>2024-07-25T15:03:28.0651742</lastEdited><Created>2024-07-25T14:47:45.4297483</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>SHAN</firstname><surname>ZHONG</surname><order>1</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2024-07-25T14:55:24.3524104</uploaded><type>Output</type><contentLength>48581740</contentLength><contentType>application/pdf</contentType><version>E-Thesis</version><cronfaStatus>true</cronfaStatus><embargoDate>2026-07-05T00:00:00.0000000</embargoDate><documentNotes>Copyright: The Author, Shan Zhong, 2024</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2024-07-25T15:03:28.0651742 v2 67186 2024-07-25 A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability 028602f372d1fe48bb0d3cf5fa329c09 SHAN ZHONG SHAN ZHONG true false 2024-07-25 Porous materials play a pivotal role in various natural and industrial processes, with their properties being significantly influenced by stress conditions. Understanding and accurately predicting these properties are crucial for optimizing processes such as oil and gas recovery.Considerable efforts have been made in researching the absolute permeability of porous media, encompassing experimental, numerical, empirical, and deep learning methods. How-ever, most research relies on rock samples analyzed in laboratory conditions, which do not accurately reflect their operational environment. Consequently, the stress-sensitivity of permeability has emerged as a focal point of research.Additionally, numerous researchers have conducted laboratory experiments on this topic and have achieved promising results. Permeability typically follows a power law with respect to compressive stress, although the parameters may vary among different samples. These experiments are invariably time-consuming, and many interference factors cannot be entirely eliminated. With the advancement of Micro-CT technology, simulations based on digital rock samples have become an indispensable tool. There exists an extensive body of literature on the deformation of porous media through the creation of numerical models from digital rock samples. However, the research focused on simulating fluid flow within porous media under varying stress conditions remains an area that requires substantial effort.This thesis endeavors to establish a comprehensive framework for investigating the behavior of porous materials under varying stress conditions, with the ultimate goal of achieving precise real-world property approximations. The thesis adopts a multi-faceted approach to tackle the challenges associated with the analysis of porous materials under stress. Initially, it reviews various methods for obtaining permeability in a succinct yet comprehensive manner. Subsequently, it introduces a Discrete Element Method (DEM) for modeling the shapes of real digital rock samples. The control parameters influencing the quality of the final discrete element model are quantitatively analyzed, demonstrating the method’s efficacy in constructing discrete element models of porous media based on 3D digital images. Furthermore, the thesis calculates and presents the trends of several descriptors along with the deformation of porous media. Leveraging the permeability calculated under different deformations, and taking descriptors and 3D images as inputs, a neural network is shown to proficiently predict permeability across different deformation levels (with strain ranging from 0 to 0.03). Additionally, the polynomial interpolation method is employed to provide an explicit equation for calculating permeability from descriptors. The thesis then proceeds to validate an open-source lattice Boltzmann method based on Palabos for simulating multiphase flow in porous media, including a quantitative analysis of key parameters to offer insights into their selection. Following this parameter selection, the relative permeability of the sphere-packing model is calculated, and the neural network is applied to forecast relative permeability in two-phase flow within porous media.To further enhance this research, future endeavors are recommended. These include incorporating a more diverse range of porous media in the neural network’s training dataset and developing empirical formulas and feature selection methods for greater reusability. Addi-tionally, the analysis of different calculation methods for descriptors lacking clear definitions,such as to rtuosity and fractal dimension, holds promise.In summary, this thesis establishes a foundational framework for investigating porous materials’ properties under varying stress, providing valuable insights and a solid base for future research in this critical field. E-Thesis Swansea University, Wales, UK Porous media, permeability, DEM, deep learning 5 7 2024 2024-07-05 10.23889/SUThesis.67186 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Li, C. Doctoral Ph.D China Scholarship Council, Zienkiewicz Center for Computational Engineering China Scholarship Council, Zienkiewicz Center for Computational Engineering 2024-07-25T15:03:28.0651742 2024-07-25T14:47:45.4297483 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering SHAN ZHONG 1 Under embargo Under embargo 2024-07-25T14:55:24.3524104 Output 48581740 application/pdf E-Thesis true 2026-07-05T00:00:00.0000000 Copyright: The Author, Shan Zhong, 2024 true eng
title A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
spellingShingle A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
SHAN ZHONG
title_short A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
title_full A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
title_fullStr A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
title_full_unstemmed A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
title_sort A novel DEM method to investigate the deformation of porous media and fast prediction of relative permeability and stress-sensitive permeability
author_id_str_mv 028602f372d1fe48bb0d3cf5fa329c09
author_id_fullname_str_mv 028602f372d1fe48bb0d3cf5fa329c09_***_SHAN ZHONG
author SHAN ZHONG
author2 SHAN ZHONG
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.67186
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description Porous materials play a pivotal role in various natural and industrial processes, with their properties being significantly influenced by stress conditions. Understanding and accurately predicting these properties are crucial for optimizing processes such as oil and gas recovery.Considerable efforts have been made in researching the absolute permeability of porous media, encompassing experimental, numerical, empirical, and deep learning methods. How-ever, most research relies on rock samples analyzed in laboratory conditions, which do not accurately reflect their operational environment. Consequently, the stress-sensitivity of permeability has emerged as a focal point of research.Additionally, numerous researchers have conducted laboratory experiments on this topic and have achieved promising results. Permeability typically follows a power law with respect to compressive stress, although the parameters may vary among different samples. These experiments are invariably time-consuming, and many interference factors cannot be entirely eliminated. With the advancement of Micro-CT technology, simulations based on digital rock samples have become an indispensable tool. There exists an extensive body of literature on the deformation of porous media through the creation of numerical models from digital rock samples. However, the research focused on simulating fluid flow within porous media under varying stress conditions remains an area that requires substantial effort.This thesis endeavors to establish a comprehensive framework for investigating the behavior of porous materials under varying stress conditions, with the ultimate goal of achieving precise real-world property approximations. The thesis adopts a multi-faceted approach to tackle the challenges associated with the analysis of porous materials under stress. Initially, it reviews various methods for obtaining permeability in a succinct yet comprehensive manner. Subsequently, it introduces a Discrete Element Method (DEM) for modeling the shapes of real digital rock samples. The control parameters influencing the quality of the final discrete element model are quantitatively analyzed, demonstrating the method’s efficacy in constructing discrete element models of porous media based on 3D digital images. Furthermore, the thesis calculates and presents the trends of several descriptors along with the deformation of porous media. Leveraging the permeability calculated under different deformations, and taking descriptors and 3D images as inputs, a neural network is shown to proficiently predict permeability across different deformation levels (with strain ranging from 0 to 0.03). Additionally, the polynomial interpolation method is employed to provide an explicit equation for calculating permeability from descriptors. The thesis then proceeds to validate an open-source lattice Boltzmann method based on Palabos for simulating multiphase flow in porous media, including a quantitative analysis of key parameters to offer insights into their selection. Following this parameter selection, the relative permeability of the sphere-packing model is calculated, and the neural network is applied to forecast relative permeability in two-phase flow within porous media.To further enhance this research, future endeavors are recommended. These include incorporating a more diverse range of porous media in the neural network’s training dataset and developing empirical formulas and feature selection methods for greater reusability. Addi-tionally, the analysis of different calculation methods for descriptors lacking clear definitions,such as to rtuosity and fractal dimension, holds promise.In summary, this thesis establishes a foundational framework for investigating porous materials’ properties under varying stress, providing valuable insights and a solid base for future research in this critical field.
published_date 2024-07-05T14:41:44Z
_version_ 1822141662597480448
score 11.048626

Similar Items