permeability-prediction-via-3d-cnn

This repository is created to document the codes and data used in the research project titled "Generalizable Permeability Prediction of Digital Porous Media via Novel Multi-scale 3D Convolutional Neural Network".

https://github.com/elmorsym1/permeability-prediction-via-3d-cnn

Science Score: 54.0%

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Last synced: 6 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: elmorsym1
Language: Python
Default Branch: main
Homepage:
Size: 4.44 MB

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Stars: 7
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 2

Created over 4 years ago · Last pushed over 1 year ago

Metadata Files

Readme Citation

README.md

Generalizable-Permeability-Prediction-Via-Novel-Multi-Scale-3D-CNN

Data:

The data includes sub-volumes of the following rocks, - Bentheimer Sandstone - Ketton Limestone - Berea Sandstone - Doddington Sandstone - Estaillades Limestone - Carbonate (C1) - Carbonate (C2)

The raw CT rock cores are obtained from the Imperial Colloge London portal.

The sub-volumes are simulated for absolute permeability using OpenFOAM and their results are summerized in the provided excel sheet having the following information,

Number of sub-samples = 65,248

Labels description:

casename = sub-sampling index per rock type sample
porosity = ratio of void fraction
eff_porosity = the connected porosity

rock_type =

       {

       1:Bentheimer Sandstone,

       2:Ketton Limestone,

       3:Berea Sandstone,

       4:Doddington Sandstone,

       5:Estaillades Limestone,

       6:Carbonate (C1),

       7:Carbonate (C2)

       }

AR = anisotropy ratio
DOA = degree of anisotropy
k = absolute permeability

This work has been published under the American Geophysical Union flagship journal: Water Rseourses Research.

Paper link: Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi‐Scale 3D Convolutional Neural Network.

For more information, please contact the repository owner at: elmorsym777@gmail.com

Owner

Name: Mohamed Elmorsy
Login: elmorsym1
Kind: user
Location: Toronto, Canada.
Company: McMaster University

Repositories: 2
Profile: https://github.com/elmorsym1

Ph.D. Candidate at McMaster University.

Citation (CITATION.cff)

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Mohamed
    given-names: Elmorsy
    orcid: https://orcid.org/0000-0002-7983-6139
title: elmorsym1/Permeability-Prediction-Via-3D-CNN: v1.1
version: v1.1
date-released: 2022-02-25

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