Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
Python code for estimating hydrogen relative permeability from capillary pressure curve, mercury intrusion porosimetry curve or pore-throat size distribution
| Authors: |
|
|
|---|---|---|
| Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 170.6 KB | |
| Created: | Jun 11, 2025 at 2:32 a.m. (UTC) | |
| Last updated: | Jun 11, 2025 at 2:41 a.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
|---|---|
| Views: | 88 |
| Downloads: | 0 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
Abstract
This Python code estimates the hydrogen relative permeability curve from the capillary pressure curve, mercury intrusion porosimetry curve, or pore-throat size distribution. The theoretical foundation is based on combining two upscaling techniques from statistical physics, i.e., percolation theory and effective-medium approximation. We apply the universal scaling law from percolation theory and the effective-medium approximation scaling to model hydrogen relative permeability at low and high hydrogen saturations, respectively.
Subject Keywords
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment