Imperial College London
Portrait Picture

ProfessorMartinBlunt

Faculty of Engineering, Department of Earth Science & Engineering

Chair in Flow in Porous Media
 
 
 
//

Contact

 

+44 (0)20 7594 6500m.blunt Website

 
 
//

Location

 

2.38ARoyal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Raeini:2017:10.1103/PhysRevE.96.013312,
author = {Raeini, AQ and Bijeljic, B and Blunt, MJ},
doi = {10.1103/PhysRevE.96.013312},
journal = {PHYSICAL REVIEW E},
title = {Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media},
url = {http://dx.doi.org/10.1103/PhysRevE.96.013312},
volume = {96},
year = {2017}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A generalized network extraction workflow is developed for parameterizing three-dimensional (3D) images of porous media. The aim of this workflow is to reduce the uncertainties in conventional network modeling predictions introduced due to the oversimplification of complex pore geometries encountered in natural porous media. The generalized network serves as a coarse discretization of the surface generated from a medial-axis transformation of the 3D image. This discretization divides the void space into individual pores and then subdivides each pore into sub-elements called half-throat connections. Each half-throat connection is further segmented into corners by analyzing the medial axis curves of its axial plane. The parameters approximating each corner—corner angle, volume, and conductivity—are extracted at different discretization levels, corresponding to different wetting layer thickness and local capillary pressures during multiphase flow simulations. Conductivities are calculated using direct single-phase flow simulation so that the network can reproduce the single-phase flow permeability of the underlying image exactly. We first validate the algorithm by using it to discretize synthetic angular pore geometries and show that the network model reproduces the corner angles accurately. We then extract network models from micro-CT images of porous rocks and show that the network extraction preserves macroscopic properties, the permeability and formation factor, and the statistics of the micro-CT images.
AU  - Raeini,AQ
AU  - Bijeljic,B
AU  - Blunt,MJ
DO  - 10.1103/PhysRevE.96.013312
PY  - 2017///
SN  - 2470-0045
TI  - Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media
T2  - PHYSICAL REVIEW E
UR  - http://dx.doi.org/10.1103/PhysRevE.96.013312
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000405926200007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/51801
VL  - 96
ER  -
Download