Genetic algorithm-based pore network extraction from micro-computed tomography images
A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Pergamon
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/24341 |
| _version_ | 1848751401279160320 |
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| author | Ebrahimi, A. Jamshidi, S. Iglauer, Stefan Bozorgmehry, R. |
| author_facet | Ebrahimi, A. Jamshidi, S. Iglauer, Stefan Bozorgmehry, R. |
| author_sort | Ebrahimi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two approaches to generate the pore network structure are presented. Unlike previous algorithms, the presented approaches are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first approach, only the difference between the real micro-CT images of the porous medium and the sliced images from the generated network is selected as the objective function which is minimized via a genetic algorithm (GA). In order to further improve the structure and behavior of the generated network, making it more representative of the real porous medium, a second optimization has been used in which the contrast between the experimental and the predicted values of the network permeability is minimized via GA. We present two case studies for two different complex geological porous media, Clashach sandstone and Indiana limestone. We compare porosity and permeability predicted by the GA generated networks with experimental values and find an excellent match. |
| first_indexed | 2025-11-14T07:52:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-24341 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:52:08Z |
| publishDate | 2013 |
| publisher | Pergamon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-243412019-02-19T04:27:24Z Genetic algorithm-based pore network extraction from micro-computed tomography images Ebrahimi, A. Jamshidi, S. Iglauer, Stefan Bozorgmehry, R. porous media pore network modelling extraction A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two approaches to generate the pore network structure are presented. Unlike previous algorithms, the presented approaches are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the network. In the first approach, only the difference between the real micro-CT images of the porous medium and the sliced images from the generated network is selected as the objective function which is minimized via a genetic algorithm (GA). In order to further improve the structure and behavior of the generated network, making it more representative of the real porous medium, a second optimization has been used in which the contrast between the experimental and the predicted values of the network permeability is minimized via GA. We present two case studies for two different complex geological porous media, Clashach sandstone and Indiana limestone. We compare porosity and permeability predicted by the GA generated networks with experimental values and find an excellent match. 2013 Journal Article http://hdl.handle.net/20.500.11937/24341 10.1016/j.ces.2013.01.045 Pergamon fulltext |
| spellingShingle | porous media pore network modelling extraction Ebrahimi, A. Jamshidi, S. Iglauer, Stefan Bozorgmehry, R. Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title | Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title_full | Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title_fullStr | Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title_full_unstemmed | Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title_short | Genetic algorithm-based pore network extraction from micro-computed tomography images |
| title_sort | genetic algorithm-based pore network extraction from micro-computed tomography images |
| topic | porous media pore network modelling extraction |
| url | http://hdl.handle.net/20.500.11937/24341 |