Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing
Characterizing heterogeneity is crucial to assess the variability of rock properties in carbonate reservoir samples. This work introduces an original multiscale approach to simulate permeability and porosity in heterogeneous carbonate samples using 3D X-ray computed tomography images. The main novel...
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| Format: | Article |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9462922/ |
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| author | Mohamed Soufiane Jouini Jorge Salgado Gomes Moussa Tembely Ezdeen Raed Ibrahim |
| author_facet | Mohamed Soufiane Jouini Jorge Salgado Gomes Moussa Tembely Ezdeen Raed Ibrahim |
| author_sort | Mohamed Soufiane Jouini |
| collection | DOAJ |
| description | Characterizing heterogeneity is crucial to assess the variability of rock properties in carbonate reservoir samples. This work introduces an original multiscale approach to simulate permeability and porosity in heterogeneous carbonate samples using 3D X-ray computed tomography images. The main novelty of our approach is to introduce a quantitative heterogeneity description in terms of texture classification using machine learning. The rock texture classification result is then used to upscale rock properties simulations from fine to coarse scale. The fine scale properties are investigated based lattice Boltzmann method, while a Darcy-scale flow simulator is adopted for estimating coarse scale properties. In addition, due to the critical role played by petrophysical properties at fine scale, a 3D printing technique is employed to validate experimentally the numerical simulations at this scale. Finally, we present an application of our proposed approach on a real carbonate sample from the Middle East carbonate oilfield reservoir. |
| format | Article |
| id | doaj-art-2dc3e2332ce9440b9a9fed6158b76cc0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2dc3e2332ce9440b9a9fed6158b76cc02025-08-20T03:12:49ZengIEEEIEEE Access2169-35362021-01-019906319064110.1109/ACCESS.2021.30917729462922Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D PrintingMohamed Soufiane Jouini0https://orcid.org/0000-0001-9741-0636Jorge Salgado Gomes1Moussa Tembely2Ezdeen Raed Ibrahim3https://orcid.org/0000-0002-8280-4066Department of Mathematics, Khalifa University, Abu Dhabi, United Arab EmiratesSubsurface Excellence Division, Abu Dhabi National Oil Company, Abu Dhabi, United Arab EmiratesDepartment of Petroleum Engineering, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Petroleum Geosciences, Khalifa University, Abu Dhabi, United Arab EmiratesCharacterizing heterogeneity is crucial to assess the variability of rock properties in carbonate reservoir samples. This work introduces an original multiscale approach to simulate permeability and porosity in heterogeneous carbonate samples using 3D X-ray computed tomography images. The main novelty of our approach is to introduce a quantitative heterogeneity description in terms of texture classification using machine learning. The rock texture classification result is then used to upscale rock properties simulations from fine to coarse scale. The fine scale properties are investigated based lattice Boltzmann method, while a Darcy-scale flow simulator is adopted for estimating coarse scale properties. In addition, due to the critical role played by petrophysical properties at fine scale, a 3D printing technique is employed to validate experimentally the numerical simulations at this scale. Finally, we present an application of our proposed approach on a real carbonate sample from the Middle East carbonate oilfield reservoir.https://ieeexplore.ieee.org/document/9462922/Machine learningmicro-computed tomographypermeabilityupscaling3D printing |
| spellingShingle | Mohamed Soufiane Jouini Jorge Salgado Gomes Moussa Tembely Ezdeen Raed Ibrahim Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing IEEE Access Machine learning micro-computed tomography permeability upscaling 3D printing |
| title | Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing |
| title_full | Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing |
| title_fullStr | Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing |
| title_full_unstemmed | Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing |
| title_short | Upscaling Strategy to Simulate Permeability in a Carbonate Sample Using Machine Learning and 3D Printing |
| title_sort | upscaling strategy to simulate permeability in a carbonate sample using machine learning and 3d printing |
| topic | Machine learning micro-computed tomography permeability upscaling 3D printing |
| url | https://ieeexplore.ieee.org/document/9462922/ |
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