A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images
Abstract Unresolved digital rock images are often used to avoid high computational costs and limited field of views associated with processing fine‐resolution rock images. However, segmentation of unresolved images using classical methods is suboptimal due to the presence of the partial‐volume effec...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036855 |
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| _version_ | 1850115845672402944 |
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| author | Rui Li Yi Yang Yuxuan Zhang Wenbo Zhan Jianhui Yang Yingfang Zhou |
| author_facet | Rui Li Yi Yang Yuxuan Zhang Wenbo Zhan Jianhui Yang Yingfang Zhou |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Abstract Unresolved digital rock images are often used to avoid high computational costs and limited field of views associated with processing fine‐resolution rock images. However, segmentation of unresolved images using classical methods is suboptimal due to the presence of the partial‐volume effect. Suboptimal segmentations can significantly influence the geometry and effective properties of the reconstructed models. This study reveals that partial‐volume pixels with high pore fractions remain as regional minima in intensity levels in unresolved images. By identifying these regional‐minima pixels, we can effectively extract pore space obscured by the partial‐volume effect. Based on this observation, we propose a novel segmentation method capable of identifying these regional‐minima partial‐volume pixels and converting them to pure pore pixels, thereby binarizing the digital rock images. The method is validated on sandstone and carbonate rock samples. Our method demonstrates a notable improvement in modeled permeability accuracy, surpassing 50% compared to the thresholding method and over 30% compared to the watershed method. Moreover, models segmented by this approach exhibit smaller pore and throat sizes compared to the substantially overestimated results obtained by classical methods. These findings suggest that the regional‐minima segmentation method effectively corrects for the partial‐volume effect and preserves more detailed pore structures. Consequently, it enhances the quality of binarized rock geometries, leading to improved accuracy in fluid‐flow simulations. |
| format | Article |
| id | doaj-art-7adcaa1b97454bb5a198ac151a414bac |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-7adcaa1b97454bb5a198ac151a414bac2025-08-20T02:36:28ZengWileyWater Resources Research0043-13971944-79732024-06-01606n/an/a10.1029/2023WR036855A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock ImagesRui Li0Yi Yang1Yuxuan Zhang2Wenbo Zhan3Jianhui Yang4Yingfang Zhou5University of Aberdeen Aberdeen UKUniversity of Aberdeen Aberdeen UKUniversity of Aberdeen Aberdeen UKUniversity of Aberdeen Aberdeen UKGeoscience Research Centre Total E&P UK Limited Aberdeen UKUniversity of Aberdeen Aberdeen UKAbstract Unresolved digital rock images are often used to avoid high computational costs and limited field of views associated with processing fine‐resolution rock images. However, segmentation of unresolved images using classical methods is suboptimal due to the presence of the partial‐volume effect. Suboptimal segmentations can significantly influence the geometry and effective properties of the reconstructed models. This study reveals that partial‐volume pixels with high pore fractions remain as regional minima in intensity levels in unresolved images. By identifying these regional‐minima pixels, we can effectively extract pore space obscured by the partial‐volume effect. Based on this observation, we propose a novel segmentation method capable of identifying these regional‐minima partial‐volume pixels and converting them to pure pore pixels, thereby binarizing the digital rock images. The method is validated on sandstone and carbonate rock samples. Our method demonstrates a notable improvement in modeled permeability accuracy, surpassing 50% compared to the thresholding method and over 30% compared to the watershed method. Moreover, models segmented by this approach exhibit smaller pore and throat sizes compared to the substantially overestimated results obtained by classical methods. These findings suggest that the regional‐minima segmentation method effectively corrects for the partial‐volume effect and preserves more detailed pore structures. Consequently, it enhances the quality of binarized rock geometries, leading to improved accuracy in fluid‐flow simulations.https://doi.org/10.1029/2023WR036855regional‐minimaimage segmentationunresoloved rock imagesfluid flow |
| spellingShingle | Rui Li Yi Yang Yuxuan Zhang Wenbo Zhan Jianhui Yang Yingfang Zhou A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images Water Resources Research regional‐minima image segmentation unresoloved rock images fluid flow |
| title | A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images |
| title_full | A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images |
| title_fullStr | A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images |
| title_full_unstemmed | A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images |
| title_short | A Novel Regional‐Minima Image Segmentation Method for Fluid Transport Simulations in Unresolved Rock Images |
| title_sort | novel regional minima image segmentation method for fluid transport simulations in unresolved rock images |
| topic | regional‐minima image segmentation unresoloved rock images fluid flow |
| url | https://doi.org/10.1029/2023WR036855 |
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