Journal article 359 views 48 downloads
Automation of the meshing process of geological data
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Jason Jones ,
Xiaolong Liu,
Nevan C Himmelberg,
Dean Thornton
Computational Geosciences, Volume: 28, Pages: 661 - 679
Swansea University Authors:
Sui Bun Lo, Oubay Hassan , Jason Jones
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DOI (Published version): 10.1007/s10596-024-10290-1
Abstract
This work proposes a novel meshing technique that is able to extract surfaces from processed seismic data and integrate surfaces that were constructed using other extraction techniques. Contrary to other existing methods, the process is fully automated and does not require any user intervention. The...
| Published in: | Computational Geosciences |
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| ISSN: | 1420-0597 1573-1499 |
| Published: |
Springer Nature
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66554 |
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| Abstract: |
This work proposes a novel meshing technique that is able to extract surfaces from processed seismic data and integrate surfaces that were constructed using other extraction techniques. Contrary to other existing methods, the process is fully automated and does not require any user intervention. The proposed system includes an approach for closing the gaps that arise from the different techniques used for surface extraction. The developed process is able to handle non-manifold domains that result from multiple surface intersections. Surface and volume meshing that comply with user specified mesh control techniques are implemented to ensure the desired mesh quality. The integrated procedures provide a unique facility to handle geotechnical models and accelerate the generation of quality meshes for geophysics modelling. The developed procedure enables the creation of meshes for complex reservoir models to be reduced from weeks to a few hours. Various industrial examples are shown to demonstrate the practicable use of the developed approach to handle real life data. |
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| Keywords: |
Reservoir modelling; Surface mesh optimization; Volume mesh generation; Surface intersection; Surface extension |
| College: |
Faculty of Science and Engineering |
| Funders: |
This study was funded by Chevron Corporation. |
| Start Page: |
661 |
| End Page: |
679 |