Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data
We apply an approach based on smooth neural networks to a 3D seismic survey in the Shedgum area of the Ghawar Field to estimate the reservoir porosity distribution of the Arab-D Member. We conducted numerous systematic cross-validation tests to assess the accuracy of the method and to compare it...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Technical Report |
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Massachusetts Institute of Technology. Earth Resources Laboratory
2012
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| Online Access: | http://hdl.handle.net/1721.1/75458 |
| _version_ | 1826206980122869760 |
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| author | Saggaf, Muhammad M. Toksoz, M. Nafi Mustafa, Husam M. |
| author2 | Massachusetts Institute of Technology. Earth Resources Laboratory |
| author_facet | Massachusetts Institute of Technology. Earth Resources Laboratory Saggaf, Muhammad M. Toksoz, M. Nafi Mustafa, Husam M. |
| author_sort | Saggaf, Muhammad M. |
| collection | MIT |
| description | We apply an approach based on smooth neural networks to a 3D seismic survey in the
Shedgum area of the Ghawar Field to estimate the reservoir porosity distribution of the
Arab-D Member. We conducted numerous systematic cross-validation tests to assess
the accuracy of the method and to compare it to that of traditional back-propagation
networks. The results obtained from these tests indicate that the regularized back-propagation network can be quite adept at estimating the porosity distribution of the reservoir in the inter-well regions from seismic data. The accuracy remained consistent as the network parameters (size and training length) were varied. On the other hand, the traditional back-propagation network gave acceptable results only when the optimal network parameters were used, and the accuracy deteriorated significantly as soon as deviations from these optimal parameters occurred. Moreover, utilizing smooth networks, the final porosity volume corroborates our existing understanding of the reservoir and shows substantial similarity to the simple geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model. We also scrutinize multi-attribute analysis, analyze how attributes can be both constructive and damaging to the prediction of the reservoir properties, and evaluate their effectiveness in enhancing the accuracy of the solution. |
| first_indexed | 2024-09-23T13:41:55Z |
| format | Technical Report |
| id | mit-1721.1/75458 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:41:55Z |
| publishDate | 2012 |
| publisher | Massachusetts Institute of Technology. Earth Resources Laboratory |
| record_format | dspace |
| spelling | mit-1721.1/754582019-04-12T20:32:24Z Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data Saggaf, Muhammad M. Toksoz, M. Nafi Mustafa, Husam M. Massachusetts Institute of Technology. Earth Resources Laboratory Saggaf, Muhammad M. Toksoz, M. Nafi We apply an approach based on smooth neural networks to a 3D seismic survey in the Shedgum area of the Ghawar Field to estimate the reservoir porosity distribution of the Arab-D Member. We conducted numerous systematic cross-validation tests to assess the accuracy of the method and to compare it to that of traditional back-propagation networks. The results obtained from these tests indicate that the regularized back-propagation network can be quite adept at estimating the porosity distribution of the reservoir in the inter-well regions from seismic data. The accuracy remained consistent as the network parameters (size and training length) were varied. On the other hand, the traditional back-propagation network gave acceptable results only when the optimal network parameters were used, and the accuracy deteriorated significantly as soon as deviations from these optimal parameters occurred. Moreover, utilizing smooth networks, the final porosity volume corroborates our existing understanding of the reservoir and shows substantial similarity to the simple geologic model constructed by interpolating the well information, while adding significant detail and enhanced resolution to that model. We also scrutinize multi-attribute analysis, analyze how attributes can be both constructive and damaging to the prediction of the reservoir properties, and evaluate their effectiveness in enhancing the accuracy of the solution. Saudi Aramco Massachusetts Institute of Technology. Borehole Acoustics and Logging Consortium Massachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation Consortium 2012-12-13T17:27:25Z 2012-12-13T17:27:25Z 2000 Technical Report http://hdl.handle.net/1721.1/75458 Earth Resources Laboratory Industry Consortia Annual Report;2000-03 application/pdf Massachusetts Institute of Technology. Earth Resources Laboratory |
| spellingShingle | Saggaf, Muhammad M. Toksoz, M. Nafi Mustafa, Husam M. Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title | Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title_full | Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title_fullStr | Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title_full_unstemmed | Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title_short | Application Of Smooth Neural Networks For Inter-Well Estimation Of Porosity From Seismic Data |
| title_sort | application of smooth neural networks for inter well estimation of porosity from seismic data |
| url | http://hdl.handle.net/1721.1/75458 |
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