Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seism...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7754 |
| _version_ | 1797442807134158848 |
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| author | Mohammed Fathy Gouda Abdul Halim Abdul Latiff Seyed Yasser Moussavi Alashloo |
| author_facet | Mohammed Fathy Gouda Abdul Halim Abdul Latiff Seyed Yasser Moussavi Alashloo |
| author_sort | Mohammed Fathy Gouda |
| collection | DOAJ |
| description | Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to Thomsen’s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling. |
| first_indexed | 2024-03-09T12:47:24Z |
| format | Article |
| id | doaj.art-e3190da27f46413f9a190fd65665ed8d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:47:24Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e3190da27f46413f9a190fd65665ed8d2023-11-30T22:11:06ZengMDPI AGApplied Sciences2076-34172022-08-011215775410.3390/app12157754Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear ImpedancesMohammed Fathy Gouda0Abdul Halim Abdul Latiff1Seyed Yasser Moussavi Alashloo2Department of Geosciences, Universiti Teknologi Petronas, Seri Iskander 32610, Perak, MalaysiaDepartment of Geosciences, Universiti Teknologi Petronas, Seri Iskander 32610, Perak, MalaysiaThe Institute of Digital Signal Processing, University of Duisburg-Essen, Forsthausweg 2, 47057 Duisburg, GermanySeismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to Thomsen’s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling.https://www.mdpi.com/2076-3417/12/15/7754anisotropyrock physicsinversionparameter estimation |
| spellingShingle | Mohammed Fathy Gouda Abdul Halim Abdul Latiff Seyed Yasser Moussavi Alashloo Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances Applied Sciences anisotropy rock physics inversion parameter estimation |
| title | Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances |
| title_full | Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances |
| title_fullStr | Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances |
| title_full_unstemmed | Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances |
| title_short | Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances |
| title_sort | estimation of litho fluid facies distribution from zero offset acoustic and shear impedances |
| topic | anisotropy rock physics inversion parameter estimation |
| url | https://www.mdpi.com/2076-3417/12/15/7754 |
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