Petrophysical data prediction from seismic attributes using committee fuzzy inference system
This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted fro...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Elsevier
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/45044 |
| _version_ | 1848757172725350400 |
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| author | Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Rahimpour-Bonab, H. Chehrazi, A. |
| author_facet | Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Rahimpour-Bonab, H. Chehrazi, A. |
| author_sort | Kadkhodaie Ilkhchi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method. |
| first_indexed | 2025-11-14T09:23:52Z |
| format | Journal Article |
| id | curtin-20.500.11937-45044 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:23:52Z |
| publishDate | 2009 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-450442018-12-13T09:31:46Z Petrophysical data prediction from seismic attributes using committee fuzzy inference system Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Rahimpour-Bonab, H. Chehrazi, A. seismic attributes probabilistic neural network Mamdani petrophysical data hybrid genetic - algorithm-pattern search Larsen Sugeno Committee fuzzy inference system This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method. 2009 Journal Article http://hdl.handle.net/20.500.11937/45044 10.1016/j.cageo.2009.04.010 Elsevier fulltext |
| spellingShingle | seismic attributes probabilistic neural network Mamdani petrophysical data hybrid genetic - algorithm-pattern search Larsen Sugeno Committee fuzzy inference system Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Rahimpour-Bonab, H. Chehrazi, A. Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title | Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title_full | Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title_fullStr | Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title_full_unstemmed | Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title_short | Petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| title_sort | petrophysical data prediction from seismic attributes using committee fuzzy inference system |
| topic | seismic attributes probabilistic neural network Mamdani petrophysical data hybrid genetic - algorithm-pattern search Larsen Sugeno Committee fuzzy inference system |
| url | http://hdl.handle.net/20.500.11937/45044 |