Intelligent approaches for the synthesis of petrophysical logs
Log data are of prime importance in acquiring petrophysical data from hydrocarbon reservoirs. Reliable log analysis in a hydrocarbon reservoir requires a complete set of logs. For many reasons, such as incomplete logging in old wells, destruction of logs due to inappropriate data storage and measure...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institute of Physics Publishing
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/20726 |
| _version_ | 1848750388805632000 |
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| author | Rezaee, M. Reza Kadkhodaie-Ilkhchi, A. Alizadeh, P. |
| author_facet | Rezaee, M. Reza Kadkhodaie-Ilkhchi, A. Alizadeh, P. |
| author_sort | Rezaee, M. Reza |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Log data are of prime importance in acquiring petrophysical data from hydrocarbon reservoirs. Reliable log analysis in a hydrocarbon reservoir requires a complete set of logs. For many reasons, such as incomplete logging in old wells, destruction of logs due to inappropriate data storage and measurement errors due to problems with logging apparatus or hole conditions, log suites are either incomplete or unreliable. In this study, fuzzy logic and artificial neural networks were used as intelligent tools to synthesize petrophysical logs including neutron, density, sonic and deep resistivity. The petrophysical data from two wells were used for constructing intelligent models in the Fahlian limestone reservoir, Southern Iran. A third well from the field was used to evaluate the reliability of the models. The results showed that fuzzy logic and artificial neural networks were successful in synthesizing wireline logs. The combination of the results obtained from fuzzy logic and neural networks in a simpleaveraging committee machine (CM) showed a significant improvement in the accuracy of theestimations. This committee machine performed better than fuzzy logic or the neural network model in the problem of estimating petrophysical properties from well logs. |
| first_indexed | 2025-11-14T07:36:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-20726 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:36:03Z |
| publishDate | 2008 |
| publisher | Institute of Physics Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-207262017-09-13T16:00:10Z Intelligent approaches for the synthesis of petrophysical logs Rezaee, M. Reza Kadkhodaie-Ilkhchi, A. Alizadeh, P. fuzzy logic petrophysical logs artificial neural networks expert systems synthesizing committee - machine Log data are of prime importance in acquiring petrophysical data from hydrocarbon reservoirs. Reliable log analysis in a hydrocarbon reservoir requires a complete set of logs. For many reasons, such as incomplete logging in old wells, destruction of logs due to inappropriate data storage and measurement errors due to problems with logging apparatus or hole conditions, log suites are either incomplete or unreliable. In this study, fuzzy logic and artificial neural networks were used as intelligent tools to synthesize petrophysical logs including neutron, density, sonic and deep resistivity. The petrophysical data from two wells were used for constructing intelligent models in the Fahlian limestone reservoir, Southern Iran. A third well from the field was used to evaluate the reliability of the models. The results showed that fuzzy logic and artificial neural networks were successful in synthesizing wireline logs. The combination of the results obtained from fuzzy logic and neural networks in a simpleaveraging committee machine (CM) showed a significant improvement in the accuracy of theestimations. This committee machine performed better than fuzzy logic or the neural network model in the problem of estimating petrophysical properties from well logs. 2008 Journal Article http://hdl.handle.net/20.500.11937/20726 10.1088/1742-2132/5/1/002 Institute of Physics Publishing restricted |
| spellingShingle | fuzzy logic petrophysical logs artificial neural networks expert systems synthesizing committee - machine Rezaee, M. Reza Kadkhodaie-Ilkhchi, A. Alizadeh, P. Intelligent approaches for the synthesis of petrophysical logs |
| title | Intelligent approaches for the synthesis of petrophysical logs |
| title_full | Intelligent approaches for the synthesis of petrophysical logs |
| title_fullStr | Intelligent approaches for the synthesis of petrophysical logs |
| title_full_unstemmed | Intelligent approaches for the synthesis of petrophysical logs |
| title_short | Intelligent approaches for the synthesis of petrophysical logs |
| title_sort | intelligent approaches for the synthesis of petrophysical logs |
| topic | fuzzy logic petrophysical logs artificial neural networks expert systems synthesizing committee - machine |
| url | http://hdl.handle.net/20.500.11937/20726 |