A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field
Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Institute of Physics Publishing IOP
2006
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/33386 |
| _version_ | 1848753931948130304 |
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| author | Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Moallemi, A. |
| author_facet | Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Moallemi, A. |
| author_sort | Kadkhodaie Ilkhchi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log data. In this study, the importance of the fuzzy logic approach for prediction of rock type from well log responses was shown by using an example of the Vp to Vs ratio for lithology determination from crisp and fuzzy logic approaches. A fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data. Then, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data. Permeability was modelled and predicted using a Takagi-Sugeno fuzzy inference system. Then a back propagation neural network was applied to verify fuzzy results for permeability modelling. For this purpose, three wells of the Iran offshore gas field were chosen for the construction of intelligent models of the reservoir, and a forth well was used as a test well to evaluate the reliability of the models. The results of this study show that fuzzy logic approach was successful for the prediction of permeability and rock types in the Iran offshore gas field. |
| first_indexed | 2025-11-14T08:32:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-33386 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:32:22Z |
| publishDate | 2006 |
| publisher | Institute of Physics Publishing IOP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-333862017-09-13T16:08:58Z A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Moallemi, A. fuzzy logic Kangan reservoir Iran offshore gas field fuzzy c-means clustering back propagation neural network rock types permeability Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log data. In this study, the importance of the fuzzy logic approach for prediction of rock type from well log responses was shown by using an example of the Vp to Vs ratio for lithology determination from crisp and fuzzy logic approaches. A fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data. Then, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data. Permeability was modelled and predicted using a Takagi-Sugeno fuzzy inference system. Then a back propagation neural network was applied to verify fuzzy results for permeability modelling. For this purpose, three wells of the Iran offshore gas field were chosen for the construction of intelligent models of the reservoir, and a forth well was used as a test well to evaluate the reliability of the models. The results of this study show that fuzzy logic approach was successful for the prediction of permeability and rock types in the Iran offshore gas field. 2006 Journal Article http://hdl.handle.net/20.500.11937/33386 10.1088/1742-2132/3/4/007 Institute of Physics Publishing IOP restricted |
| spellingShingle | fuzzy logic Kangan reservoir Iran offshore gas field fuzzy c-means clustering back propagation neural network rock types permeability Kadkhodaie Ilkhchi, A. Rezaee, M. Reza Moallemi, A. A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title | A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title_full | A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title_fullStr | A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title_full_unstemmed | A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title_short | A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field |
| title_sort | fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the kangan reservoir in the iran offshore gas field |
| topic | fuzzy logic Kangan reservoir Iran offshore gas field fuzzy c-means clustering back propagation neural network rock types permeability |
| url | http://hdl.handle.net/20.500.11937/33386 |