Prediction of hydrocarbon reservoirs permeability using support vector machine
Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are cor...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/24559 |
| _version_ | 1848751464989589504 |
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| author | Gholami, Raoof Shahraki, A. Jamali Paghaleh, M. |
| author_facet | Gholami, Raoof Shahraki, A. Jamali Paghaleh, M. |
| author_sort | Gholami, Raoof |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability. Copyright © 2012 R. Gholami et al. |
| first_indexed | 2025-11-14T07:53:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-24559 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:53:09Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-245592017-09-13T15:11:56Z Prediction of hydrocarbon reservoirs permeability using support vector machine Gholami, Raoof Shahraki, A. Jamali Paghaleh, M. Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability. Copyright © 2012 R. Gholami et al. 2012 Journal Article http://hdl.handle.net/20.500.11937/24559 10.1155/2012/670723 unknown |
| spellingShingle | Gholami, Raoof Shahraki, A. Jamali Paghaleh, M. Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title | Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title_full | Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title_fullStr | Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title_full_unstemmed | Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title_short | Prediction of hydrocarbon reservoirs permeability using support vector machine |
| title_sort | prediction of hydrocarbon reservoirs permeability using support vector machine |
| url | http://hdl.handle.net/20.500.11937/24559 |