The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method...
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| Format: | Article |
| Language: | English |
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Petroleum University of Technology
2013-07-01
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| Series: | Iranian Journal of Oil & Gas Science and Technology |
| Subjects: | |
| Online Access: | http://ijogst.put.ac.ir/article_3642_4286517623335d98d338ff94a3483bdd.pdf |
| _version_ | 1819064386933751808 |
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| author | Mohsen Karimian Nader Fathianpour Jamshid Moghaddasi |
| author_facet | Mohsen Karimian Nader Fathianpour Jamshid Moghaddasi |
| author_sort | Mohsen Karimian |
| collection | DOAJ |
| description | Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions. |
| first_indexed | 2024-12-21T15:29:45Z |
| format | Article |
| id | doaj.art-d768218d67004772ac496948760caf7e |
| institution | Directory Open Access Journal |
| issn | 2345-2412 2345-2420 |
| language | English |
| last_indexed | 2024-12-21T15:29:45Z |
| publishDate | 2013-07-01 |
| publisher | Petroleum University of Technology |
| record_format | Article |
| series | Iranian Journal of Oil & Gas Science and Technology |
| spelling | doaj.art-d768218d67004772ac496948760caf7e2022-12-21T18:58:48ZengPetroleum University of TechnologyIranian Journal of Oil & Gas Science and Technology2345-24122345-24202013-07-0123253610.22050/ijogst.2013.36423642The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector RegressionMohsen Karimian0Nader Fathianpour1Jamshid Moghaddasi2Department of Petroleum Exploration Engineering, Petroleum University of Technology, Abadan, IranDepartment of Mining Engineering, Isfahan University of Technology, Isfahan, IranDepartment of Petroleum Engineering, Petroleum University of Technology, Ahwaz, IranPorosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions.http://ijogst.put.ac.ir/article_3642_4286517623335d98d338ff94a3483bdd.pdfPetrophysical ParameterReservoirsPorosityWell Log DataSupport Vector Machine |
| spellingShingle | Mohsen Karimian Nader Fathianpour Jamshid Moghaddasi The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression Iranian Journal of Oil & Gas Science and Technology Petrophysical Parameter Reservoirs Porosity Well Log Data Support Vector Machine |
| title | The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression |
| title_full | The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression |
| title_fullStr | The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression |
| title_full_unstemmed | The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression |
| title_short | The Porosity Prediction of One of Iran South Oil Field Carbonate Reservoirs Using Support Vector Regression |
| title_sort | porosity prediction of one of iran south oil field carbonate reservoirs using support vector regression |
| topic | Petrophysical Parameter Reservoirs Porosity Well Log Data Support Vector Machine |
| url | http://ijogst.put.ac.ir/article_3642_4286517623335d98d338ff94a3483bdd.pdf |
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