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Mathematical Problems in Engineering
Volume 2012, Article ID 670723, 18 pages
Research Article

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

1Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, P.O. Box 316, Shahrood, Iran
2Department of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran
3Young Researchers Club, Islamic Azad University, Zahedan Branch, Zahedan 98168, Iran

Received 16 July 2011; Revised 8 October 2011; Accepted 1 November 2011

Academic Editor: P. Liatsis

Copyright © 2012 R. Gholami et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.