Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine
Joint Authors
Gholami, R.
Shahraki, A. R.
Jamali Paghaleh, M.
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-01-10
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gholami, R.& Shahraki, A. R.& Jamali Paghaleh, M.. 2012. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1001812
Modern Language Association (MLA)
Gholami, R.…[et al.]. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine. Mathematical Problems in Engineering No. 2012 (2012), pp.1-18.
https://search.emarefa.net/detail/BIM-1001812
American Medical Association (AMA)
Gholami, R.& Shahraki, A. R.& Jamali Paghaleh, M.. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1001812
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001812