Show simple item record

dc.contributor.authorGholami, Raoof
dc.contributor.authorShahraki, A.
dc.contributor.authorJamali Paghaleh, M.
dc.identifier.citationGholami, R. and Shahraki, A. and Jamali Paghaleh, M. 2012. Prediction of hydrocarbon reservoirs permeability using support vector machine. Mathematical Problems in Engineering. 2012.

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.

dc.titlePrediction of hydrocarbon reservoirs permeability using support vector machine
dc.typeJournal Article
dcterms.source.titleMathematical Problems in Engineering
curtin.departmentCurtin Sarawak
curtin.accessStatusOpen access via publisher

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record