Prediction of hydrocarbon reservoirs permeability using support vector machine
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Identification of sedimentary-diagenetic facies and reservoir porosity and permeability prediction: An example from the Eocene beach-bar sandstone in the Dongying Depression, ChinaWang, J.; Cao, Y.; Liu, K.; Liu, Jie; Kashif, M. (2017)© 2017 Elsevier LtdAccurate prediction of reservoir porosity and permeability is essential for prospecting hydrocarbon reserves and petroleum production capacity. We propose an innovative reservoir porosity and permeability ...
Permeability Prediction from Mercury Injection Capillary Pressure: An Example from the Perth Basin, Western AustraliaAl Hinai, A.; Rezaee, M. Reza; Saeedi, A.; Lenormand, R. (2013)For shale gas reservoirs, permeability is one of the most important—and difficult—parameters to determine. Typical shale matrix permeabilities are in the range of 10 microdarcy–100 nanodarcy, and are heavily dependent on ...
Chehrazi, A.; Rezaee, M. Reza (2012)In this study, using a relatively large and complete data set of a complex carbonate reservoir, it is proven that among the numerous methods proposed for the prediction of permeability, the porosity-facies based models ...