Author/Authors :
Maleki، Sh نويسنده Faculty of mining engineering, Shahrood University of Technology, Iran Maleki, Sh , Moradzadeh، A نويسنده Faculty of mining engineering, Shahrood University of Technology, Iran Moradzadeh, A , Ghavami، R نويسنده Faculty of mining engineering, Shahrood University of Technology, Iran Ghavami, R , Sadeghzadeh، F نويسنده Iranian Oil and Gas Company, Iran Sadeghzadeh, F
Abstract :
DT log is one of the most frequently used wireline logs to determine compression wave velocity. This log is commonly used to
gain insight into the elastic and petrophysical parameters of reservoir rocks. Acquisition of DT log is, however, a very expensive and
time consuming task. Thus prediction of this log by any means can be a great help by decreasing the amount of money that needs to
be allocated for acquisition. Support vector machine (SVM) is one of the best artificial intelligence techniques proven to be a reliable
method in the prediction of various real world problems. The aim of this paper is to use SVM to predict the DT log data of a well
located in the southern oilfields of Iran. By comparing the results of SVM with those obtained by a Back Propagation Neural
Network (BPNN) we were able to verify the accuracy of SVM in the prediction of P-wave velocity. Hence, this method is
recommended as a cost effective tool in the prediction of P- wave velocity.
