A Robust Methodology for Prediction of DT Wireline Log
محورهای موضوعی : Mineralogy
Sh. Maleki
1
(Faculty of mining engineering, Shahrood University of Technology, Iran)
A. Moradzadeh
2
(Faculty of mining engineering, Shahrood University of Technology, Iran)
R. Ghavami
3
(Faculty of mining engineering, Shahrood University of Technology, Iran)
F. Sadeghzadeh
4
(Iranian Oil and Gas Company, Iran)
کلید واژه: Prediction, Support vector machine, DT Wireline Log, Back Propagation Neural Network, Southern Oil Field,
چکیده مقاله :
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
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
