DocumentCode
3423316
Title
Application of support vector machine in prediction of reservoir parameters
Author
Duan-Nan, Ye ; Guang-Zhi, Zhang
Author_Institution
Coll. of Geo-resources & Inf., China Univ. of Pet. (East China), Qingdao, China
fYear
2010
fDate
24-28 Oct. 2010
Firstpage
2539
Lastpage
2542
Abstract
The conventional method is not performing well in reservoir parameters prediction because of lacking learning samples. The support vector machine method could help us in this situation. We repeat an experiment to verify the excellent generalization ability of SVM. Four applications of real data processing were done by us, and they were all working very well. The result shows that this method would bring us to a nice place.
Keywords
geophysics computing; learning (artificial intelligence); reservoirs; seismology; support vector machines; data processing; learning samples; reservoir parameter prediction; support vector machine method; Kernel; Petroleum; Prediction algorithms; Reservoirs; Risk management; Support vector machines; Training; porosity prediction; regression method; reservoir parameter prediction; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing (ICSP), 2010 IEEE 10th International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-5897-4
Type
conf
DOI
10.1109/ICOSP.2010.5656929
Filename
5656929
Link To Document