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
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;
Conference_Titel :
Signal Processing (ICSP), 2010 IEEE 10th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5897-4
DOI :
10.1109/ICOSP.2010.5656929
