SVM Model Based on Signal Transformation and its Applications in Oil Water-Flooded Identification

Author

Shang, Fuhua ; Wang, Lei

Author_Institution

Comput. Sci. & Technol. Dept., Daqing Pet. Inst., Daqing, China

Volume

5

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

220

Lastpage

224

Abstract

This paper, a method of signal transformation for feature extraction is proposed. It can transform log-signal space into the vector space, which the experiment system requires, and then use SVM (Support Vector Machine) automatically to identify the water-flooded status of oil-saturated stratum. The results of experiment indicate that this algorithm has good identification ability and strong generalization ability in condition that the number of training swatch is limited.

Keywords

floods; oil technology; production engineering computing; support vector machines; SVM model; feature extraction; log-signal space; oil water-flooded identification; oil-saturated stratum; signal transformation; support vector machine; vector space; water-flooded status; Computer science; Feature extraction; Geology; Hydrocarbon reservoirs; Information analysis; Petroleum; Production; Signal processing; Space technology; Support vector machines; SVM; signal transformation; water-flooded identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.100

Filename

5360627