• DocumentCode
    569736
  • Title

    Application in Water Flooded Layer Recognition Based on Wavelet Packet Transform and Support Vector Machine

  • Author

    Liu Jin-Yue ; Zhu Bao-Ling

  • Author_Institution
    Comput. & Inf. Technol. Coll., Northeast Pet. Univ., Daqing, China
  • fYear
    2012
  • fDate
    17-19 Aug. 2012
  • Firstpage
    1206
  • Lastpage
    1209
  • Abstract
    In view of the complex time-varying signal pattern recognition, the method of water flooded layer recognition based on wavelet packet transform and support vector machine had been proposed in this paper. According to the multi-resolution characteristics of wavelet packet analysis, the denoising for signals and the extraction of useful signals had been presented. The sample space had been mapped into the high dimensional feature space by employing the support vector machine. At the same time, the classification of signals had been also achieved by constructing the optimal separating hyperplane. The experiment showed that this method had the better classification performance and robustness.
  • Keywords
    feature extraction; hydrocarbon reservoirs; petroleum industry; production engineering computing; signal classification; signal denoising; support vector machines; wavelet transforms; well logging; complex time-varying signal pattern recognition; high dimensional feature space; multiresolution characteristics; oilfield development; signal classification; signal denoising; signal extraction; support vector machine; water flooded layer recognition; wavelet packet analysis; wavelet packet transform; Floods; Kernel; Support vector machines; Wavelet analysis; Wavelet packets; support vector machine; water flooded layer recognition; wavelet packet transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4673-2406-9
  • Type

    conf

  • DOI
    10.1109/ICCIS.2012.79
  • Filename
    6301333