• DocumentCode
    2459630
  • Title

    Lithology Identification Methods Contrast Based on Support Vector Machines at Different Well Logging Parameters Set

  • Author

    Li Xin-hu ; Luo Jie ; Liu Dong

  • Author_Institution
    Sch. of Geol. & Environ., Xi´an Univ. of Sci. & Technol., Xi´an, China
  • fYear
    2010
  • fDate
    17-19 Dec. 2010
  • Firstpage
    495
  • Lastpage
    498
  • Abstract
    Based on the coring well and well logging data, according to three methods, including M-N value, curve superposition and curve characteristic value, which are often be used on lithology identification, three different well logging curve parameters set was collected, joining with SVM, lithology identification was fulfilled, after that, to selecting the best well logging parameters set that suitable to used on lithology identification according to error minimum principle through contrasting the results. Results show that two of three different parameters set indicated the error minimum characteristic on the process, those are curve superposition value and curve characteristic value, the parameters sets of curve superposition value and curve characteristic value methods can be the preferable fundamental data to be used on lithology identification from well logging.
  • Keywords
    civil engineering computing; geotechnical engineering; radial basis function networks; support vector machines; well logging; M-N value; curve characteristic value; curve superposition value; error minimum principle; lithology identification methods contrast; radial basis function; support vector machines; well logging parameters set; Acoustics; Geology; Neutrons; Reservoirs; Support vector machines; Training; Well logging; Activity; Lithology identification; SVM; Well logging; radial-basis function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2010 International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-8814-8
  • Electronic_ISBN
    978-0-7695-4270-6
  • Type

    conf

  • DOI
    10.1109/ICCIS.2010.128
  • Filename
    5709132