By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xin-hu Li ; Sch. of Geol. & Environ., Xi'an Univ. of Sci. & Technol., Xi'an, China ; Jie Luo ; Dong Liu

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.

Published in:

Computational and Information Sciences (ICCIS), 2010 International Conference on

Date of Conference:

17-19 Dec. 2010