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
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;
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
DOI :
10.1109/ICCIS.2010.128