DocumentCode
2459041
Title
Research on Drill String Failure in Gas Drilling Based on Statistical Learning Theory
Author
Bin, Yang
Author_Institution
Sch. of Oil & Gas Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China
fYear
2010
fDate
17-19 Dec. 2010
Firstpage
396
Lastpage
398
Abstract
The failure of drill string in gas drilling has become a technical problem for drilling workers. In this paper, based on the analysis of drill string failure data at home and abroad using Statistical Learning Theroy and Support Vector Machine which have a very rapid development in recent years, a new predictive model of drill string failure has been established in gas drilling. Experimental results show that the model has very high accuracy for the prediction of drill string failure in gas drilling.
Keywords
drilling (geotechnical); failure analysis; learning (artificial intelligence); support vector machines; drill string failure; gas drilling; statistical learning theory; support vector machine; Data models; Drilling; Kernel; Predictive models; Statistical learning; Support vector machine classification; dring string failure; gas drilling; research; statistical learning theory;
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.2010.103
Filename
5709106
Link To Document