Failure analysis of drill stem based on support vector machine and cluster analysis theory

doi:10.7623/syxb200703029

Editorial office of ACTA PETROLEI SINICA ›› 2007, Vol. 28 ›› Issue (3): 135-140.DOI: 10.7623/syxb200703029

• Petroleum Engineering • Previous Articles Next Articles

Failure analysis of drill stem based on support vector machine and cluster analysis theory

Yan Tie, Bi Xueliang, Wang Changjiang

Key Laboratory for Enhanced Oil Recovery of the Ministry of Education, Daqing Petroleum Institute, Daqing 163318, China

Received:2006-02-05 Revised:2006-10-25 Online:2007-05-25 Published:2010-05-21

基于支持向量机和聚类分析理论的钻具失效分析方法

闫铁, 毕雪亮, 王长江

大庆石油学院提高油气采收率教育部重点实验室, 黑龙江大庆, 163318

作者简介:闫铁,男,1957年2月生,2001年获哈尔滨工业大学工程力学博士学位,现为大庆石油学院油气井工程专业博士生导师,主要从事油气井工程力学等方向的研究.E-mail:yant@dqpi.edu.cn
基金资助:
黑龙江省自然科学基金项目(E200507)资助.

Abstract

Abstract: Considering the complex influence factors for the failure of drill stem,an analytic model for failure of drill stem based on support vector machine and cluster analysis theory was established.This model can be used to collect the data of trouble drill stem samples.Then,the data of samples were optimized,and the best inner structure of the data was obtained.Furthermore,the real failure reasons of drill stem could be obtained with the model.The failure data of drill stem collected from a district of Daqing Oilfield were pretreated and analyzed,according to the requirements of the model.Good results have been obtained successfully.Six measures for preventing failure of drill stem were presented.The new model is more accurate and reliable to calculate and analyze the failure of drill stem in drilling engineering.

Key words: drill stem, failure analysis, support vector machine, cluster analysis, analytic model, field application

摘要： 针对钻具失效影响因素比较复杂的问题,建立了基于支持向量机和聚类分析理论的钻具失效原因分析模型.应用该模型可收集钻具失效样本数据,并对数据进行机器学习及优化,得到最佳的内部结构,从而可以计算和分析出钻具失效的原因.对大庆油田某区块的钻具失效数据进行了处理和分析,确定了该区块钻具发生失效的根本原因,并且有针对性地提出了6点预防钻具失效措施.通过计算证明,这种新模型的计算和分析结果真实、可靠.

关键词: 钻具, 失效分析, 支持向量机, 聚类分析, 分析模型, 现场应用

CLC Number:

TE242

Yan Tie, Bi Xueliang, Wang Changjiang. Failure analysis of drill stem based on support vector machine and cluster analysis theory[J]. Editorial office of ACTA PETROLEI SINICA, 2007, 28(3): 135-140.

闫铁, 毕雪亮, 王长江. 基于支持向量机和聚类分析理论的钻具失效分析方法[J]. 石油学报, 2007, 28(3): 135-140.

[1]	Li Boliang, Li Binfei, Ji Yanmin, Gai Pingyuan, Wang Jian, Li Zhaomin, Wang Chengjian. Mechanism and application of flue gas-assisted steam injection for heavy oil recovery [J]. Acta Petrolei Sinica, 2024, 45(3): 574-585.
[2]	Cao Xulong, Liu Yu, Cao Weidong. Matching relation between preformed particle gel and reservoir pore throat during heterogeneous phase combination flooding [J]. Acta Petrolei Sinica, 2022, 43(2): 262-269.
[3]	Liu He, Lu Qiuyu, Zhu Shijia, Jiang Wei, Wang Suling. Application of typical clustering algorithms in analysis of system efficiency of pumping wells in blocks [J]. Acta Petrolei Sinica, 2020, 41(12): 1657-1664.
[4]	Zhou Bin, Wang Yanjiang, Liu Weifeng, Liu Baodi. A working condition recognition method of sucker-rod pumping wells based on Hessian-regularized SVM and multi-view co-training algorithm [J]. Acta Petrolei Sinica, 2018, 39(12): 1429-1436.
[5]	Guan Baoshan, Liang Li, Cheng Fang, Liu Jing, Xue Xiaojia, Xu Yun. Application of recycling technology for fracturing flowback fluid [J]. Acta Petrolei Sinica, 2017, 38(1): 99-104.
[6]	Xing Haiyan, Dang Yongbin, Wang Ben, Ge Hua, Wang Zunce. Q uantitative MMM identification of damage levels based on KNN FSVM for remanufactured sucker rod [J]. Acta Petrolei Sinica, 2015, 36(11): 1427-1432,1456.
[7]	Qiu Zhengsong, Mao Hui, Xie Binqiang, Wang Zaiming, Xing Weiliang, Shen Zhonghou, Wang Shengwu, Ju Bo. Synthesis and field application of high temperature resistant viscosifying agent for drilling fluid [J]. Acta Petrolei Sinica, 2015, 36(1): 106-113.
[8]	ZHU Yixiang SHI Guangren. Identification of lithologic characteristics of volcanic rocks by support vector machine [J]. Editorial office of ACTA PETROLEI SINICA, 2013, 34(2): 312-322.
[9]	YU Deliang QI Weigui DENG Shengchuan ZHANG Yongming ZHANG Fengwu. Submergence forecasting of a submersible plunger pump based on the support vector machine [J]. Editorial office of ACTA PETROLEI SINICA, 2011, 32(3): 534-538.
[10]	ZANG Yanbin WANG Ruihe ZHANG Rui WANG Zizhen. A failure analysis of drill string based on fault tree [J]. Editorial office of ACTA PETROLEI SINICA, 2011, 32(1): 171-176.
[11]	YI Fang LI Zhuxin LU Hongqing MA Weiping. Defect recognition by metal magnetic memory detection of pipelines based on the fuzzy kernel function SVM [J]. Editorial office of ACTA PETROLEI SINICA, 2010, 31(5): 863-866.
[12]	WANG Mingda ZHANG Laibin LIANG Wei CHEN Zhigang. Pipeline leakage detection method based on independent component analysis and support vector machine [J]. Editorial office of ACTA PETROLEI SINICA, 2010, 31(4): 659-663.
[13]	LIU He;YE Peng;LIU Yan WANG Xin. Nitrogen foam injection technique and its application in reservoirs with high water cut [J]. Editorial office of ACTA PETROLEI SINICA, 2010, 31(1): 91-95.
[14]	LI Hongqi, GUO Haifeng, GUO Haimin, MENG Zhaoxu, TAN Fengqi, ZHANG Jun. An approach of data mining for evaluation of complex formation using well logs [J]. Editorial office of ACTA PETROLEI SINICA, 2009, 30(4): 542-549.
[15]	LIU He, XIAO Danfeng. A novel low-damage vegetable gum-based fracturing fluid and its application in low-permeability reservoirs [J]. Editorial office of ACTA PETROLEI SINICA, 2008, 29(6): 880-884.

Failure analysis of drill stem based on support vector machine and cluster analysis theory

基于支持向量机和聚类分析理论的钻具失效分析方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments