Dynamic prediction method of casing damage based on rough set theory and support vector machine

被引：2

作者：

Zhou Y.-J. ^{[1
,2
]}

Jia J.-H. ^{[2
]}

Li R.-H. ^{[3
]}

机构：

[1] College of Petroleum Engineering in China University of Petroleum

[2] Institute of Drilling Technology, Shengli Petroleum Administrative Bureau

[3] Geology Institute of Daqing Yushulin Oilfield

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2010年 / 34卷 / 06期

关键词：

Attribute reduction; Casing damage; Dynamic prediction; Rough set; Support vector machine;

D O I：

10.3969/j.issn.1673-5005.2010.06.013

中图分类号：

学科分类号：

摘要：

Aiming at casing damage problem affected by both geological factors and engineering factors, with characteristics of uncertainty, fuzziness, time-varying, a dynamic prediction method of casing damage was proposed based on rough set integrated with support vector machine. The influencing factors for casing damage were reduced by using rough set theory and the main factors causing casing damage were obtained. Then prediction models were established based on support vector machine, and the problem that the traditional support vector machine can not deal with dynamic data and is prone to dimension disasters with large samples, was avoided in this method. The field application in Yushulin Oilfield indicates that the prediction consistent rate is 72.7% in this method, which provides guidance for oilfield adjusting development parameters and developing casing protection measures early.

引用

页码：71 / 75

页数：4

共 11 条

[1] Diao S., Yang C.-H., Liu J.-J., Et al., Mechanism of seepage induced casing damage and numerical simulation, Rock and Soil Mechanics, 29, 2, pp. 327-331, (2008)
[2] (1994)
[3] Pei G.-H., Ji Y.-J., Analysis on geological effects on oil and water well casing damage, Journal of Wuhan Polytechnic University, 28, 3, pp. 102-105, (2009)
[4] (2001)
[5] Deng J.-Y., Wang Q.-R., Mao Z.-Y., Et al., Support vector regression hybrid a lgorithm based on rough set, Journal of China University of Petroleum (Edition of Natural Science), 33, 5, pp. 159-163, (2009)
[6] Liang W.-K., Zhao D.-L., Ma W., Et al., Fault diagnosis of hydroelectric units based on rough set & RBF network, Chinese Journal of Scientific Instrument, 28, 10, pp. 1806-1809, (2007)
[7] Wang W., Ma D.-H., Su J.-Y., Et al., Study on predicting method for earth quake damage to underground pipelines system based on rough set and support vector machine, Journal of Basic Science and Engineering, 17, 2, pp. 274-279, (2009)
[8] Yang P.-J., Yin X.-Y., Prestack seismic inversion method based on support vector machine, Journal of China University of Petroleum (Edition of Natural Science), 32, 1, pp. 37-41, (2008)
[9] Xu C.-H., Chen G.-M., Xie J., Structural reliability analysis method based on support vector machines and Monte Carlo and its application, Journal of China University of Petroleum (Edition of Natural Science), 32, 4, pp. 103-107, (2008)
[10] Zhang X.-G., Introduction to statistical learning theory and support vector machines, Acta Automatica Sinica, 26, 1, pp. 32-42, (2000)

← 1 2 →