Research on predictions of casing damage based on machine learning

被引：0

作者：

Zhao Y. ^{[1
,2
,3
]}

Jiang H. ^{[1
]}

Li H. ^{[2
]}

Liu H. ^{[4
]}

Han D. ^{[4
]}

Wang Y. ^{[2
]}

Liu C. ^{[4
]}

机构：

[1] College of Petroleum Engineering in China University of Petroleum(Beijing), Beijing

[2] Petroleum Engineering and Data Mining Laboratory, China University of Petroleum(Beijing), Beijing

[3] College of Artificial Intelligence in China University of Petroleum(Beijing), Beijing

[4] The 7th Oil Production Plant of Daqing Oilfield, Daqing

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2020年 / 44卷 / 04期

关键词：

Casing damage; Casing damage prediction; Data driven; Machine learning; Time series;

D O I：

10.3969/j.issn.1673-5005.2020.04.007

中图分类号：

学科分类号：

摘要：

Casing damage can be the result of a number of factors during a long process of oilfield production, which involves complicated and interaction of various factors. Regarding to this characteristics, a data-driven machine learning prediction approach of casing damage was proposed. Firstly, the evaluation indexes of casing damage were designed from the massive historical field data related to multiple factors, including water injection pressure, injection volume and injection and production pressure difference. Then, samples of individual well casing damage were collected after the data preprocessing, feature extraction and correlation analysis. Finally, the algorithms of random forest and support vector machine were applied to build the predictions models of casing damage based on different feature combinations. The results of case studies show that high-pressure water injection is the major factor causing casing damage, and the prediction accuracy of the model is 93.3% using the MDA feature combination and the support vector machine algorithm. © 2020, Editorial Office of Journal of China University of Petroleum(Edition of Natural Science). All right reserved.

引用

页码：57 / 67

页数：10