含硫气井完整性风险等级预测研究*

doi:10.16265/j.cnki.issn1003-3033.2017.10.026

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (10): 155-161.doi: 10.16265/j.cnki.issn1003-3033.2017.10.026

含硫气井完整性风险等级预测研究^*

张智¹ 教授, 何雨¹, 黄茜¹, 何汉平², 鲍洪志²

1 西南石油大学油气藏地质及开发工程国家重点实验室,四川成都 610500;
2 中国石化石油工程技术研究院,北京 100101

收稿日期:2017-06-19 修回日期:2017-08-30 出版日期:2017-10-20
作者简介:张智 (1976—),男,四川南充人,博士,教授,主要从事油气井工程方面的教学和科研工作。E-mail:wisezh@126.com。
基金资助:
国家科技重大专项资助(2016ZX05017-003);四川省科技厅项目(2016JQ0010);四川省省属高校科技创新团队建设计划资助项目(13TD0026)。

Research on prediction of integrity risk grade of sour gas well

ZHANG Zhi, HE Yu, HUANG Xi, HE Hanping, BAO Hongzhi

1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Southwest Petroleum University,Chengdu Sichuan 610500,China;
2 SINOPEC Research Institute of Petroleum Engineering,Beijing,100101,China

Received:2017-06-19 Revised:2017-08-30 Published:2017-10-20

摘要/Abstract

摘要： 为保证含硫气井在开发过程中安全高效生产,提出一种基于主成分分析(PCA)和BP神经网络的含硫气井完整性风险等级预测模型。首先,采用Bow-tie方法得到含硫气井完整性失效风险因素,通过模糊评价法对风险因素进行量化处理;然后,利用PCA提取综合指标,并结合BP神经网络得到预测模型,进而得到风险等级。研究结果表明:通过PCA能使BP神经网络的输入数据由28个减少至4个,所建模型的风险等级预测精度高于未经PCA的BP神经网络;通过PCA与BP神经网络结合的预测模型可识别含硫气井开发过程中完整性失效的风险因素,完善了含硫气井风险等级预测技术。

关键词: 含硫气井, 井筒完整性, 主成分分析(PCA), 风险评价, BP神经网络, 模糊评价

Abstract: In order to ensure the safe and efficient production of sour gas wells, a model was built for predicting sour gas wells integrity risk level on the basis of PCA and BP neural network method. Before building the model, a bow-tie model was built for identifying integrity failure risk factors. The factors were quantified by fuzzy evaluation method. Comprehensive indexes were extracted by PCA. An application of the prediction model was made to 5 sour gas wells. The results show that the input data of BP neural network can be reduced from 28 to 4 by PCA, and the prediction accuracy of risk grade is higher than that of BP neural network without PCA, and that the model built on the basis of combination of PCA and BP neural network can be used to identify the risk factors of integrity failure in sour gas well development and improve the risk grade prediction technology of sour gas wells.

Key words: sour gas well, wellbore integrity, principal component analysis(PCA), risk assessment, BP neural network, fuzzy evaluation

中图分类号:

X937

张智, 何雨, 黄茜, 何汉平, 鲍洪志. 含硫气井完整性风险等级预测研究^*[J]. 中国安全科学学报, 2017, 27(10): 155-161.

ZHANG Zhi, HE Yu, HUANG Xi, HE Hanping, BAO Hongzhi. Research on prediction of integrity risk grade of sour gas well[J]. China Safety Science Journal, 2017, 27(10): 155-161.

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含硫气井完整性风险等级预测研究^*

Research on prediction of integrity risk grade of sour gas well

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