凝析气田集输管线腐蚀预测研究

doi:10.16265/j.cnki.issn1003-3033.2019.11.022

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (11): 135-140.doi: 10.16265/j.cnki.issn1003-3033.2019.11.022

凝析气田集输管线腐蚀预测研究

骆正山教授, 宋莹莹, 王小完, 毕傲睿

西安建筑科技大学管理学院,陕西西安 710055

收稿日期:2019-08-26 修回日期:2019-10-14 发布日期:2020-10-30
作者简介:骆正山 (1969—),男,陕西汉中人,博士,教授,博士生导师,主要从事管理科学与工程、信息管理与信息系统、油气管道风险评估等方面的教学与科研工作。E-mail:luozhengshan@ 163.com。
基金资助:
国家自然科学基金资助(41877527);陕西省社会科学基金资助(2018S34)。

Corrosion prediction of gathering pipelines in condensate gas field

LUO Zhengshan, SONG Yingying, WANG Xiaowan, BI Aorui

School of Management, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China

Received:2019-08-26 Revised:2019-10-14 Published:2020-10-30

摘要/Abstract

摘要： 为提高凝析气田集输管线腐蚀速率的预测精度,基于灰色关联分析(GRA)法,融合随机森林回归(RFR)算法,建立内腐蚀速率预测模型,分析气田集输管线内腐蚀原因;采用GRA优选特征因素变量作为RFR的输入,以内腐蚀率作为目标因素输出,并以雅克拉凝析气田数据为例,对比验证构建的RFR预测模型、反向传播(BP)神经网络和支持向量机(SVM)预测模型。结果表明:通过GRA排序得到管线内腐蚀的主要因素有:CO₂体积分数、Cl^-质量浓度、压力、温度、流速。同时RFR预测模型的均方根误差、平均相对误差均低于对比模型相应值,且决定系数达到96.48%。

关键词: 集输管线, 腐蚀因素, 灰色关联分析(GRA), 随机森林回归(RFR), 腐蚀速率

Abstract: In order to improve the prediction accuracy of the corrosion rate of condensate gas field gathering pipelines, an internal corrosion prediction model based on GRA and RFR was constructed to analyze the causes of internal corrosion. The GRA was used to select characteristic variables as inputs of RFR, and the internal corrosion rate was used as target factor output. Taking the data of Yakela condensate gas field as an example, the PRF prediction model was verified by comparison with the Back Propagation(BP)neural network and Support Vecor Machine(SVM) prediction model. The results show that the main factors of corrosion in pipelines obtained by GRA are CO₂ volume fraction, Cl^-concentration, pressure, temperature and flow rate, that the root mean square error, average relative error of the RFR prediction model are lower than those of BP neural network and SVM prediction model, and that determination coefficient of RFR prediction model is 96.48%.

Key words: condensate gas field, corrosion factors, grey relational analysis (GRA), random forest regression (RFR), corrosion rate

中图分类号:

X937

骆正山, 宋莹莹, 王小完, 毕傲睿. 凝析气田集输管线腐蚀预测研究[J]. 中国安全科学学报, 2019, 29(11): 135-140.

LUO Zhengshan, SONG Yingying, WANG Xiaowan, BI Aorui. Corrosion prediction of gathering pipelines in condensate gas field[J]. China Safety Science Journal, 2019, 29(11): 135-140.

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凝析气田集输管线腐蚀预测研究

Corrosion prediction of gathering pipelines in condensate gas field

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