基于灰色理论的储气库管柱剩余寿命预测研究
Research on Predicting the Remaining Life of Gas Storage Pipe Strings Based on Grey Theory
魏昊天 1董绍华 1段宇航 2徐晴晴 1马晓红 3赵景涛3
作者信息
- 1. 中国石油大学(北京)管道技术与安全研究中心
- 2. 国家管网集团工程技术创新有限公司
- 3. 中国石油吉林油田公司
- 折叠
摘要
储气库在天然气的运输和储存方面发挥着不可替代的作用.针对储气库管柱腐蚀变量众多、关系复杂,且获取数据样本困难等问题,提出了基于灰色理论的储气库管柱剩余使用寿命预测方法.首先,结合灰色理论,设计了一种可动态更新建模数据的GM(1,1)(Grey Model)模型.其次,通过中石油某地下储气库中C5井中管柱剩余壁厚的样本数据集,建立了储气库管柱定量腐蚀剩余寿命预测模型,统计预测模型的平均相对误差,并与监测数据、最小二乘预测法进行比较.结果表明:基于灰色理论的预测方法平均相对误差为 0.27%,模型预测达到一级精度,可作为预测储气库管柱安全性的一种新方式.最后,使用Matlab进行软件界面设计,开发储气库管柱剩余寿命预测软件,更加直观、快捷地实现其剩余寿命定量预测,最大限度地延长管柱乃至整个储气库的寿命.
Abstract
Gas storage plays an irreplaceable role in the transportation and storage of natural gas.Aim-ing at the problems of numerous corrosion variables,complex relationships,and difficulty in obtaining data samples,a method for predicting the remaining service life of gas storage strings based on grey the-ory is proposed.First,combined with the grey theory,a GM(1,1)(Grey Model)model that can dy-namically update modeling data is designed.Secondly,based on the sample data set of the residual wall thickness of pipe strings in C5 well in an underground gas storage of CNPC,a quantitative corrosion residual life prediction model of pipe strings in gas storage is established.The average relative error of the prediction model is calculated,and compared with the monitoring data and the least square prediction method.The results show that the average relative error of the prediction method based on grey theory is 0.27%,the model prediction reaches first-level accuracy,which can be used as a new way to pre-dict the safety of gas storage pipe strings.Finally,the software interface design is carried out using Mat-lab,and the remaining life prediction software of the gas storage strings is developed,which realizes the quantitative prediction of the remaining life of the pipe strings more intuitively and quickly,and maxi-mizes the life of the pipe strings and even the entire gas storage.
关键词
储气库管柱/腐蚀/剩余寿命预测/灰色理论Key words
gas storage pipe strings/corrosion/remaining life prediction/grey theory引用本文复制引用
基金项目
中国石油科技创新基金(2021DQ02-0801)
出版年
2024
NETL
NSTL
万方数据