老工业采油井场输油管道泄漏真假异常识别方法

Identification method for true and false anomalies of oil pipeline leakage in an old industrial oil production well site

在生产环境下油管泄漏异常极少发生,并且人为调整泵频率、仪表校准等带来的监测数据曲线突变,即假异常,混淆在真异常中难以被区分,导致传统基于机器学习的泄漏异常识别方法召回率较低,误报率较高。针对该问题,提出一种基于真假异常区分的输油管道泄漏异常识别方法,利用一类支持向量机(One-Class Support Vector Machine,OCSVM)学习输油管道的正常工作模式,并利用该模式筛出管道的疑似异常,即真假异常。通过叠加多源数据的方式增加真假异常曲线形态差异,并利用相似性聚类发现泄漏事件的异常模式。将该方法应用到中国西北某老工业采油井场输油生产环境中进行验证,结果表明:泄漏异常识别召回率为100%,假异常排除率达到83.49%。该方法实现了在复杂生产环境中对输油管道泄漏异常事件的实时、高效监测,并为机器学习方法应用于生产环境提供了实践思路。(图8,表4,参28)

Abnormal oil pipeline leakage rarely occurs in the production environment.Besides,the abrupt change in the monitoring data curve brought about by the artificial adjustment of pump frequency and instrument calibration,i.e.,false anomaly,is hard to be distinguished from the true anomaly,leading to a low recall rate and a high false alarm rate of the traditional abnormal leakage identification method based on machine learning.To solve such problems,an abnormal leakage identification method based on the distinction between true and false anomalies was proposed.Specifically,the normal working mode of oil pipelines were learned with the One-Class Support Vector Machine(OCSVM),and the suspected pipeline anomalies,i.e.,true and false anomalies,were screened out with the model.Then,the morphological difference between true and false anomalies curves was increased by superimposing the multi-source data,and in this way,the anomaly patterns of leakage events were found by similarity clustering.Further,the method was verified by applying it to the oil transportation environment.The results show that the recall rate of abnormal leakage identification method is 100%,and the exclusion rate of false anomaly is 83.49%.Generally,this method achieves the real-time and efficient monitoring of abnormal pipeline leakage events in complex operation environments and provides a practical idea for the application of machine learning methods in production environments.(8 Figures,4 Tables,28 References)