《油气储运》

成品油管道泄漏次声波监测及信号处理方法

1.中国石油大学（华东）储运与建筑工程学院·山东省油气储运安全重点实验室;2.国家管网集团华南分公司;3.中国石油天然气管道局有限公司

Study on infrasonic leakage monitoring and signal processing for product oil pipeline

YIN Yuanbo¹，LI Yuxing¹，YANG Wen²，LU Shu³，ZHANG Chen²，LIU Cuiwei¹，YANG Kai³，WANG Wuchang¹

1.College of Pipeline and Civil Engineering, China University of Petroleum (East China)//Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety;2.PipeChina South China Pipeline Co. Ltd.;3.China Petroleum Pipeline Bureau

Keywords：product oil pipeline, leakage, infrasound, monitoring, wavelet analysis, random forest, minimum detectable leakage

DOI: 10.6047/j.issn.1000-8241.2024.08.007

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【目的】随着油气输送管道总里程不断增加，泄漏监测已成为保证管道安全平稳运行的关键技术之一。次声波监测因其灵敏度高、定位精度高、维护费用低等诸多优点备受关注，但其在成品油管道中的工程应用有待探讨。【方法】基于次声波监测基本原理，自主搭建了液体管道泄漏监测实验装置，分析了不同的泄漏孔尺寸、管道压力以及泄漏点距离工况下，次声波传感器采集信号的特征。分析了db系小波基与sym系小波基1～9层小波变换的信号处理效果；利用信号的15个时域特征与4个频域特征参与随机森林分类模型建模，以ROC（ReceiverOperatingCharacteristic）曲线下的面积（AreaUnderCurve,AUC）作为目标函数对模型参数进行优化，并采用基于WT-RF（WaveletTransform-RandomForest）的方法对实验数据进行信号处理与分类。【结果】将新建方法应用于国家管网集团华南分公司某成品油输送管道发现，经过sym2小波基8层分解处理后的次声波信号在时频域具有明显可识别特征，随机森林识别模型结合定位信息可实现生产管道中泄漏工况的误报率、漏报率均为0；在91km的成品油管道监测区间，定位误差800m左右，稳定泄漏速率0.0016m3/s，最小可检测泄漏速率为0.00046m3/s。【结论】次声波泄漏监测技术在成品油管道测试中获得了较好的效果，其误报率、漏报率均极低，且定位误差小，相关研究成果可为该技术在成品油管道的应用提供技术支持与参考。（图 13，表2，参[20]）

[Objective] With the total length of oil and gas transmission pipelines increasing due to booming development, pipeline leakage monitoring has emerged as one of the critical technologies to ensure the safe and stable operation of these pipelines. Infrasonic monitoring has garnered significant attention due to its high sensitivity, high positioning accuracy, and low maintenance costs. However, its engineering application in product oil pipelines requires further discussion. [Methods] Based on the basic principle of infrasonic monitoring, an experimental setup for liquid pipeline leakage monitoring was independently constructed, aimed to analyze the characteristics of signals acquired by infrasonic sensors across different leak hole sizes, pipe pressures, and distances from these sensors to the leak points. The signal processing effects of wavelet transforms at 1–9 layers on the db and sym wavelet bases were analyzed. Subsequently, a random forest classification model was established, incorporating fifteen time-domain features and four frequency-domain features of the signals. The model parameters were optimized, using the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) curve as an objective function. Furthermore, the experimental data were processed and classified, utilizing the method based on Wavelet Transform-Random Forest (WT-RF). [Results] The proposed approach was applied to a product oil transmission pipeline section of PipeChina South China Pipeline Co. Ltd., resulting in the following findings. Following an 8-layer decomposition on the sym2 wavelet basis, the infrasound signals exhibited distinct recognizable characteristics in both the time and frequency domains. The random forest identification model, supported by positioning information, showcased a zero false alarm rate and missing alarm rate under leakage conditions of the production pipeline. At a 91 km monitoring interval along the product oil pipeline, the positioning error was about 800 m, facilitating reliable monitoring up to a leak rate of 0.001 6 m3/s, with the minimum detectable leak rate recorded at 0.000 46 m3/s. [Conclusion] This study showcases the favorable experimental efficacy of infrasonic leakage monitoring technology for product oil pipelines, emphasizing extremely low false alarm rates and missing alarm rates, alongside small positioning errors. The findings of this study offer valuable technical support and serve as a reference for the application of this technology in product oil pipelines. (13 Figures, 2 Tables, 20 References)

引言
1 基于次声波信号的管道泄漏监测实验
2 信号处理
3 现场应用
4 结论

图1 基于次声波信号的管道泄漏监测实验装置组成示意图Fig. 1 Composition diagram of experimental setup for pipeline leakage monitoring based on infrasound signals

图2 不同实验工况下管道泄漏监测次声波信号图Fig. 2 Infrasound signal diagram for pipeline leakage monitoring under different experimental conditions

图3 基于次声波的某成品油管道泄漏监测现场应用设备布置示意图Fig. 3 Equipment layout for field application of infrasonic leakage monitoring in a product oil pipeline

表1 2022年10月9日某成品油管道泄漏现场测试工况表Table 1 Leakage conditions for field experiment of a product oil pipeline on October 9, 2022

图4 某成品油管道门站3第3次泄漏次声波信号时域特征与频域特征图Fig. 4 Time-domain and frequency-domain characteristics of infrasound signals in the 3rd round of leakage at gate station 3 of a product oil pipeline

图5 不同小波基与分解层数下某成品油管道泄漏次声波信号RSN、ERMS计算结果对比图Fig. 5 Comparison of RSN and ERMS calculation results for infrasound signals from leakage of a product oil pipeline under different wavelet bases and decomposition layers

图6 某成品油管道门站3第3次泄漏次声波信号小波变换处理前后对比图Fig. 6 Comparison before and after wavelet transform processing of infrasound signals for the 3rd round of leakage at gate station 3 of a product oil pipeline

图7 某成品油管道门站1与门站3第3次泄漏次声波信号处理图Fig. 7 Infrasound signal processing in the 3rd round of leakage at gate stations 1 and 3 of a product oil pipeline

图8 某成品油管道门站1与门站3第4次泄漏次声波信号处理图Fig. 8 Infrasound signal processing in the 4th round leakage at gate stations 1 and 3 of a product oil pipeline

图9 某成品油管道门站1与门站3第5次泄漏次声波信号处理图Fig. 9 Infrasound signal processing in the 5th round leakage at gate stations 1 and 3 of a product oil pipeline

表2 某成品油管道泄漏监测次声波信号识别结果表Table 2 Identification results of infrasound signals for leakage monitoring of a product oil pipeline

图10 基于次声波的某成品油管道监测异常信号识别定位图Fig. 10 Identification and positioning of abnormal signals in infrasonic monitoring of a product oil pipeline

图11 某成品油管道油品泄漏速率变化示意图Fig. 11 Variations in oil leakage rates in a product oil pipeline

图12 某成品油管道油品泄漏计算时间与实际时间对比图Fig. 12 Comparison between calculated and actual durations of oil leakage in a product oil pipeline

图13 某成品油管道最小可检测泄漏速率修正系数计算结果图Fig. 13 Calculation results for correction coefficient of minimum detectable leakage rate of a product oil pipeline

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引言

1 基于次声波信号的管道泄漏监测实验

2 信号处理

3 现场应用

4 结论