埋地管道微小泄漏与保温层破坏分布式光纤监测试验

Test on tiny leakage and insulating layer damage monitoring of buried oil pipelines using distributed optical fiber temperature sensor

分布式光纤测温技术可以实现埋地管道泄漏的实时在线监测,但是目前国内外在微小泄漏识别、保温层破坏导致的温度异常、传感器位置与监测性能的关系等方面的研究仍不够完善。设计了一套埋地输油管道泄漏监测试验装置,通过对足尺寸管道的泄漏模拟,研究了管道输送介质温度、泄漏流量、保温层破坏程度、传感器布设位置对分布式光纤传感器监测性能的影响,提出了基于分布式温度监测数据的实时泄漏识别方法。研究结果表明:管内介质与管外土体之间存在温度差异是实现泄漏监测的关键,在内压和温度确定的条件下,泄漏流量对监测效果的影响不显著;为了有效监测不同环向位置的泄漏,建议将分布式光纤传感器布设于管道底部;基于温度监测时程数据的B值识别算法,可以有效地区分温度异常情况是由管道泄漏或保温层破坏引起的,具有较高的识别精度。

The distributed fiber optical temperature sensing technique can conduct real-time online monitoring on the leakage of buried oil pipelines. At present, however, it is necessary to enhance the studies on the tiny leakage identification method, the temperature abnormality due to the damage of insulating layer, the relationship between the sensor locations and the monitoring performance and so on at home and abroad. In this paper, a set of test apparatus for monitoring the leakage of buried oil pipeline was designed. Then, the full-scale pipeline leakage was simulated to systematically study the effects of transported medium temperature, leakage flow rate, insulating layer destroying degree and sensor location on the monitoring performance of distributed optical fiber sensor. Finally, a real-time leakage identification method based on distributed temperature monitoring data was developed. It is indicated that the temperature difference between the inner medium and the outer soil is the critical parameter for leakage monitoring. The flow rate of the leakage has little impact on the monitoring results when inner pressure and temperature are determined. It is recommended to place the distributed optical fiber temperature sensors beneath the bottom of the pipeline so as to monitor the leakage at circumferential positions effectively. The B-value identification algorithm based on temperature monitoring time history data can effectively discriminate the leakage from the insulating layer damage, so its identification accuracy is higher.