管道内检测数据对齐方法与应用

Alignment method of pipeline in-line inspection data and its application

随着内检测的不断推进,大部分管道已经开展了两轮及以上次数的内检测作业,获得了大量内检测数据。由于内检测受外部环境及检测误差的影响,多轮内检测数据在里程、缺陷识别与量化方面存在一定差异,难以实现多轮内检测数据的快速对齐,且人工对齐工作量巨大。为研究内检测数据的快速对齐方法,结合大量内检测数据,构建了内检测数据对齐算法模型,基于该模型实现了内检测数据的快速对齐,并通过不同单位、不同格式的内检测数据进行应用测试。测试结果表明:该方法可以实现管道阀门、三通等特征100%对齐,管节对齐比例达99%以上,弯头对齐比例达90%以上。基于该方法,可快速对内检测数据进行深度挖掘分析,预测管道本体缺陷发展趋势,为管道腐蚀控制及管道本体管理提供数据支撑,实现管道本体风险的预控,提高管道完整性管理水平。

With the continuous development of in-line inspection, two or more rounds of in-line inspection have been performed to most pipelines, and a large number of in-line inspection data are obtained. Due to the influence of external environment and detection error, the in-line inspection data in multiple rounds have deviations in mileage, defect identification and quantification, they are difficult to be aligned rapidly, and manual alignment is a huge workload. In order to study the rapid alignment method of in-line inspection data, an in-line inspection data alignment algorithm model was established based on the massive in-line inspection data, and with the model, the rapid alignment of in-line inspection data was realized, for which application test was conducted with the in-line inspection data of different units in different formats. The test results show that: the achievable alignment with this method is 100% for the features of pipeline valve and tee, over 99% for pipe joint and at least 90% for elbow. Further, based on the method, the inspection data could be quickly mined and analyzed in depth and the development trend of defects on pipe body could be predicted, so that data support could be provided for internal and external corrosion control and management of pipe body, the risks of pipe body could be precontrolled, and the pipeline integrity management could be improved.