As one of the main risks of long-distance oil and gas pipelines, the third-party damage has a huge impact on the consequences of the accident. At present, the third-party damage prevention mainly adopts the safety early warning technologies such as line patrol, fiber-optical vibration and UAV line patrol, but there are many problems such as untimely warning, false alarm and missed report. As the location technology of mobile device matures, the huge user group provides massive data sources for the collection of location data, with which the activity track and activity characteristics of the third party along the pipeline can be directly obtained. Therefore, this paper proposes a method to identify the third-party damage behavior based on the location data of mobile devices. Firstly, according to the type of the third-party damage, the corresponding characteristics of the third-party damage are extracted from the relevant historical spatiotemporal location data, including time characteristics, number characteristics and location characteristics. Then, the location information of the third party activity near the target pipeline is obtained and the data is processed to remove the influence of noise, so as to reduce the computational burden of the subsequent identification process. Calculate the similarity with the third-party damage feature and the difference degree of neighborhood trajectory based on the data feature grouping (Similarity feature and Difference feature) to identify the type of the third-party activity, so as to realize the intelligent prediction of the third-party damage accident and improve the intelligent management level of the pipeline. Finally, taking a 10km pipeline section as an example on the basis of theoretical research, 52,994 pieces of valid data were obtained by collecting nearby mobile devices location information for 10 days. The method of this paper is used to preprocess the collected data and calculate their similarity and difference, and 232 third-party damage events are identified, including 196 suspected private excavation damage events, 28 engineering construction damage events, and 8 stolen oil and gas damage events. After the on-site verification of the suspected damage by the line patrol , the results show that the method can better identify the the third-party personnel activities of the pipeline.


Intelligent Prevention Method for Third-Party Damage of Long-Distance Pipeline Based on Mobile Devices Location Information

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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