Abstract:
Objective China's backbone natural gas pipeline network comprises numerous large-diameter and long-distance pipelines.Periodic pigging operations play a crucial role in ensuring the safe, stable and efficient operation of these pipelines. However, since current pigging operations rely heavily on operator experience and the establishment of monitoring points, the inability to simulate pigging schemes in advance prevents accurate prediction of parameters such as the operating speed, position and arrival time of the pig, and pressure changes in the pipe.
Methods To address this challenge, a mathematical model for dynamic prediction of the pigging process was constructed and put into practice according to the characteristics of actual pigging operations. Subsequently, the software for dynamic prediction of the pigging process was developed and applied to the actual pigging operations of several sections (A-B, B-C and D-E) of a designated pipeline for predicting the pigging time and pigging speed.
Results The improved two-phase flow transient pigging model and the dynamic prediction software accurately forecast critical parameters, including the running speed, position and arrival time at each station of the pig during the pigging process; The accuracy of the dynamic pigging prediction technology and the reliability of the software were verified using the monitoring data of 52 actual pigging operations; And compared with actual measurements, the software's predictions had average relative errors of 5.94% for pigging time and 6.56% for pigging speed.
Conclusion The utilization of the dynamic prediction technology and software enables efficient preparation of pigging operation schemes for natural gas pipelines, reduces manual workload, ensures pigging process safety, and enhances pipeline management intelligence.
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