A Pigging Model for Wax Removal in Pipes
- Qiyu Huang (China University of Petroleum, Beijing) | Wenda Wang (China University of Petroleum, Beijing) | Weidong Li (China University of Petroleum, Beijing) | Yijie Ren (China University of Petroleum, Beijing) | Fangda Zhu (China University of Petroleum, Beijing)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- November 2017
- Document Type
- Journal Paper
- 469 - 475
- 2017.Society of Petroleum Engineers
- pipeline, wax breaking force, wax removal, pigging model
- 2 in the last 30 days
- 354 since 2007
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Pigging is widely used in pipelines for wax removal. However, pigging operation relies heavily on “rules of thumb.” Because of its complexity, rather-limited pigging models were presented to predict the wax-removal mechanics in past decades. This work aims to develop a pigging model for wax removal in pipelines. A unique experimental facility was designed and constructed for simulating pigging operation for wax removal in pipelines. This facility is composed of five main parts: an experiment system, a wax-casting system, a motor and control system, a measurement and data-acquisition system, and a specially designed pig system. The mixture of a crude-oil and a field-wax deposit was cast inside the test section to carry out the pigging experiments with disk-and-cup pigs. It was found that hardness of the scraping element in the pig has a profound effect on wax removal, and this effect depends closely on the wax thickness on the pipe wall. A pigging model, which could well explain the effects of wax thickness, wax hardness, pipe diameter, pig geometry, and hardness of the scraping element in the pig, was established on the basis of the experimental findings. To the best of our knowledge, this is the first time to incorporate into a pigging model the effect of the hardness of the scraping element in the pig on wax removal. Seventeen sets of pigging experiments were used to verify the developed pigging model, with an average relative error of 10.69%. The pigging model developed in this work could be a practical tool in designing economic and safe pigging programs.
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