MONITORING GAS DISTRIBUTION PIPELINES

Abstract

Pipelines are a vital tool for transporting materials, such as natural gas, oil, and water. However, in extreme circumstances, defects such as blockages and leakages can occur. To limit the economic loss and environmental consequences of such events, it is important that any defects can be detected and located at an early stage, preferably before failure occurs. Research at the University of Manchester has led to the development of a tool that uses acoustic pulse reectometry (APR) to locate and characterise defects and features in tubes and pipes. The work described in this thesis began with the modelling and validation of acoustic attenuation in pipes. Previously published validation studies focused on short pipes (< 40 m) where high frequencies are dominant. The present work focuses on much longer pipelines where low frequencies play a much larger role. As such, comprehensive laboratory experiments were conducted to measure the attenuation of acoustic signals of varying frequencies in pipes with lengths of up to 200 m and inner diameters between 15 mm and 39.8 mm. The results of these experiments showed that theoretically obtained attenuation functions fitted measured results to within 5%. This provided evidence to suggest that, in theory, APR could be applied to high-pressure gas pipelines to detect full blockages with lengths of up to 100 km. This result was supported by the successful application of the theory to a pipe with a distance exceeding 12 km. A major weakness that restricts the deployment of APR technology is that even when applied to single pipes with only a few axial features, the results can be difficult to interpret. To aid the interpretation of the recorded APR measurements, a numerical simulator was developed, which was able to estimate the acoustic attenuation as it travels inside a pipe. This simulator models the propagation of acoustic waves in a cylindrical tube (waveguide) by considering the effects of both viscous and thermal attenuation, as well as changes in the internal cross section of the tube. The simulator divides the tube into discrete cylindrical segments, each segment being characterised by a digital filter that defines transmission and attenuation. By comparing the expected results from the simulator with those obtained from the real system, defects, such as partial blockages can be detected and located. The simulator's ability to characterise a range of defects, such as different forms of blockage, holes and erosion was thoroughly assessed utilising a number of pipes with lengths of up to 200 m and inner diameters of 39.8 mm in the laboratory. These results showed that when there were no uncertainties in the pipe layout, the experimental and simulated results were consistent to within approximately 3%. As final validation of the simulator, it was applied to an industrial pipeline with a length of more than 12 km. The simulator was able to accurately estimate the attenuation of the acoustic signal in the pipe and was also able to locate a blockage within this pipe with an accuracy of less than 5 m. The single pipe simulator was extended such that it was capable of modelling the behaviour of acoustic signals in pipeline networks. This is important if APR is to be applied to pipeline networks, such as those used for gas distribution. A network model was used to build the pipe network simulator; the model considered time, axial location and branch number. To validate the accuracy of the pipe network simulator, a series of laboratory tests were conducted using different pipeline network layouts. Data collected from a series of field tests were also used to verify the accuracy of the pipe network simulator. These tests showed that the network simulator was able to accurately detect and locate a number of features located within pipeline networks. The size of the pipes used in evaluating the simulator ranged from 50 mm to 200 mm with lengths of 60 m to 400 m.

Date of Award	31 Dec 2017
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Barry Lennox (Supervisor) & Zhengtao Ding (Supervisor)