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Data Smoothing for Leak Detection

[+] Author Affiliations
Jianping Gao, Chris Lewis, Tania Rizwan

Enbridge Pipelines Inc., Edmonton, AB, Canada

Paper No. IPC2012-90108, pp. 717-725; 9 pages
doi:10.1115/IPC2012-90108
From:
  • 2012 9th International Pipeline Conference
  • Volume 1: Upstream Pipelines; Project Management; Design and Construction; Environment; Facilities Integrity Management; Operations and Maintenance; Pipeline Automation and Measurement
  • Calgary, Alberta, Canada, September 24–28, 2012
  • Conference Sponsors: International Petroleum Technology Institute, Pipeline Division
  • ISBN: 978-0-7918-4512-7
  • Copyright © 2012 by ASME

abstract

Noise is prevalent in all pipeline instrumentation and measurement system data and is perhaps one of the biggest environmental factors, which determines if a system will operate reliably in practice. Noise present in data can be random or repetitive, occurring continuous or in isolated bursts and can have a multitude of sources. At Enbridge, the leak detectability of Computational Pipeline Modeling (CPM) based leak detection (LD) systems is notably affected by the presence of noise in the pipeline data. Data smoothing of noisy pipeline data is a non-trivial process; these techniques are used not only to eliminate “noise” from the operating data, but also to extract real trends and patterns within the data, and to maintain important characteristics of the signal itself, particularly during transient time periods. Finding an effective data smoothing methodology has, therefore, become an important and challenging task for the Leak Detection team at Enbridge.

This paper presents an innovative approach on data smoothing with the aim of improving data quality and detectability of the CPM based LD systems using the Wavelet analysis method which is incorporated in the MATLAB toolbox. Wavelet-based algorithms have proved to be quite an effective tool for the analysis, synthesis, denoising, and compression of signals and provide more precise information about signal data than other signal analysis techniques, such as Fourier Transform. In this paper the Wavelet analysis technique has been reviewed and is shown to be effective in manipulation and smoothing of pipeline data, both during the transient and steady state conditions. Based on the algorithm and utilities developed, extensive tests have been performed and comparison studies have been made to validate the methodology using simulated leak test data, fluid withdrawal test data, and API 1130/1149 calculations etc. The test results indicate that data smoothing technique employed has a substantial effect on the data quality which in turn has significantly improved the detectability of Enbridges current leak detection system.

Copyright © 2012 by ASME
Topics: Leakage

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