A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines

Zhe Lu, Yuntong She and Mark Loewen
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Zhe Lu: Department of Civil and Environmental Engineering, University of Alberta, 7-203 Donadeo Innovation Centre for Engineering, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada
Yuntong She: Department of Civil and Environmental Engineering, University of Alberta, 7-203 Donadeo Innovation Centre for Engineering, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada
Mark Loewen: Department of Civil and Environmental Engineering, University of Alberta, 7-203 Donadeo Innovation Centre for Engineering, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada

Energies, 2017, vol. 10, issue 8, 1-17

Abstract: Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties on leak detectability of a commonly used real-time transient model-based leak detection system. Uncertainties in fluid properties, field sensors, and the data acquisition system were evaluated. Errors were introduced into the input variables of the leak detection system individually and collectively, and the changes in leak detectability caused by the uncertainties were quantified using simulated leaks. This study provides valuable quantitative results contributing towards a better understanding of how real-world uncertainties affect leak detection. A general ranking of the importance of the uncertainty sources was obtained: from high to low it is time skew, bulk modulus error, viscosity error, and polling time. It was also shown that inertia-dominated pipeline systems were less sensitive to uncertainties compared to friction-dominated systems.

Keywords: oil pipeline; computational pipeline monitoring; real-time transient model-based leak detection; uncertainties; leak detectability; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
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