This paper examines both the method and results of a leak detection sensitivity analysis for a liquid pipeline. A fractional factorial design is used to quantify both primary effects as well as confounding effects between parameters. The analysis examines the impact of uncertainty and bias in pressure and flow measurements, as well as spatial and temporal discretization on leak flow estimation. These are considered under conditions of transient pressures, the presence of a leak and with altered SCADA poll frequency. The results of the parametric study as well as the applicability of the general approach are discussed.
The ability of pipeline operators to swiftly detect pipeline leaks is critical to the safeguarding of public and environmental interests. One of the prevalent tools for achieving this ability within industry is the use of a real time transient model of the pipeline. A primary benefit of utilizing a real time transient model for pipeline leak detection is the ability to accurately represent the pressure profile of the pipeline under transient conditions (Learn, 2015). A more accurate representation of pipeline transients leads to a more accurate estimation of linepack and hence a lower error in the leak flow estimate. As a result, alarm threshold values can be lowered without increasing false alarm frequency, and a better leak detection sensitivity can be achieved.
One of the more challenging roles for a leak detection engineer is to assess and understand the multitude of parameters affecting the error in leak flow estimation. The most widely applied standard, API1149 (1993), provided an excellent theoretical framework for estimating leak flow uncertainty as a function of time averaging window and telemetry uncertainty. However, the most recent update to this standard recognizes that potentially many different parameters affect leak flow uncertainty and recommends a perturbation approach against a reference model. (Salmatanis, 2015)
Given the number of parameters which may affect leak detection sensitivity, a more efficient method is needed to assess the impact of such parameters. Assessing all the potential effects of all parameters within a large quantity of scenarios can be time consuming. It can be onerous to perform this analysis on pipelines in the early stages of project development, during which certain other design assumptions are yet to be confirmed. In addition, many projects may never progress beyond the prospecting stage despite significant design and analysis.