Experimental Observation and Analysis of Inverse Transients for Pipeline Leak Detection

TLDR

Fluid transients generate abundant pressure‑wave data, enabling new leak‑detection and pipe‑roughness calibration techniques that use inverse transient analysis (ITA) to detect, locate, and quantify leaks. The study advances ITA and presents laboratory‑pipeline experiments to detect leaks. The authors employ model parsimony to limit unknown leak candidates in ITA, simplifying the minimization problem. Experiments show that data and model errors can be mitigated with compensation techniques, that rapid small‑magnitude input transients enhance ITA uniqueness, that head‑measurement boundary conditions reduce sensitivity, and that the method successfully detects and quantifies single and multiple leaks in a laboratory pipeline.

Abstract

Fluid transients result in a substantial amount of data as pressure waves propagate throughout pipes. A new generation of leak detection and pipe roughness calibration techniques has arisen to exploit those data. Using the interactions of transient waves with leaks, the detection, location, and quantification of leakage using a combination of transient analysis and inverse mathematics is possible using inverse transient analysis (ITA). This paper presents further development of ITA and experimental observations for leak detection in a laboratory pipeline. The effects of data and model error on ITA results have been explored including strategies to minimize their effects using model error compensation techniques and ITA implementation approaches. The shape of the transient is important for successful application of ITA. A rapid input transient (which may be of small magnitude) contains maximum system response information, thus improving the uniqueness and quality of the ITA solution. The effect of using head measurements as boundary conditions for ITA has been shown to significantly reduce sensitivity, making both detection and quantification problematic. Model parsimony is used to limit the number of unknown leak candidates in ITA, thus reducing the minimization problem complexity. Experimental observations in a laboratory pipeline confirm the analysis and illustrate successful detection and quantification of both single and multiple leaks.