Leak detection in gas transmission pipelines

Abstract

This dissertation applies a commercial flow simulation software package together with common signal processing techniques to the task of accurately detecting leakage in a large commercial gas pipeline. The techniques developed significantly improved the ability to produce accurate, reliable and stable leak detection predictions for the gas transmission pipeline studied and can be applied generally to other pipelines as well. Recommendations for minimum pipeline requirements to implement successful leak detection are also detailed. There are several commercial software packages available that perform some form of leak detection via system modelling. However, due to the commercial aspects of these products, vendors do not publish the detailed methods of leak detection. This thesis identifies the fundamental techniques required to have accurate and reliable leak detection on a gas transmission pipeline, whilst taking into account the lack of measurement data typically encountered on most gas pipelines. The investigation confirmed that a mass balance technique could be successfully used to produce stable leak detection results for compressible flow in gas transmission pipelines. This leak detection [using mass balance] can be achieved without flow measurement along the pipeline, instead, using only pressure and temperature measurements. Although it is recognized that flow measurement data will greatly improve the ability to detect leaks, the focus of this work is on pipelines where this flow measurement data at intermediate points along the pipeline is not available. It was also demonstrated the reliability of the leak detection was improved by the application of on-line signal processing techniques at various stages of the data processing.It was clear early into the investigation that the majority of the errors within the leak detection model were created by random errors from the input field data. These non-systematic errors from the measurement data that included pressure and temperature, produced interference with model output. This interference resembled random “white” noise that was removed by a combination of well established data filtering techniques. The most appropriate process of calculating leak detection flow was determined after analysing the results of different techniques applied to large quantities of actual pipeline operating data. The validation of the on-line techniques developed provides a valuable resource for those wishing to implement similar leak detection schemes elsewhere. Furthermore a software environment was chosen which incorporated an open input and output platform for data that could be interfaced with any operating system. Therefore these techniques can be applied to the numerous Supervisory Control and Data Acquisition (SCADA) systems in operation throughout the gas transmission industry, to provide a low cost solution to leak monitoring.