Leak detection in gas pipeline networks using an efficient state estimator. Part-I: Theory and simulations

https://doi.org/10.1016/j.compchemeng.2010.10.006Get rights and content

Abstract

Dynamic simulation models can be used along with flow and pressure measurements, for on-line leak detection and identification in gas pipeline networks. In this two part paper, a methodology is proposed for detecting and localizing leaks occurring in gas pipelines. The main features of the proposed methodology are: (i) it is applicable to both single pipelines and pipeline networks and (ii) it considers non-ideal gas mixtures. In order to achieve the desired computational efficiency for on-line deployment, an efficient state estimation technique based on a transfer function model, previously developed by the authors, is embedded in a hypothesis testing framework. In Part-I of this paper, a detailed description of the methodology is presented, and its performance is evaluated using simulations on two illustrative pipeline systems. The proposed method is shown to perform satisfactorily even with noisy measurements and during transient conditions, provided there is sufficient redundancy in the measurements.

Introduction

Pipelines are used extensively all over the world for transportation and distribution of water, natural gas and other light petroleum products. Natural gas and petroleum products are carried over long distances from oil fields and refineries to customers and communities. Majority of these pipelines are buried and pass through crowded cities and remote areas such as forests and farms. One of the most difficult problems affecting the safe operation of pipeline systems is development of rupture leaks, caused by corrosion and pressure surges. A challenging task for the operators of these systems is to detect leaks as and when they occur, and subsequently locate them.

Leak detection methodologies can be broadly classified into hardware based and software based systems. Hardware based leak detection systems include pigging (Furness & Reet, 1998), acoustic methods (Sharp and Campbell, 1997, Watanabe and Himmelblau, 1980), tracer gas methods (Tracer Research Corporation, 2003), sensor cable method (Sandberg, Holmes, McCoy, & Koppitsch, 1989), fiber optic methods (McLean et al., 2003), infrared photography methods (Eidenshink, 1985), and radar methods (Gopalsami & Raptis, 2001). Hardware based leak detection systems often provide very accurate leak location at the expense of high system costs and complexity of installation (Geiger et al., 2003). Also, some of the hardware based leak detection systems (pigging, acoustic method, etc.) are used only periodically to test the integrity of the pipeline. In contrast, software based solutions allow continuous on-line monitoring and rapid detection of leaks, and are therefore being actively sought these days.

Several model based solutions for leak detection in pipelines have been proposed in the literature, particularly for water distribution networks. Although these methods cannot be directly applied to gas pipeline networks due to the compressible nature of gases, some of these methods are reviewed here due to close similarities in the dynamic simulation models for water and gas pipelines. Brunone and Ferrante (2001), Beck, Curren, Sims, and Stanway (2005), and Misiunas, Vitkovsky, Olsson, Simpson, and Lambert (2005) have developed leak detection methods based on the analysis of pressure transients induced or reflected at a location where there is a leak. In these methods, every singularity of the system, such as junctions, nodes and bends reflects incident waves giving misleading information on the location of real leaks (Covas & Romas, 1999). Liggett and Chen (1994), and Vitkovsky, Simpson, and Lambert (2000) developed leak detection methods for water distribution networks, based on inverse transient analysis. These inverse transient methods detect leakages only at nodal points. They also require significant computational effort. In recent years, leak detection approaches based on the analysis of how the transient regime in the frequency domain is affected by the presence of a leak have been gaining attention (Ferrante and Brunone, 2003, Kim, 2005, Lee et al., 2005, Mpesha et al., 2002). These methods are applicable only for well defined boundary conditions, and are very sensitive to the topology of the system. Accuracy of leak detection is affected by the presence of other singularities and free damping of the system. The fact that leaks in pipelines dampen the transient events was used by Wang, Lambert, Simpson, Liggett, and Vitkovsky (2002) to develop a method for finding leak location and magnitude in water pipelines. Application of the above methods to networks is complex and in many cases, also interferes with the normal operations of the pipeline.

One of the earliest and most popular methods for detecting leaks in gas pipelines is the volume balance method (Griebenow and Mears, 1989, Liou and Tian, 1994). In this method, flows entering and leaving the pipeline, and other process variables like pressure and temperature are measured. A leak is detected when the mass of the fluid exiting from the pipeline differs from the estimated mass entering the pipeline, after accounting for the line pack. These methods can be used only for detecting a leak and not for identifying the leak location. Leak detection methods based on the use of state estimation models have been developed in the past for water distribution networks (Andersen and Powell, 2000, Mukherjee and Narasimhan, 1996) and gas pipelines (Benkherouf and Allidina, 1988, Billmann and Isermann, 1987, Emara-Shabaik et al., 2002). Billmann and Isermann (1987) used a non-linear state estimation procedure to detect and localize small leaks in pipelines carrying compressible fluids. In this method, measured flow and pressure data are compared with those predicted using a transient process model (state estimator), and a leak is detected if the discrepancy between the two is greater than prescribed limits. This method requires pressure and flow measurements at the inlet and outlet of the pipeline. Also, this method has been applied to only single pipeline systems. Benkherouf and Allidina (1988) used an Extended Kalman filter for simultaneous state and parameter estimation in gas pipelines. The leak was modeled using multiple parameters representing the unknown constant leak magnitude at every node in the pipeline. This method has been developed assuming pressure wave velocity to be constant, which is valid for only ideal gases and isothermal conditions. Liu, Zang, and Zhou (2005) improved the accuracy of the above method by using an adaptive particle filter (APF) algorithm. However, applicability of this method was demonstrated for the case of leaks occurring under steady state conditions. Also, only single pipelines were considered. Emara-Shabaik et al. (2002) also used a Modified Extended Kalman Filter (MEKF) state estimation technique, in conjunction with a transient model. Ideal gas assumption is used and a backward time-centered space discretization was also used for solving the governing partial differential equations. However, this approach was demonstrated only on a single pipeline system and simulation results for only a 10% leak case were presented. Furthermore, the method detects leaks only at the discretized nodes. Based on a detailed survey of currently available leak detection methods for oil and gas pipelines, Scott and Barrufet (2003) concluded that conventional material balance methods remain the most widely used methods for leak detection in commercial software packages. Occasionally, these are supplemented with momentum balance methods. They concluded that there exists a need for independent verification and demonstration of capabilities of these methods.

Above literature review indicates that although many methods have been proposed for model based leak detection in gas pipelines, none of these are applicable to networks. Furthermore, they have not considered either non-ideal behavior of the gas arising from high pressures or composition of the natural gas. Also, applicability of the methods for detecting leaks that may occur during normal transient operating conditions has not been demonstrated explicitly. In this work, we propose a reliable and computationally efficient method for detection and identification of leaks in gas pipeline networks. The proposed method uses the available pressure and flow rate measurements, sampled at regular intervals and is based on an efficient state estimation technique, developed by the authors (Reddy, Narasimhan, & Bhallamudi, 2006). The key features of the proposed methodology are: (i) it is applicable to both single pipelines as well as pipeline networks, (ii) it is applicable to non-ideal gas mixtures, (iii) it can detect and identify leaks even when they occur during transient conditions created by normal operations, and (iv) it can be applied on-line because it is based on a state estimation technique which uses a computationally efficient transfer function model of a pipe segment. Performance of the proposed methodology is evaluated using simulations on two illustrative pipeline systems. These include a real life 204.7 km long series pipeline system, and a hypothetical eight-node-nine-pipe network. The effect of measurement noise and the redundancy in the availability of measured data on the performance of the method are also studied. In a companion paper, the proposed methodology is validated using experimental data obtained on a laboratory scale model and operating data obtained from tests conducted on a real life pipeline carrying natural gas.

Section snippets

State estimation

An efficient state estimation method for a gas pipeline network forms the basic building block to develop an on line methodology for leak detection. The state estimation model provides flow and pressure profiles for the entire pipeline network, which best fit the measured pressures and flows available at some locations. The state estimation models takes into account the measurement noise and exploits the redundancy in measurements to obtain the estimates. The proposed leak detection methodology

Results and discussion

The online leak detection and identification method proposed in this work consists of the following objectives: (a) detection of the time at which a leak has occurred by continually monitoring the measured pressures and flows; (b) identification of the pipe segment or branch where the leak has occurred; and (c) estimation of the leak location and magnitude. Among the above objectives, it is important to detect the time of occurrence of a leak without significant delay. It is also important to

Conclusions

A computationally efficient transfer function based state estimation model for dynamic flows has been successfully used in a hypothesis testing framework for developing an approach for leak detection and identification in gas pipeline networks. Ability of the proposed method to accurately detect and isolate leaks is evaluated using numerical simulations of a real life 204.7 km long series pipeline, and a hypothetical pipeline network. Leak magnitudes considered ranged from 2% to 10% of normal

Acknowledgement

This research work was financially supported by GAIL (India) Ltd. under sponsored project “Development of leak detection methods in gas pipeline networks”.

References (37)

  • J.H. Andersen et al.

    Implicit state-estimation technique for water network monitoring

    Urban Water Journal

    (2000)
  • L. Billmann et al.

    Leak detection methods for pipelines

    Automatica

    (1987)
  • M. Ferrante et al.

    Pipe system diagnosis and leak detection by unsteady state tests. 1. Harmonic analysis

    Advances in Water Resources

    (2003)
  • American Gas Association

    Compressibility factor of natural gas and related hydrocarbon gases, report 8

    (1994)
  • S.B.M. Beck et al.

    Pipeline network features and leak detection by cross correlation analysis of reflected waves

    Journal of Hydraulic Engineering-ASCE

    (2005)
  • A. Benkherouf et al.

    Leak detection and location in gas pipelines

    IEE Proceedings-D

    (1988)
  • B. Brunone et al.

    Detecting leaks in pressurized pipes by means of transients

    Automatica

    (2001)
  • T.J. Chung

    Computational fluid dynamics

    (2002)
  • D. Covas et al.

    Leakage detection in single pipeline using pressure wave behavior

  • J.C. Eidenshink

    Detection of leaks in buried rural water pipelines using thermal infrared images

    Photogrammetric Engineering and Remote Sensing

    (1985)
  • H.E. Emara-Shabaik et al.

    A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation

    Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering

    (2002)
  • R.A. Furness et al.

    Pipeline leak detection techniques

  • G. Geiger et al.

    Leak detection and locating—a survey

  • N. Gopalsami et al.

    Millimeter-wave radar sensing of airborne chemicals

    IEEE Transactions on Microwave Theory and Techniques

    (2001)
  • G. Griebenow et al.

    Leak detection implementations: Modeling and tuning methods

    Journal of Energy Research Technology

    (1989)
  • S.H. Kim

    Extensive development of leak detection algorithm by impulse response method

    Journal of Hydraulic Engineering-ASCE

    (2005)
  • J. Kralik et al.

    Modeling the dynamics of flow in gas pipeline

    IEEE Transactions on Systems, Man, and Cybernetics

    (1984)
  • P.J. Lee et al.

    Frequency domain analysis for detecting pipeline leaks

    Journal of Hydraulic Engineering-ASCE

    (2005)
  • Cited by (0)

    View full text