A Review of Leak Detection Systems for Natural Gas Pipelines and Facilities

Pipelines facilities, used for the transportation of natural gas in large quantities to homes and industries, remain the best economic, most reliable and safest mode of transport of energy. Despite these numerous advantages, gas pipelines have been enmeshed in various accidents and thefts, nonetheless this could be reduced if properly maintained and pipelines can last indefinitely without leaks. Pipelines are susceptible to leakages and rupture accidents as a result of age, corrosion, material defects, operational errors or other reasons. Pipeline failures may be caused intentionally (e.g. vandalism) or unintentionally (e.g. device/material failure and corrosion), which may result into irreversible damages such as financial losses, human casualties, ecological disaster and extreme environmental pollution. Leakages in natural gas facilities and installations require three vital aspects, namely: Gas Leakage Prevention, Gas Leakage Detection and Gas Leakage Mitigation. Many Gas Leak Detection methods are used for pipeline integrity management and especially for minimizing gas leakage. The performance of these methods depends on the approaches, operational conditions and pipeline networks. Also, there are some essential requirements and guidelines which must be met before we can consider any leak detection system suitable for production solutions, including sensitivity, reliability, accuracy and robustness. The attempt of this study is to carry out a critical review of these models, to ascertain the best model(s) applicable to natural gas leak detection. Keywords: Gas Leak Detection System, Leak Location, Leak Size DOI: 10.7176/JETP/13-2-02 Publication date: April 30th 202

Current technologies and the applications of data analytics for crude oil leak detection in surface pipelines

Pipeline pressure monitoring has been the traditional and most popular leak detection approach, however, the delays with leak detection and localization coupled with the large number of false alarms led to the development of other sensor-based detection technologies. The Real Time Transient Model (RTTM) currently has the best performance metric, but it requires collection and analysis of large data volume which, in turn, has an impact in the detection speed. Several data mining (DM) methods have been used for leak detection algorithm development with each having its own advantages and shortcomings. Mathematical modelling is used for the generation of simulation data and this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the ANN and SVM require a large training dataset for development of accurate models, mathematical modelling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modelling for the development of a robust real time leak detection and localization system for surface pipelines. Several case studies are also presented

TDR-based Multiple Leak Detection System using an S-parameter Transmission Line Model for Long-Distance Pipelines

Leaks in water distribution systems should be detected to avoid economic, environmental, and social problems. Existing Bayesian Inference based time-domainreflectometry (TDR) methods for leak detection have a limitation for real applications due to the lengthy time in building sample data. As the pipeline distance becomes longer and multiple leaks must be considered in long distance pipelines, the computational time for building training data gets larger. This paper proposes a scattering-parameter-based forward model to relieve computational burden of the existing TDR methods. It was shown that the proposed model outperformed the existing RLGC-based forward model in terms of computational time. The proposed model that is combined with Bayesian inference and TDR signal modeling is validated with an experimental pipeline, leak detectors, transmission line, and TDR instrument for leak detection. In summary, the proposed method is promising for leak detection in long pipelines as well as multiple leaks

Design and Evaluation of an In-Pipe Leak Detection Sensing Technique Based on Force Transduction

Leakage is the major factor for unaccounted fluid losses in almost every pipe network. In most cases the deleterious effects associated with the occurrence of leaks may present serious economical and health problems and therefore, leaks must be quickly detected, located and repaired. The problem of leakage becomes even more serious when it is concerned with the vital supply of fresh water to the community. Leaking water pipelines can develop large health threats to people mostly because of the infiltration of contaminants into the water network. Such possibilities of environmental health disasters have spurred research into the development of methods for pipeline leakage detection. Most state of the art leak detection techniques have limited applicability, while some of them are not reliable enough and sometimes depend on user experience. Our goal in this work is to design and develop a reliable leak detection sensing system. The proposed technology utilizes the highly localized pressure gradient in the vicinity of a small opening due to leakage in a pressurized pipeline. In this paper we study this local phenomenon in detail and try to understand it with the help of numerical simulations in leaking pipelines (CFD studies). Finally a new system for leak detection is presented. The proposed system is designed in order to reduce the number of sensing elements required for detection. The main concept and detailed design are laid out. A prototype is fabricated and presented as a proof of concept. The prototype is tested in a simple experimental setup with artificial leakages for experimental evaluation. The sensing technique discussed in this work can be deployed in water, oil and gas pipelines without significant changes in the design, since the concepts remain the same in all cases.King Fahd University of Petroleum and Minerals (Project Number R7-DMN-08

Location of leaks in pipelines using parameter identification tools

This work proposes an approach to locate leaks by identifying the parameters of finite models associated with these fault events. The identification problem is attacked by using well-known identification methods such as the Prediction Error Method and extended Kalman filters. In addition, a frequency evaluation is realized to check the conditions for implementing any method which require an excitation condition.Comment: This paper has some error

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

A Modelling Study for Smart Pigging Technique for Pipeline Leak Detection

Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion BOE through 2035. In addition, increasing number and length of subsea tiebacks face new challenges in term of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how to create a framework for a smart pigging technique for pipeline leak detection, as an active leak detection method. Numerical modeling of smart pigging for leak detection requires two crucial components: detailed mathematical descriptions for fluid-solid and solid-solid interactions around pig, and network modeling for the calculation of pressure and rate along the pipeline using iterative algorithms. The first step of this study is to build a numerical model that shows the motion of a pig along the pipeline with no leak, i.e., at a given injection rate, a pig first accelerates until it reaches its terminal velocity, beyond which the pig moves at a constant velocity. The second step is to construct a network model that consists of two pipeline segments (one upstream and the other downstream of leak location) through which the pig travels and at the junction of which fluid leak occurs. By putting these multiple mechanisms together and using resulting pressure signatures, this study presents a new method to predict the location and size of a leak present in pipeline

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Vibration-Based Discriminant Analysis for Pipeline Leaks Detection

Pipelines are useful for transporting liquids from one place to another. The main problem that often occurs in pipelines is leakage which results in production and financial losses. The importance of detecting pipeline leaks makes the industries look for effective detection methods to avoid bigger losses. Several previous studies have proven that the vibration-based method is successful in detecting leaks in pipelines. However, the vibration-based method used in the previous study is relatively complicated and requires specialists to interpret the results. This study proposes a machine learning-based detection method that can classify pipe conditions directly without the help of a specialist. The proposed method is vibration-based discriminant analysis; a machine learning algorithm that recognizes pipeline conditions from their vibration pattern instead of spectrum. The proposed method was tested on a test rig consisting of a closed-loop pipeline equipped with a leak-pipe test segment. The vibration signal is taken using an accelerometer placed on the leak-pipe test segment. Time domain vibration data is extracted using several statistical parameters which aims to reveal information related to pipe conditions. The vibration data collected is divided into two groups, namely training-data and testing-data. The discriminant analysis model is trained to recognize the vibration pattern of the pipeline using training-data and then tested using testing-data. There are four leak sizes introduced in this study, small, medium, and large. Meanwhile, normal condition (no leaks) is used as benchmarking. The study shows that the proposed method is effective in classifying four pipe conditions with the accuracy up to 95%