A Study of Multi-Method Based Subsea Pipeline Leak Detection System

2016 ◽  
Author(s):  
Yanyao Li ◽  
Tianyu Zhang ◽  
Weidong Ruan ◽  
Yong Bai ◽  
Chuntian Zhao
Keyword(s):  
Pattern Recognition ◽  
Detection System ◽  
Leak Detection ◽  
Recognition System ◽  
Economic Losses ◽  
Environmental Damage ◽  
False Alarms ◽  
Ratio Test ◽  
Balance Principle ◽  
Statistical Analysis Method

Pipelines are of most importance to subsea systems. The leakage of pipelines which may be caused by aging or corrosion will lead to serious environmental damage and significant economic losses. In this paper, a submarine pipeline leak detection system is developed to protect environment and also improve the safety of subsea system via quick detection and relatively correct location. The leak detection system includes data acquisition devices, wireless communication devices, the calculation part is also involved, like data processing module, leak detection module, pattern recognition module and positioning module. The corrected flow balance principle and a statistical analysis method, namely Wald’s Sequential Probability Ratio Test (SPRT), are used to decide whether it is leak-free or leak-present. Besides, a pattern recognition system is developed to minimize false alarms. The method of Hydraulic Grade Line was employed to locate the leakage. Our study provides a quick response to leak detection as well as leak location. A quick and convenient method to leak detection and location is provided by this paper.

Ethical dilemmas related to predictions and warnings of impending natural disaster

2013 ◽  
Vol 8 (4) ◽  
pp. 243-252
Author(s):  
Kai-Lit Phua, PhD ◽  
J. W. Hue, BS
Keyword(s):  
Natural Disaster ◽  
Global Justice ◽  
Technical Assistance ◽  
Ethical Issues ◽  
Economic Losses ◽  
Individual Autonomy ◽  
Environmental Damage ◽  
False Alarms ◽  
Policy Makers ◽  
Legal Proceedings

Scientists and policy makers issuing predictions and warnings of impending natural disaster are faced with two major challenges, that is, failure to warn and issuing a false alarm. The consequences of failure to warn can be serious for society overall, for example, significant economic losses, heavy infrastructure and environmental damage, large number of human casualties, and social disruption. Failure to warn can also have serious for specific individuals, for example, legal proceedings against disaster research scientists, as in the L’Aquila earthquake affair. The consequences of false alarms may be less serious. Nevertheless, false alarms may violate the principle of nonmaleficence (do no harm), affect individual autonomy (eg, mandatory evacuations), and may result in the “cry wolf” effect. Other ethical issues associated with natural disasters include the promotion of global justice through international predisaster technical assistance and postdisaster aid. Social justice within a particular country is promoted through greater postdisaster aid allocation to the less privileged.

Pipeline Leak Detection Based on Double Sensor Negative Pressure Wave

2013 ◽  
Vol 313-314 ◽  
pp. 1225-1228 ◽  
Author(s):  
Chun Xia Hou ◽  
Er Hua Zhang
Keyword(s):  
False Alarm ◽  
Negative Pressure ◽  
Pressure Wave ◽  
Detection System ◽  
Low Frequency ◽  
Leak Detection ◽  
Frequency Component ◽  
Economic Losses ◽  
Single Sensor ◽  
Negative Pressure Wave

Pipeline leak lead to huge economic losses and environmental pollution. Leak detection system based on single sensor negative pressure wave often causes false alarm. In this paper the double sensor method is adopted to exclude false alarm by determining the propagation direction of the pressure wave. In order to remove the inverse coherent interference caused by pump running, the phase difference of primary low frequency component is used to identify the sign of the time delay between the double sensors. The experiment shows the mothod is effective.

Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition

2016 ◽  
Author(s):  
Martin Di Blasi ◽  
Zhan Li
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Task Force ◽  
Detection System ◽  
Leak Detection ◽  
Guidance Document ◽  
Performance Expectations ◽  
Detection Systems ◽  
Short Period ◽  
Recognition And Response

Pipeline ruptures have the potential to cause significant economic and environmental impact in a short period of time, therefore it is critical for pipeline operators to be able to promptly detect and respond to them. Public stakeholder expectations are high and an evolving expectation is that the response to such events be automated by initiating an automatic pipeline shutdown upon receipt of rupture alarm. These types of performance expectations are challenging to achieve with conventional, model-based, leak-detection systems (i.e. CPM–RTTMs) as the reliability measured in terms of the false alarm rate is typically too low. The company has actively participated on a pipeline-industry task force chaired by the API Cybernetics committee, focused on the development of best practices in the area of Rupture Recognition and Response. After API’s release of the first version of a Rupture Recognition and Response guidance document in 2014, the company has initiated development of its own internal Rupture Recognition Program (RRP). The RRP considers several rupture recognition approaches simultaneously, ranging from improvements to existing CPM leak detection to the development of new SCADA based rupture detection system (RDS). This paper will provide an overview of a specific approach to rupture detection based on the use of machine learning and pattern recognition techniques applied to SCADA data.

High Sensitivity External Leak Detection for Liquid Fuel Pipelines

2004 ◽  
Author(s):  
David G. Parman ◽  
Ken McCoy
Keyword(s):  
Liquid Fuel ◽  
Risk Mitigation ◽  
Detection System ◽  
High Sensitivity ◽  
Leak Detection ◽  
Liquid Fuels ◽  
Detection Methods ◽  
Environmental Damage ◽  
Distributed Sensor ◽  
Environmentally Sensitive Areas

Pipeline risk mitigation in high consequence areas can be facilitated through the use of a high sensitivity external leak detection (HSELD) system. Such systems have been implemented for both off-site and on-site pipeline applications, including the Longhorn Pipeline (Texas) and the Madrid Barajas International Airport (Spain). We define high-sensitivity external leak detection as a leak detection system that will continuously and automatically detect very small amounts of liquid fuels and is physically independent of pipeline pumping operations. In addition, such systems monitor their own integrity on a continuous basis, without requiring periodic recalibration or operator interaction. The HSELD system we describe incorporates a distributed sensor cable, installed in a slotted PVC conduit which is run in close proximity to the pipeline. Many pipeline leaks start out as very small cracks or holes resulting from corrosion and wear. In their initial stages, such leaks go undetected by standard leak detection methods, but over time large volumes of liquid fuel may leak into the environment. In high consequence areas, such as above aquifers and other environmentally sensitive areas, the leak may go undetected until traces show up in water samples. The critical characteristic of an effective HSELD is its ability to detect and accurately locate very small volumes of liquid fuels, so that these small leaks can be identified, cleaned up and repaired before environmental damage is done.

Leak Detection in a Pipeline Using Modified Line Volume Balance and Sequential Probability Tests

2006 ◽  
Author(s):  
Marti´n Di Blasi ◽  
Carlos Muravchik
Keyword(s):  
Dynamic Range ◽  
Detection System ◽  
Real Data ◽  
Leak Detection ◽  
Normal Operation ◽  
Ratio Test ◽  
Oil Pipeline ◽  
Probability Ratio ◽  
Volume Balance ◽  
Sequential Probability

The use of statistical tools to improve the decision aspect of leak detection is becoming a common practice in the area of computer pipeline monitoring. Among these tools, the sequential probability ratio test is one of the most named techniques used by commercial leak detection systems [1]. This decision mechanism is based on the comparison of the estimated probabilities of leak or no leak observed from the pipeline data. This paper proposes a leak detection system that uses a simplified statistical model for the pipeline operation, allowing a simple implementation in the pipeline control system [2]. Applying linear regression to volume balance and average pipeline pressure signals, a statistically corrected volume balance signal with reduced variance is introduced. Its expected value is zero during normal operation whereas it equals the leak flow under a leak condition. Based on the corrected volume balance, differently configured sequential probability ratio tests (SPRT) to extend the dynamic range of detectable leak flow are presented. Simplified mathematical expressions are obtained for several system performance indices, such as spilled volume until detection, time to leak detection, minimum leak flow detected, etc. Theoretical results are compared with leak simulations on a real oil pipeline. A description of the system tested over a 500 km oil pipeline is included, showing some real data results.

Pipeline Rupture Detection Using Real-Time Transient Modelling and Convolutional Neural Networks

2018 ◽  
Author(s):  
Joel Smith ◽  
Jaehee Chae ◽  
Shawn Learn ◽  
Ron Hugo ◽  
Simon Park
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Detection System ◽  
High Reliability ◽  
Leak Detection ◽  
Sudden Onset ◽  
False Alarms ◽  
Pipeline System ◽  
Transient Model ◽  
Social License

Demonstrating the ability to reliably detect pipeline ruptures is critical for pipeline operators as they seek to maintain the social license necessary to construct and upgrade their pipeline systems. Current leak detection systems range from very simple mass balances to highly complex models with real-time simulation and advanced statistical processing with the goal of detecting small leaks around 1% of the nominal flow rate. No matter how finely-tuned these systems are, however, they are invariably affected by noise and uncertainties in a pipeline system, resulting in false alarms that reduce system confidence. This study aims to develop a leak detection system that can detect leaks with high reliability by focusing on sudden-onset leaks of various sizes (ruptures), as opposed to slow leaks that develop over time. The expected outcome is that not only will pipeline operators avoid the costs associated with false-alarm shut downs, but more importantly, they will be able to respond faster and more confidently in the event of an actual rupture. To accomplish these goals, leaks of various sizes are simulated using a real-time transient model based on the method of characteristics. A novel leak detection model is presented that fuses together several different preprocessing techniques, including convolution neural networks. This leak detection system is expected to increase operator confidence in leak alarms, when they occur, and therefore decrease the amount of time between leak detection and pipeline shutdown.

Near Real-Time Automated Detection of Small Hazardous Liquid Pipeline Leaks Using Remote Optical Sensing and Machine Learning

2016 ◽  
Author(s):  
Maria S. Araujo ◽  
Shane P. Siebenaler ◽  
Edmond M. Dupont ◽  
Samantha G. Blaisdell ◽  
Daniel S. Davila
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Real Time ◽  
Optical Sensors ◽  
Environmental Conditions ◽  
Detection System ◽  
Leak Detection ◽  
False Alarms ◽  
Different Types ◽  
Pump Stations

The prevailing leak detection systems used today on hazardous liquid pipelines (computational pipeline monitoring) do not have the required sensitivities to detect small leaks smaller than 1% of the nominal flow rate. False alarms of any leak detection system are a major industry concern, as such events will eventually lead to alarms being ignored, rendering the leak detection system ineffective [1]. This paper discusses the recent work focused on the development of an innovative remote sensing technology that is capable of reliably and automatically detecting small hazardous liquid leaks in near real-time. The technology is suitable for airborne applications, including manned and unmanned aircraft, ground applications, as well as stationary applications, such as monitoring of pipeline pump stations. While the focus of the development was primarily for detecting liquid hydrocarbon leaks, the technology also shows promise for detecting gas leaks. The technology fuses inputs from various types of optical sensors and applies machine learning techniques to reliably detect “fingerprints” of small hazardous liquid leaks. The optical sensors used include long-wave infrared, short-wave infrared, hyperspectral, and visual cameras. The utilization of these different imaging approaches raises the possibility for detecting spilled product from a past event even if the leak is not actively progressing. In order to thoroughly characterize leaks, tests were performed by imaging a variety of different types of hazardous liquid constitutions (e.g. crude oil, refined products, crude oil mixed with a variety of common refined products, etc.) in several different environmental conditions (e.g., lighting, temperature, etc.) and on various surfaces (e.g., grass, pavement, gravel, etc.). Tests were also conducted to characterize non-leak events. Focus was given to highly reflective and highly absorbent materials/conditions that are typically found near pipelines. Techniques were developed to extract a variety of features across the several spectral bands to identify unique attributes of different types of hazardous liquid constitutions and environmental conditions as well as non-leak events. The characterization of non-leak events is crucial in significantly reducing false alarm rates. Classifiers were then trained to detect small leaks and reject non-leak events (false alarms), followed by system performance testing. The trial results of this work are discussed in this paper.

Real Time Statistical Leak Detection on the RRP Crude Oil Network, Holland

2002 ◽  
Author(s):  
Joep Hoeijmakers ◽  
John Lewis
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Field Application ◽  
Normal Operation ◽  
False Alarms ◽  
Pipeline Network ◽  
Oil Pipeline ◽  
Detection Systems

Prior to the year 2000, the RRP crude oil pipeline network in Holland and Germany was monitored using a dynamic leak detection system based on a dynamic model. The system produced some false alarms during normal operation; prompting RRP to investigate what advances had been made in the leak detection field before committing to upgrade the existing system for Y2K compliance. RRP studied the available leak detection systems and decided to install a statistics-based system. This paper examines the field application of the statistics based leak detection system on the three crude oil pipelines operated by RRP. They are the 177 km Dutch line, the 103 km South line, and the 86 km North line. The results of actual field leak trials are reported. Leak detection systems should maintain high sensitivity with the minimum of false alarms over the long term; thus this paper also outlines the performance of the statistical leak detection system over the last year from the User’s perspective. The leak detection experiences documented on this crude oil pipeline network demonstrate that it is possible to have a reliable real-time leak detection system with minimal maintenance costs and without the costs and inconvenience of false alarms.

scholarly journals Detecting Pipeline Leaks

2017 ◽  
Vol 139 (11) ◽  
pp. 34-39
Author(s):  
Vicki Niesen ◽  
Melissa Gould
Keyword(s):  
Detection System ◽  
Leak Detection ◽  
American Petroleum Institute ◽  
False Alarms ◽  
Case Scenario ◽  
Detection Program ◽  
Computer Simulators ◽  
Improved Technology ◽  
The Impact ◽  
Do So

This article explores technological advancements for detecting pipeline leaks. An ideal leak detection system should not only quickly detect both small and large leaks, but also do so reliably and not trigger false alarms. Operations in gas pipelines can differ quite a bit from those for liquids, so the experience gained in one type of line may not be entirely applicable when changing jobs. Fortunately, computer simulators are increasingly sophisticated, enabling operators to become comfortable handling a variety of situations. In December 2015, the American Petroleum Institute released a set of guidelines (RP 1175), written by a representative group of hazardous liquid pipeline operators, that established a framework for leak detection management. The focus of the guidelines is getting pipeline operators to use a risk-based approach in their leak detection program, with the goal of uncovering leaks quickly and with certainty. The best-case scenario is for leaks to not occur at all, and the industry is making great strides to keep them from happening. The combination of improved technology and risk-based management should enable operators to keep leaks small and contained, and reduce the impact on the environment as much as possible.

An Automated Leak Detection and Location System in a Brazilian Liquid Ammonia Pipeline

2006 ◽  
Author(s):  
Emir Ceciliato ◽  
Claudia C. Magalha˜es ◽  
David G. P. Bueno
Keyword(s):  
Liquid Ammonia ◽  
Detection System ◽  
Main Idea ◽  
Ammonium Hydroxide ◽  
Leak Detection ◽  
Field Trials ◽  
Raw Material ◽  
Ratio Test ◽  
Human Intervention ◽  
Initial Field

Liquid ammonia is the basis of a variety of products, ranging from fertilisers, important intermediate chemical raw material (such as nitric acid and ammonium hydroxide), cosmetics, up to explosives. It is a very toxic product, requiring special care when transported. This paper shall present an automated leak detection system (i. e., without human intervention for shutting pumps down) for a short (approximately 6 kilometres long), 6 inch., liquid ammonia pipeline located at the city of Cubata˜o, state of Sa˜o Paulo, Brazil, within the bounds of a fertiliser producer. The pipeline runs from a Terminal (which is a port) down to a fertiliser complex, where the ammonia is used as raw material to produce ammonium nitrate. There is approximately 3.5 km of buried pipes, as well as 2.5 km of aerial ones. A special set of insulation layers is coating pipeline segments in order to keep ammonia in an undercooled temperature (and showing an undercooled liquid behaviour, as a consequence). The selected leak detection system is based on statistical concepts, using the SPRT (Sequential Probability Ratio Test) sampling technology. The main idea is to establish an automated procedure in which the LDS is capable of sending an alarm signal directly to the SCADA, to shut down pumps, without human intervention. The system architecture is covered, as well as the details on the pipeline hydraulics and ammonia transport properties (especially density and viscosity). Finally, initial field trials data are provided and analysed.

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