Optimization of Averaging Window Length and Alarming Hold Time in Volume Balance RTTM Leak Detection to Minimize False Alarms and Spill Volume

2018 ◽  
Author(s):  
Shawn Learn ◽  
Ehsan Shahidi
Keyword(s):  
Detection System ◽  
Hold Time ◽  
Leak Detection ◽  
False Alarms ◽  
Pipeline System ◽  
Window Length ◽  
Post Processing ◽  
Volume Balance ◽  
The Common ◽  
Moving Averaging

Reliability and sensitivity are two main performance metrics of leak detection systems as defined by API 1130 [1]. Proper thresholding scheme is one of the primary factors in having a sensitive and reliable leak detection system with timely detection. In RTTM leak detection, if not dealt with properly, severe pipeline pressure transients can degrade the performance of the leak detection system. One of the common basic methods of reducing the effect of pressure transients is using moving averaging windows; having looser thresholds on the shorter averaging windows, while maintaining tighter thresholds on the longer ones. The thresholds are typically set to meet the API 1149 [2] curve for the pipeline. While the post-processing of filtered data and alarm assessment has been explored via different methods such as sequential probability ratio test, to the authors’ knowledge, there is currently no systematic way of selecting the averaging windows to minimize false alarms prior to the post-processing of the average-filtered data. Moreover, to be able to maintain tight thresholds, especially in shorter averaging windows, one of the common methods is to apply dynamic thresholds, i.e. temporarily expanding thresholds when transients occur. While effective in some scenarios, the main disadvantage of this method is that the imbalance caused by a transient may not clear until the entire averaging window period is passed. This causes either extended periods of degraded performance, or more false positives. This paper utilizes an alarming hold time (also referred to as alarm persistence [3]) to remedy this problem where the averaging window length is reduced while maintaining detection time and sensitivity. To find the optimal set of threshold values, hold times, and averaging window lengths, a Particle Swarm Optimization (PSO) is performed. The ‘fitness function’ of the optimization algorithm is designed to minimize total spill volume for leak scenarios and have minimum false alarms for no-leak scenarios. The former is achieved via setting the objective function to the spill volume and the latter is enforced via applying constraints to the optimization algorithm. For no-leak scenarios, the historical operational data of a pipeline system is used. For leak scenarios, the historical data is modified by introducing a bias in the inlet volume of the section to simulate a leak. The result of the PSO provides a set of alarming parameters, threshold value, averaging window length, alarm hold time, and clearing threshold that provide the minimum false alarm rate and spill volume for different detectability ranges. The optimization method proposed in this paper can be applied to any mass or volume balance-based leak detection system that utilizes moving averaging windows. However, the leak detection parameters found with this method depend on the pipeline system.

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.

scholarly journals Leak Detection Systems for Short Pipelines

2000 ◽  
Author(s):  
Renan Martins Baptista
Keyword(s):  
Detection System ◽  
Leak Detection ◽  
Low Complexity ◽  
Transient Model ◽  
Service Companies ◽  
Detection Systems ◽  
Volume Balance ◽  
Pipeline Networks ◽  
Phase Liquid ◽  
Flow Detection

This paper describes procedures developed by PETROBRAS Research & Development Center to assess a software-based leak detection system (LDS) for short pipelines. These so-called “Low Complexity Pipelines” are short pipeline segments with single-phase liquid flow. Detection solutions offered by service companies are frequently designed for large pipeline networks, with batches and multiple injections and deliveries. Such solutions are sometimes impractical for short pipelines, due to high cost, long tuning procedures, complex instrumentation and substantial computing requirements. The approach outlined here is a corporate approach that optimizes a LDS for shorter lines. The two most popular implemented techniques are the Compensated Volume Balance (CVB), and the Real Time Transient Model (RTTM). The first approach is less accurate, reliable and robust when compared to the second. However, it can be cheaper, simpler, faster to install and very effective, being marginally behind the second one, and very cost-efective. This paper describes a procedure to determine whether one can use a CVB in a short pipeline.

Dynamic Modelling and Real-Time Leak Detection for NGL Pipelines

2004 ◽  
Author(s):  
Brent R. Young ◽  
J. Greg Cooke ◽  
Ron E. Daye ◽  
William Y. Svrcek
Keyword(s):  
Real Time ◽  
Detection System ◽  
Dynamic Modelling ◽  
Leak Detection ◽  
Balance System ◽  
Volume Balance ◽  
Control Scheme ◽  
Dynamic Simulation Model ◽  
Flow Pressure ◽  
Real Time Information

This paper describes the development and use of a dynamic simulation model and the implementation of a novel leak detection system. Experiences from the implementation and operation of the system will also be detailed from a user perspective. The dynamic model may be used for the transient simulation of the pipelines. The model was used to test the real-time leak detection system. The results of the simulation also prompted a change in the control scheme of the pipelines that resulted in less transient operation. The leak detection system is based upon rigorous thermodynamics and dynamic mass balance calculations driven by real-time information from field flow, pressure and temperature sensors. This system was successfully implemented to replace a simple volume balance system for NGL pipelines near Empress, Alberta.

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.

High Speed Data Communications and High Speed Leak Detection Models: Impact of Thermodynamic Properties for Heated Crude Oil in Large Diameter, Insulated Pipelines — Application Pacific Pipeline System

2000 ◽  
Author(s):  
Travis Mecham ◽  
Galen Stanley ◽  
Michael Pelletier ◽  
Jim C. P. Liou
Keyword(s):  
Physical Properties ◽  
Crude Oil ◽  
High Speed ◽  
Detection System ◽  
Large Diameter ◽  
Leak Detection ◽  
San Joaquin Valley ◽  
Pipeline System ◽  
Oil Pipeline ◽  
Detection Model

Recent advances in SCADA and leak detection system technologies lead to higher scan rates and faster model speeds. As these model speeds increase and the inherent mathematical uncertainties in implicit method solutions are reduced, errors and uncertainties in measurement of the physical properties of the fluids transported by pipeline come to dominate the confidence calculations for computer generated leak alerts in the control center. The ability to collect more data must be supported by the need for better model data in order to achieve optimal leak detection system performance. This is particularly true when the products transported are non-homogeneous and have strong viscosity-vs-temperature relationships. These are characteristics of crude oils in California’s San Joaquin Valley where significant heating is required to pump these oils in an efficient manner. Proper characterization and correct mathematical expression of these physical properties in leak models has become critical. This paper presents these new developments in the context of an implementation of this new technology for the Pacific Pipeline System (PPS). PPS is a recently constructed and commissioned 209 km (130-mile), 50.8 cm (20″) diameter, insulated, hot crude oil pipeline between the southern portion of California’s San Joaquin Valley and refineries in the Los Angeles basin. Operational temperatures in this line vary from ambient to 82.2°C (180°F) with pressures ranging from 345 kPa (50 psi) to 11,720 kPa (1700 psi). Due to the unique geometry of the line, facilities along the route include pumping stations, metering stations and numerous “throttle-type” pressure reduction facilities. On PPS, a high-speed leak detection model is supported by a fiber optic (OC-1) communication backbone with data rate capacities in excess of 50 Megabits Per Second (MPS). Total scan times for the distributed communication system have been reduced to 1/4 second — each facility reports data to the SCADA host four times each second. A corresponding 1/4 second leak detection model cycle leads to selection of Methods of Characteristics segments on the order of 260 meters (850 feet). This resolution, in conjunction with the advanced instrumentation package of PPS, makes detection of very small leaks realizable. This paper starts with an overview of the system and combines a mix of the theoretical requirements imposed by the mathematical solutions with a practical description of the laboratory procedures and propagated experimental errors. The paper reviews temperature-related errors and uncertainties and their influence on leak detection performance.

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.

Full Integration of SCADA, Field Control Systems and High Speed Hydraulic Models: Application Pacific Pipeline System

2000 ◽  
Author(s):  
Travis Mecham ◽  
Bruce Wilkerson ◽  
Bryan Templeton
Keyword(s):  
Control System ◽  
Environmental Impact ◽  
Control Systems ◽  
Fiber Optic ◽  
Detection System ◽  
Leak Detection ◽  
Pipeline System ◽  
Scada System ◽  
Fiber Optic Communication ◽  
Optic Communication

Recent advances in PLC, SCADA and leak detection system technologies lead to the development of a highly integrated control system. Interconnected with fiber optic communication speeds (OC-1), this level of integration moves away from the historic model of stand-alone field controllers connected over low speed communication links to a centralized control center which, in turn, exchanges data from the host system to stand-alone leak detection processors. A new system design approach utilized familiar pipeline control elements such as PLC controllers and MODBUS communication protocols in combination with elements more typically associated with an office environment such as Windows NT servers, PC compatible computers, and Ethernet TCP/IP communications networks. These open-architecture components were used to fully develop, debug and test the SCADA system prior to system startup. The pipeline simulator is used as the centerpiece for this process to perform thorough operational validation of the system long before initial linefill. Once the various components were fully tested they were exported to the physical system in an operational state. The result is nearly seamless control systems supported by high data rates, high model speeds, common databases, and multi-channel communications. The increased level of integration has had dramatic impacts in leak detection, system safety, engineering development, operator training, and overall reliability of the control systems. The following paper presents a narrative overview of these new developments in the context of an implementation on Pacific Pipeline System (PPS). PPS is a recently constructed and commissioned 209 km (130 mile), 50.8 cm (20″) diameter, hot crude oil pipeline between the southern portion of California’s San Joaquin Valley and refineries in the Los Angeles basin. Following the Interstate 5 corridor over the “Grapevine”, Tejon Pass, Angeles National Forest and through heavily populated areas, this pipeline traverses some of the most environmentally and safety sensitive regions in the United States. The joint federal and state Environmental Impact Report / Environmental Impact Statement (EIR/EIS) set high hurdles for leak detection and control system performance. The historic control architecture and technologies were not adequate. This paper provides an overview of the environmental and physical constraints of the Pacific Pipeline System alignment, hydraulics, pumping and metering equipment, and block valve locations. It also discusses their impact on the design, programming and commissioning of a SCADA system meeting the requirements of the EIR/EIS. The paper then describes in more detail the fiber-optic communication system, control system architecture, SCADA system, leak detection models, simulator models and implementation methods, along with the engineering decisions leading to a comprehensive solution for the SCADA and leak detection requirements.

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.

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