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 The challenges and solution of implementing a leak detection system on a complex crude oil pipeline network in Romania

2021 ◽  
Vol 7 (1) ◽  
pp. 60-73
Author(s):  
Harry SMITH ◽  
Kirsty MCNEIL ◽  
Tom RECORD ◽  
Dan BUZATU ◽  
Georgian ILIESCU ◽  
...  
Keyword(s):  
Crude Oil ◽  
Detection System ◽  
Leak Detection ◽  
Pipeline Network ◽  
Oil Pipeline

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.

Commissioning a Real-Time Leak Detection System on a Large Scale Crude Oil Pipeline During Start Up

2006 ◽  
Author(s):  
Joanna Mabe ◽  
Keefe Murphy ◽  
Gareth Williams ◽  
Andrew Welsh
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Leak Detection ◽  
Oil Pipeline ◽  
Start Up ◽  
Environmental Requirements ◽  
The Moment ◽  
Operational Data

This paper describes the process of incremental pipeline filling and the phased commissioning of a real-time leak detection system for the 1768 km long BTC crude oil pipeline. Due to stringent environmental requirements, it is essential for the leak detection system to work from the moment that crude oil is introduced into the pipeline. Without any prior operational data and with the pipeline partially filled, it is challenging for the leak detection system to monitor the integrity of the pipeline throughout the whole filling process. The application of the pig tracking software to track the oil front as the crude displaces nitrogen is also discussed.

Transient Leak Detection in Crude Oil Pipelines

2004 ◽  
Author(s):  
Rainer Beushausen ◽  
Stefan Tornow ◽  
Harald Borchers ◽  
Keefe Murphy ◽  
Jun Zhang
Keyword(s):  
Steady State ◽  
Crude Oil ◽  
Pressure Wave ◽  
Detection System ◽  
Leak Detection ◽  
Statistical System ◽  
Transient Conditions ◽  
Oil Pipeline ◽  
Detection Systems ◽  
Oil Pipelines

This paper addresses the specific issues of transient leak detection in crude oil pipelines. When a leak occurs immediately after pumps are switched on or off, the pressure wave generated by the transients dominates the pressure wave that results from the leak. Traditional methods have failed to detect such leaks. Over the years, NWO has developed and implemented various leak detection systems both in-house and by commercial vendors. These systems work effectively under steady-state conditions but they are not able to detect leaks during transients. As it is likely for a leak to develop during transients, NWO has decided to have the ATMOS Pipe statistical leak detection system installed on their pipelines. This paper describes the application of this statistical system to two crude oil pipeline systems. After addressing the main difficulties of transient leaks, the field results will be presented for both steady-state and transient conditions.

Pipeline Diagnostics With Ultrasonic Meters

2016 ◽  
Author(s):  
Nicole Gailey ◽  
Noman Rasool
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Flow Measurement ◽  
Detection System ◽  
Leak Detection ◽  
The United States ◽  
Time Data ◽  
Critical System ◽  
Detection Systems ◽  
Transient Models

Canada and the United States have vast energy resources, supported by thousands of kilometers (miles) of pipeline infrastructure built and maintained each year. Whether the pipeline runs through remote territory or passing through local city centers, keeping commodities flowing safely is a critical part of day-to-day operation for any pipeline. Real-time leak detection systems have become a critical system that companies require in order to provide safe operations, protection of the environment and compliance with regulations. The function of a leak detection system is the ability to identify and confirm a leak event in a timely and precise manner. Flow measurement devices are a critical input into many leak detection systems and in order to ensure flow measurement accuracy, custody transfer grade liquid ultrasonic meters (as defined in API MPMS chapter 5.8) can be utilized to provide superior accuracy, performance and diagnostics. This paper presents a sample of real-time data collected from a field install base of over 245 custody transfer grade liquid ultrasonic meters currently being utilized in pipeline leak detection applications. The data helps to identify upstream instrumentation anomalies and illustrate the abilities of the utilization of diagnostics within the liquid ultrasonic meters to further improve current leak detection real time transient models (RTTM) and pipeline operational procedures. The paper discusses considerations addressed while evaluating data and understanding the importance of accuracy within the metering equipment utilized. It also elaborates on significant benefits associated with the utilization of the ultrasonic meter’s capabilities and the importance of diagnosing other pipeline issues and uncertainties outside of measurement errors.

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.

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.

Integrity Monitoring: Not Just Leak Detection

2000 ◽  
Author(s):  
Kevin Hagar ◽  
Bruce Young ◽  
Ross Mactaggart
Keyword(s):  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Time Model ◽  
Integrity Monitoring ◽  
Detection Systems ◽  
Line Flow ◽  
Instrument Analysis ◽  
Flow Detection

There are many uses for a software based, real time leak detection system other than just leak detection. Leak detection systems, based on a real time model, have many uses, including instrument analysis, slack line flow detection, batch, scraper and DRA tracking. Hydraulic profiles, trends and Imbalance Signature Plots provide operational tools to augment SCADA displays. When abnormal imbalance is reported, the operator has tools to pin point the problem. These results can also be made available enterprise-wide for use by higher level processes such as accounting, scheduling, planning, and marketing.

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

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Martín 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 process within 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 (Zhang and Di Mauro, 1998, “Implementing a Reliable Leak Detection System on a Crude Oil Pipeline,” Advances in Pipeline Technology, Dubai, UAE). 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 (Di Blasi, M., 2004, “Detección y localización de fugas en sistemas de transporte de fluidos incompresibles,” MS thesis, Universidad Nacional de La Plata, Buenos Aires, Argentina). Applying real-time recursive linear regression to volume balance and average pipeline pressure signals, a statistically corrected volume balance signal with reduced variance is derived. Its average 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 are presented to extend the dynamic range of detectable leak flow. 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.

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