Computational Pipeline Monitoring in Challenging Situations

2002 ◽  
Author(s):  
James E. Short
Keyword(s):  
Real Time ◽  
Detection System ◽  
Measurement Data ◽  
Leak Detection ◽  
Data Uncertainty ◽  
Computational Pipeline ◽  
Individual Measurement ◽  
General Terms ◽  
Pipeline Monitoring

This paper introduces a new, active methodology to modeling and leak detection intended to mitigate the effects of data uncertainty in such challenging situations, and presents three case studies. The American Petroleum Institute (API) has coined the phrase Computational Pipeline Monitoring (CPM) to encompass several methods of leak detection. The use of real-time transient hydraulic simulation tools, driven by data gathered by a Supervisory Control and Data Acquisition (SCADA) system, is one form of CPM system. Such real-time simulations impose SCADA-gathered data (typically pressures, flows, temperatures) onto a characterization of the pipeline (the model) and the fluids in the system. In a tuned CPM system, if the SCADA-gathered data cannot be successfully imposed on the model without transgressing the laws of fluid mechanics, this signifies a pipeline anomaly, which may be a release. However, in reality, many pipeline hydraulic anomalies are due to changing uncertainties in the data presented to the model and if annunciated to the pipeline operators would constitute a “false leak alarm.” While they typically are not large enough to compromise pipeline operations, uncertainties abound in the SCADA-gathered data. Even were the SCADA-gathered pressure and temperature data to contain no uncertainty, the fluid properties might not be sufficiently characterized for the simulation to accurately calculate how the fluid behaves under pressure and/or temperature changes. Measurement failure further complicates the task of the CPM application, as does slack line flow. Uncertainty in the CPM-driving data is not constant, it is ever-changing with variations in the pipeline flow rate, the characterization of the fluids in the line, and the quality of the individual measurement data, to mention only a few. CPM systems use a variety of methodologies to vary their sensitivity according to the uncertainty in the data used for their calculations. However, in general terms, the more uncertainty there is in the data, the lower the resulting system sensitivity becomes. Active features in a CPM leak detection system can mitigate the performance degradation due to varying data uncertainty.

scholarly journals Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: influence of local factors

2014 ◽  
Vol 18 (11) ◽  
pp. 4467-4484 ◽  
Author(s):  
B. Revilla-Romero ◽  
J. Thielen ◽  
P. Salamon ◽  
T. De Groeve ◽  
G. R. Brakenridge
Keyword(s):  
Real Time ◽  
River Discharge ◽  
Detection System ◽  
Poor Performance ◽  
Model Verification ◽  
Area Index ◽  
General Terms ◽  
Skill Scores ◽  
Flood Detection ◽  
Discharge Measurements

Abstract. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.

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.

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.

The Development and Design of a Laser Detection System for Tire Thickness Measurement

2013 ◽  
Vol 418 ◽  
pp. 128-131
Author(s):  
King Sun Lee
Keyword(s):  
Standard Deviation ◽  
Real Time ◽  
Detection System ◽  
Measurement Data ◽  
Thickness Measurement ◽  
Inspection System ◽  
Laser Sensors ◽  
Repeatability Error ◽  
On Line ◽  
Measuring Machine

This system is a self-developed real-time thickness inspection system including high-precision laser sensors and a mobile platform for on-line detection of tire rubber skin. The measurement data is used to calculate the standard deviation and process capability indices, and to evaluate measurement capacity. The system is a real-time measurement system in which the obtained measuring data compare with the standard value and show any errors. A technician can adjust the process parameters precisely on-line to improve product quality. The standard deviation of repeatability of the system for height is within +/- 0.0081 mm. The repeatability error of the horizontal sliding rail is within 0.0145mm, while the measurement error between this system and a coordinated measuring machine is within 0.028mm.

Modeling of Ethylene in Pipeline Leak Detection System

2004 ◽  
Author(s):  
Jakob Bu¨chert
Keyword(s):  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Practical Experience ◽  
Time Model ◽  
Pump Stations ◽  
Pipeline Model ◽  
Improve Determination

This paper describes experiences with an improved equation of state (EOS) for ethylene for an existing real time pipeline model. The main scope of the model is leak detection, batch, contaminant and pig tracking. Altogether the pipeline model includes transportation of batched liquid ethylene, ethane, propane, butane and natural gas liquids (NGL). The pipeline is approximately 1900 miles miles long and includes laterals, 33 pump stations, 9 injection/delivery stations and 5 propane terminals. Originally the model used a BWRS EOS for all the above products. At that time a number of false leak alarms were experienced related to pipeline sections containing ethylene. A case study was carried out, specifically for ethylene, to investigate the effect of replacing the BWRS EOS with a modified Helmholtz EOS. The study showed that replacing the EOS on average would improve determination of the ethylene densities by 1.6%–5.6% with an expected reduction in the alarm rate for ethylene cases by approximately 50%. As a result the modified Helmholtz EOS was implemented in the real time model. Results are presented to show the practical experience with the new EOS gained over the last years.

Remote and Intelligent Diagnostic Methods on Leakage Fault for Crude Oil Pipelines

2006 ◽  
Author(s):  
Lai-Bin Zhang ◽  
Zhao-Hui Wang ◽  
Wei Liang
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Data Exchange ◽  
Detection System ◽  
Leak Detection ◽  
Diagnostic Methods ◽  
Oil Fields ◽  
Detection Methods ◽  
Development Environment ◽  
Oil Pipelines

Oil and gas transportation pipelines are the key equipment in petroleum and chemical industry. At present, with the increase of transportation task in oil fields, real-time leak detection system becomes a demand that petroleum companies need to safeguard routines. At the heart of the leakage monitoring and detection procedures are the report of leakage event timely and of leakage point precisely. This paper presents a more realistic approach for using rarefaction-pressure wave technique in oil pipelines, which aims to two targets, one is the improvement of remote and intelligent degree, and the other is the improvement of the leakage location ability. This paper introduces a new scheme to meet the requirements of real time and high data transferring necessary for remote monitoring and leak detection methods for pipelines. The scheme is based on SCADA framework for remote pipeline leakage diagnosis, in which the Dynamic Data Exchange technology is utilized to construct the data-acquiring component to acquire the real-time information that could perform remote test and analysis. It also introduces a basic concept and structure of the remote leak detection system. Primarily, an embedded leak-detection package is designed to exchange the diagnostic information with the RTU data package of Modbus protocol, and then via fiber network, the SCADA-based remote monitoring and leak detection system is realized. Existing data acquisition apparatus applied in oil fields and city underground water pipeline is used, without changing the structure of pipeline supervisory system. This paper introduces the method of constructing DDE-based hot links between servers and client terminals, using Borland C++ Builder 6.0 development environment, and also explains the universality and friendliness of the method. It can easily access similar Windows’ applications simply by modifying Service names, Topic options and data Items. System feasibility was tested using negative-pressure data from oil-fields. Additionally, the applied results show that the whole running status of pipeline can be monitored effectively, and a higher automation grade and an excellent leak location precision of the system can be obtained.

Reality Check: Improving Real-Time Pipeline Monitoring Using Near Real-Time Fluid Data

2008 ◽  
Author(s):  
Joseph Jutras ◽  
Rick Barlow
Keyword(s):  
Real Time ◽  
Detection System ◽  
Leak Detection ◽  
Fluid Type ◽  
Transient Model ◽  
Oracle Database ◽  
Detection Model ◽  
The Past ◽  
Batch Data ◽  
Time Injection

MBS, the software based leak detection system employed by Enbridge, is a real time transient model and as such requires fluid characteristics of the various batches that enter the pipeline. In the past, of the 25 plus pipelines modeled, only 4 received fluid identifiers from the field. These fluid identifiers are a sub-string of the batch identifiers stored in flow computers located at custody transfer locations. On the remaining pipelines, Enbridge used fluid density from the field to infer fluid type and therefore characteristics. In the past whenever a number of fluids had the same density, MBS assigned a best-guess of fluid type. The ‘MBS Real Time Injection Batch Data’ project was proposed to bring fluid identifiers to MBS on the remaining lines with the purpose of improving MBS’ selection of fluid properties. Since injection points on the remaining lines were not custody transfer there were no flow computers at these locations. An existing application called Commodity Movement Tracking, or CMT, was used to provide fluid names to the leak detection model. CMT holds past, present, and future injection batch information in an Oracle database. Batch identifiers are queried, placed into the SCADA system, and forwarded on to MBS. This paper explores the new approach, introduced by the ‘MBS Real Time Injection Batch Data’ project, of providing MBS with batch identifiers.

Dual Threshold Analysis and its Tuning Tool Development for Leak Detection

2014 ◽  
Author(s):  
Jianping Gao ◽  
Mike Chen
Keyword(s):  
Steady State ◽  
Real Time ◽  
Design Process ◽  
Detection System ◽  
Leak Detection ◽  
Tool Development ◽  
Operating Period ◽  
Data Set ◽  
Threshold Setting ◽  
Threshold Tuning

Dual threshold setting in CPM (Computational Pipeline Monitoring) systems is a concept to apply two kind of thresholds, namely steady state threshold and transient threshold setting to improve sensitivity during steady state operating period and reduce false alarm rate during transient operating period. Dual threshold implementation in CPM systems is not a trivial task since a real time pipeline may go through very complicated hydraulic scenarios. During design phase of dual threshold, the data set evaluated needs to cover on operational scenario long enough to represent the typical operation of the pipeline. The design process needs to include the design of transient/steady state switching, transient and steady state threshold, waiting time etc and tuning of those design parameter to achieve the optima. This calls for effective analysis to ensure its validity and a tuning tool development with user-friendly graphical user interface (GUI) to facilitate the tuning efforts from leak detection engineers. This paper details the process from dual threshold design and analysis to tuning tool development and application of the tool in real time CPM systems. At first, the concept of dual threshold and its design process being employed in CPM systems are reviewed; Secondly the paper discusses an analysis approach in testing and evaluation of dual threshold design in current CPM systems with identification of room for improvement. Next, the paper elaborates the design and development of a dual threshold tuning tool to simplify the process with intensive application of the tool during the threshold tuning of real time CPM systems to improve the detectability of the current leak detection system, finally concludes with some closing remarks.

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.

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