MAOP Reconfirmation for a 20 Inch Gas Pipeline Using the ECA Method and Enhanced ILI

2020 ◽  
Author(s):  
Simon Slater ◽  
Chris Davies ◽  
Ollie Burkinshaw ◽  
Peter Clyde ◽  
John Griffen
Keyword(s):  
Material Properties ◽  
Metal Loss ◽  
Critical Element ◽  
Critical Aspect ◽  
Safety Regulations ◽  
Reduce Risk ◽  
Verification Process ◽  
Property Verification ◽  
Pipeline Safety ◽  
Size Target

Abstract In light of the revised Federal Pipeline Safety Regulations, active from July 1, 2020, operators of gas transmission pipelines are faced with the task of reconfirming pipeline MAOP in a prescriptive set of circumstances. This requirement is defined in section 192.624 of 49 CFR 192. Louisville Gas & Electric (LGE) is operating a pipeline with an MAOP established using a combination of partial and full traceable, verifiable and complete (TVC) documentation and the ‘grandfathering’ clause defined by 192.619(a)(3). LGE has developed a plan and embarked on the process of reconfirming the MAOP using Method 3 – Engineering Critical Assessment (ECA). The pipeline is 20 inch diameter and predominately 0.25 inch wall thickness. It was originally constructed from vintage ERW pipe circa. 1960 to 1970 and is 29.86 miles long. There have been no reportable incidents on the line. There are various HCAs, Class 3 and Class 4 locations, and newly defined MCAs (per 192.3) along the line. The approach taken by the operator is to reconfirm the MAOP along the full pipeline length to cover the possibility of class location changes in the future. MAOP Reconfirmation via method 3 is detailed in clause 192.632. A critical element of an ECA per method 3 is the implementation of various ILI technologies to detect, identify, and size target threats. ROSEN provided an enhanced program of ILI including technologies to assess material properties, and crack-like/metal-loss anomalies. A critical aspect of method 3 is the use of appropriate material properties in the ECA. The operator deployed the RoMat PGS ILI system as the foundation for material property verification process to fulfill the requirements of clause 192.607 of 49 CFR 192, a requirement to establish ‘unknown material properties’. This paper describes the comprehensive work performed by the partnership of ROSEN and LGE to establish and fulfill the MAOP reconfirmation process. Such a large ILI program is a significant undertaking considering the associated data-integration, operational and engineering details that have to be addressed. This paper presents the methodologies used for each stage of the ECA process and how the ILI and material verification results were used to determine predicted failure pressures and remaining life. By satisfying the regulations, the operator has established a process to manage pipeline integrity, reduce risk, and reconfirm MAOP.

A Pipeline DNA Approach Within Fitness-For-Purpose Assessments

2021 ◽  
Author(s):  
Jason Edwards
Keyword(s):  
Material Property ◽  
Material Properties ◽  
Critical Assessment ◽  
Fitness For Purpose ◽  
Safety Regulations ◽  
Integration Approach ◽  
The Us ◽  
Property Verification ◽  
Pipeline Safety ◽  
Representative Material

Abstract Establishing a robust knowledge of material properties forms the basis of any FFP assessment. In light of the revised Federal Pipeline Safety Regulations in the US, operators of gas transmission pipelines are required to possess Traceable, Verifiable and Complete (TVC) records for input into FFP assessments and to support MAOP. ROSEN has been engaged by several operators to reconfirm the MAOP along the full pipeline length using the Engineering Critical Assessment (ECA) approach. This is a data integration approach using multiple ILI technologies to detect, identify and quantify the inputs required for a robust FFP assessment. A crucial aspect was the use of TVC material properties in the ECA, in which the RoMat Pipe Grade Sensor (PGS) service was used as the foundation for material property verification, ensuring accurate material properties are used in the ECA. Traditionally, ILI has not been able to provide strength data. However, with the addition of ROSEN’s Pipe Grade Sensor (PGS) technology, pipe populations; defined as a group of pipes with shared material properties and characteristics, can now be reliably identified and a strength grade assigned to each population. New NDT technologies already available on the market allow us to increase the confidence within the population assessment as well as further characterize the populations of pipes. This “Pipeline DNA” approach, incorporating both the PGS technology and in-field material property verification, ensures accurate or representative material properties are used in any future integrity studies. This paper describes the ROSEN approach to “Pipeline DNA”, and how it can be used in combination with material verification as a foundation for FFP assessments in an effort to reconfirm MAOP.

New Classification Approach for Dents With Metal Loss and Corrosion Along the Seam Weld

2016 ◽  
Author(s):  
J. Bruce Nestleroth ◽  
James Simek ◽  
Jed Ludlow
Keyword(s):  
Magnetic Field ◽  
Low Frequency ◽  
Metal Loss ◽  
Integrity Assessment ◽  
Seam Weld ◽  
Low Field ◽  
Electric Resistance Welding ◽  
Weld Corrosion ◽  
Pipeline Safety

The ability to characterize metal loss and gouging associated with dents and the identification of corrosion type near the longitudinal seam are two of the remaining obstacles with in-line inspection (ILI) integrity assessment of metal loss defects. The difficulty with denting is that secondary features of corrosion and gouging present very different safety and serviceability scenarios; corrosion in a dent is often not very severe while metal loss caused by gouging can be quite severe. Selective seam weld corrosion (SSWC) along older low frequency electric resistance welding (ERW) seams also presents two different integrity scenarios; the ILI tool must differentiate the more serious SSWC condition from the less severe conventional corrosion which just happens to be near a low frequency ERW seam. Both of these cases involve identification difficulties that require improved classification of the anomalies by ILI to enhance pipeline safety. In this paper, two new classifiers are presented for magnetic flux leakage (MFL) tools since this rugged technology is commonly used by pipeline operators for integrity assessments. The new classifier that distinguishes dents with gouges from dents with corrosion or smooth dents uses a high and low magnetization level approach combined with a new method for analyzing the signals. In this classifier, detection of any gouge signal is paramount; the conservatism of the classifier ensures reliable identification of gouges can be achieved. In addition to the high and low field data, the classifier uses the number of distinct metal loss signatures at the dent, the estimated maximum metal loss depth, and the location of metal loss signatures relative to dent profile (e.g. Apex, Shoulder). The new classifier that distinguishes SSWC from corrosion near the longitudinal weld uses two orientations of the magnetic field, the traditional axial field and a helical magnetic field. In this classifier, detection of any long narrow metal loss is paramount; the conservatism of the classifier ensures that high identification of SSWC can be achieved. The relative amplitude of the corrosion signal for the two magnetization directions is an important characteristic, along with length and width measures of the corrosion features. These models were developed using ILI data from pipeline anomalies identified during actual inspections. Inspection measurements from excavations as well as pipe removed from service for lab analysis and pressure testing were used to confirm the results.

Balancing Pipeline Safety and Cost Integrity Management Through Performance Validation of In-Line Inspection Data

2010 ◽  
Author(s):  
Rick McNealy ◽  
Sergio Limon-Tapia ◽  
Richard Kania ◽  
Martin Fingerhut ◽  
Harvey Haines
Keyword(s):  
Failure Criteria ◽  
Actual Condition ◽  
Metal Loss ◽  
Integrity Management ◽  
Multiple Case ◽  
Multiple Case Studies ◽  
Inspection Data ◽  
Performance Validation ◽  
The Cost ◽  
Pipeline Safety

In-Line Inspection (ILI) surveys are widely employed to identify potential threats by capturing changes in pipe condition such as metal loss, caused by corrosion. The better the performance and interpretation of these survey data, the higher the reliability of being able to predict the actual condition of the pipe and required remediation. Each ILI survey has a certain level of conservatism from the assessment equations such as B31G and sensitivity to ILI performance for measurement uncertainty. Multiple levels of conservatism intended to limit the possibility of a non-conservative assessment can result in a significant economic penalty and excessive digs without improving safety. A study was undertaken to evaluate the reliability of responses to ILI corrosion features through multiple case studies examining the effects of failure criteria and data analysis parameters. This paper discusses the effect of validated ILI performance on safety, and addresses the risk of false acceptance of corrosion indications at a prescribed safety factor. The cost of unnecessary excavations due to falsely rejecting ILI predictions is also discussed.

scholarly journals Predictive Scheduling with Markov Chains and ARIMA Models

2020 ◽  
Vol 10 (17) ◽  
pp. 6121
Author(s):  
Łukasz Sobaszek ◽  
Arkadiusz Gola ◽  
Edward Kozłowski
Keyword(s):  
Production Scheduling ◽  
Job Scheduling ◽  
Job Shop ◽  
Moving Average ◽  
Industrial Applications ◽  
Critical Element ◽  
Machine Failure ◽  
Simplifying Assumptions ◽  
Simulation Testing ◽  
Verification Process

Production scheduling is attracting considerable scientific interest. Effective scheduling of production jobs is a critical element of smooth organization of the work in an enterprise and, therefore, a key issue in production. The investigations focus on improving job scheduling effectiveness and methodology. Due to simplifying assumptions, most of the current solutions are not fit for industrial applications. Disruptions are inherent elements of the production process and yet, for reasons of simplicity, they tend to be rarely considered in the current scheduling models. This work presents the framework of a predictive job scheduling technique for application in the job-shop environment under the machine failure constraint. The prediction methods implemented in our work examine the nature of the machine failure uncertainty factor. The first section of this paper presents robust scheduling of production processes and reviews current solutions in the field of technological machine failure analysis. Next, elements of the Markov processes theory and ARIMA (auto-regressive integrated moving average) models are introduced to describe the parameters of machine failures. The effectiveness of our solutions is verified against real production data. The data derived from the strategic machine failure prediction model, employed at the preliminary stage, serve to develop the robust schedules using selected dispatching rules. The key stage of the verification process concerns the simulation testing that allows us to assess the execution of the production schedules obtained from the proposed model.

scholarly journals Generic Methodology for Formal Verification of UML Models

2022 ◽  
Vol 72 (1) ◽  
pp. 40-48
Author(s):  
K.H. Kochaleema ◽  
G. Santhosh Kumar
Keyword(s):  
Software Development ◽  
Embedded System ◽  
Formal Verification ◽  
Formal Methods ◽  
Error Detection ◽  
Formal Model ◽  
Early Error ◽  
Verification Process ◽  
Property Verification ◽  
Uml Models

This paper discusses a Unified Modelling Language (UML) based formal verification methodology for early error detection in the model-based software development cycle. Our approach proposes a UML-based formal verification process utilising functional and behavioural modelling artifacts of UML. It reinforces these artifacts with formal model transition and property verification. The main contribution is a UML to Labelled Transition System (LTS) Translator application that automatically converts UML Statecharts to formal models. Property specifications are derived from system requirements and corresponding Computational Tree Logic (CTL)/Linear Temporal Logic (LTL) model checking procedure verifies property entailment in LTS. With its ability to verify CTL and LTL specifications, the methodology becomes generic for verifying all types of embedded system behaviours. The steep learning curve associated with formal methods is avoided through the automatic formal model generation and thus reduces the reluctance of using formal methods in software development projects. A case study of an embedded controller used in military applications validates the methodology. It establishes how the methodology finds its use in verifying the correctness and consistency of UML models before implementation.

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