Advances in Feature Identification Using Tri-Axial MFL Sensor Technology

2006 ◽  
Author(s):  
Scott Miller ◽  
Frank Sander
Keyword(s):  
Testing Procedure ◽  
Sensor Technology ◽  
Metal Loss ◽  
Feature Identification ◽  
Accurate Identification ◽  
Technology And Innovation ◽  
The Past ◽  
Data Analyst ◽  
Integrity Management ◽  
Flux Leakage

Pipeline operators have been using intelligent in-line inspection (ILI) tools as part of their pipeline integrity management systems for several decades now. A wide variety of ILI tools have been developed to serve a multitude of uses. Most notable is the detection, locating, and sizing of metal loss corrosion. Magnetic Flux Leakage Technology (MFL) was developed for that exact purpose, however over the years technology and innovation has vastly improved the capabilities of MFL tools. This paper contains a comparison of historical and current pipeline feature identification/classification capabilities for axial magnetizing MFL tools with Tri-Axial sensor technology. The pipeline features discussed include corrosion, mechanical defects, structural pipeline components, as well as the physical and magnetic parameters that affect accurate identification, location, and/or sizing. Some of these features have never been detected, identified, or reported in the past, and now constitute a significant portion of the training and testing procedure that occurs in the certification of a new MFL data analyst.

Development of Pinhole Corrosion Management Using MFL

2018 ◽  
Author(s):  
Guy Desjardins ◽  
Joel Falk ◽  
Vitaly Vorontsov
Keyword(s):  
Magnetic Flux Leakage ◽  
Metal Loss ◽  
Physical Measurement ◽  
Reporting Accuracy ◽  
Magnetic Modeling ◽  
Number Of Factors ◽  
Modeling Software ◽  
Measurement Methodology ◽  
Integrity Management ◽  
Flux Leakage

While In-line Inspection Magnetic Flux Leakage (MFL) tools have been used for many years to successfully manage corrosion related threats, small pinhole-sized metal-loss anomalies remain a significant concern to pipeline operators. These anomalies can grow undetected to develop leaks and cause significant consequences. The physical dimensions of these anomalies, their proximity to and/or interaction with other nearby anomalies can challenge MFL’s detection and sizing capabilities. Other factors such as tool speed, cleanliness of the line and incorrect assumptions have an impact as well. For pipeline operators to develop effective and efficient mitigation programs and to estimate risks to an asset, the underlying uncertainties in detection and sizing of pinholes need to be well understood. By using magnetic modeling software, the MFL response of metal-loss anomalies can be determined, and the effect of a number of factors such as radial position, wall thickness, depth profile, pipe cleanliness and tool speed on MFL response and reporting accuracy can be determined. This paper investigates these factors to determine the leading causes of uncertainties involved in the detection and sizing of pinhole corrosion. The understanding of these uncertainties should lead to improvements in integrity management of pinhole for pipeline operators. This paper first investigates the physical measurement methodology of MFL tools to understand the limitations of MFL technology. Then, comparisons of actual MFL data with field excavation results were studied, to understand the limitations of specific MFL technologies. Finally, recommendations are made on how to better use and assess MFL results.

Enhanced Use of ILI Data to Improve Integrity Decisions

2012 ◽  
Author(s):  
Collin Taylor ◽  
Renkang Rain Zhu
Keyword(s):  
Metal Loss ◽  
Pipeline System ◽  
Precision Data ◽  
Data Alignment ◽  
Joint Characteristics ◽  
Tool Performance ◽  
Integrity Management ◽  
Data Improvement ◽  
Longitudinal Seam ◽  
Flux Leakage

With the current generation of in-line inspection (ILI) tools capable of recording terabytes of data per inspection and obtaining millimeter resolution on features, integrity sciences are becoming awash in a sea of data. However, without proper alignment and relationships, all this data can be at best noise and at worst lead to erroneous assumptions regarding the integrity of a pipeline system. This paper will explore the benefits of a statistical alignment method utilizing joint characteristics, such as length, long seam orientation (LSO), wall thickness (WT) and girth weld (GW) counts to ensure precision data alignment between ILI inspections. By leveraging the “fingerprint” like morphology of a pipeline system many improvements to data and records systems become possible including but not limited to: • Random ILI Tool performance errors can be detected and compensated for. • Repair history and other records become rapidly searchable. • New statistically accurate descriptions are created by leveraging the sensitivities of various ILI technologies. One area of material data improvement focused on within this paper relates to long seam type detection through ILI tools. Due to the differing threat susceptibility of various weld types, it is accordingly important to identify the long seam weld types for integrity management purposes. Construction records of older vintage lines do not always contain information down to the joint level; therefore, ILI tools may be leveraged to increase the accuracy of construction records down to this level. In this paper, the possibility of ILI tools, such as magnet flux leakage tools, ultrasonic crack tools, and ultrasonic metal loss tools, to distinguish different types of longitudinal seam welds is also discussed.

Corrosion Metal Loss Interaction From In-Line Inspection and How it Can Affect the Calculated Failure Pressure

2012 ◽  
Author(s):  
Lucinda Smart ◽  
Richard McNealy ◽  
Harvey Haines
Keyword(s):  
Field Measurements ◽  
Metal Loss ◽  
Data Correlation ◽  
Failure Pressure ◽  
Industry Standards ◽  
Direct Measurements ◽  
Non Destructive ◽  
Flux Leakage

In-Line Inspection (ILI) is used to prioritize metal loss conditions based on predicted failure pressure in accordance with methods prescribed in industry standards such as ASME B31G-2009. Corrosion may occur in multiple areas of metal loss that interact and may result in a lower failure pressure than if flaws were analyzed separately. The B31G standard recommends a flaw interaction criterion for ILI metal loss predictions within a longitudinal and circumferential spacing of 3 times wall thickness, but cautions that methods employed for clustering of ILI anomalies should be validated with results from direct measurements in the ditch. Recent advances in non-destructive examination (NDE) and data correlation software have enabled reliable comparisons of ILI burst pressure predictions with the results from in-ditch examination. Data correlation using pattern matching algorithms allows the consideration of detection and reporting thresholds for both ILI and field measurements, and determination of error in the calculated failure pressure prediction attributable to the flaw interaction criterion. This paper presents a case study of magnetic flux leakage ILI failure pressure predictions compared with field results obtained during excavations. The effect of interaction criterion on calculated failure pressure and the probability of an ILI measurement underestimating failure pressure have been studied. We concluded a reason failure pressure specifications do not exist for ILI measurements is because of the variety of possible interaction criteria and data thresholds that can be employed, and demonstrate herein a method for their validation.

A Hybrid Topside Structural Approach to the Management of Aging Fleet

2021 ◽  
Author(s):  
Biramarta Isnadi ◽  
Luong Ann Lee ◽  
Sok Mooi Ng ◽  
Ave Suhendra Suhaili ◽  
Quailid Rezza M Nasir ◽  
...  
Keyword(s):  
Structural Integrity ◽  
Metal Loss ◽  
Web Based ◽  
Strong Basis ◽  
Asset Risk ◽  
Risk Ranking ◽  
Design Life ◽  
Industry Standards ◽  
Integrity Management ◽  
Inspection Data

Abstract The objective of this paper is to demonstrate the best practices of Topside Structural Integrity Management for an aging fleet of more than 200 platforms with about 60% of which has exceeded the design life. PETRONAS as the operator, has established a Topside Structural Integrity Management (SIM) strategy to demonstrate fitness of the offshore topside structures through a hybrid philosophy of time-based inspection with risk-based maintenance, which is in compliance to API RP2SIM (2014) inspection requirements. This paper shares the data management, methodology, challenges and value creation of this strategy. The SIM process adopted in this work is in compliance with industry standards API RP2SIM, focusing on Data-Evaluation-Strategy-Program processes. The operator HSE Risk Matrix is adopted in risk ranking of the topside structures. The main elements considered in developing the risk ranking of the topside structures are the design and assessment compliance, inspection compliance and maintenance compliance. Effective methodology to register asset and inspection data capture was developed to expedite the readiness of Topside SIM for a large aging fleet. The Topside SIM is being codified in the operator web-based tool, Structural Integrity Compliance System (SICS). Identifying major hazards for topside structures were primarily achieved via data trending post implementation of Topside SIM. It was then concluded that metal loss as the major threat. Further study on effect of metal loss provides a strong basis to move from time-based maintenance towards risk-based maintenance. Risk ranking of the assets allow the operator to prioritize resources while managing the risk within ALARP level. Current technologies such as drone and mobile inspection tools are deployed to expedite inspection findings and reporting processes. The data from the mobile inspection tool is directly fed into the web based SICS to allow reclassification of asset risk and anomalies management.

Challenges Related to Pipeline Free Spans in Watercourse Crossings

2021 ◽  
Author(s):  
Mario Caruso ◽  
Gerry Ferris ◽  
Hans Olav Heggen ◽  
Burke Delanty
Keyword(s):  
Management Practice ◽  
Economic Loss ◽  
The Past ◽  
Repair Costs ◽  
Vortex Induced Vibrations ◽  
River Erosion ◽  
Liquid Products ◽  
Integrity Management ◽  
Erosion Mechanisms ◽  
Pipeline Failures

Abstract Free span assessment in watercourse crossings for the on-shore pipeline industry has become a more and more important part of pipeline integrity practice. One reason is that it has become increasingly well known that the dominant cause of pipeline failures in watercourse crossings is fatigue failure due to vortex induced vibrations at pipeline free spans. Recognition of this is now being identified in industry recommended practices and owners are incorporating this type of assessment into their pipeline integrity management practice. On shore pipelines are not designed with an allowable free span as is the practice with off-shore pipelines, but are buried. Design codes specify minimum depths of cover when constructed and indicate that pipelines should be maintained so that no excessive loads occur. In the past the no excessive loads requirement has been interpreted that the pipeline must remained buried. As experience from the off-shore environment and increasingly from experience on-shore has shown that most exposed and/or free spans do not fail. Due to various river erosion mechanisms; scour, bank erosion or avulsion, previously buried pipelines do develop free spans. Some of the free spans fail and release products directly into the watercourse. Failures, particularly for liquid products, are very expensive due to the economic loss, repair costs and environment clean-up of the watercourse and its banks. Similarly, costs associated with pipeline replacement for free spanning pipelines or repair of pipelines that might develop free spans are relatively high. It is important to develop an understanding of the probability of the pipeline failing due to a free span, or put another way, determine which free span is a threat to integrity. This paper discusses some of the challenges with assessing free spans in watercourse crossings as part of integrity programs and highlights experiences in assessing this threat to integrity. The objective of this paper is to discuss some of the key uncertainties related to the management of the threat due to free spans. These uncertainties are due to the reliability of information about the free span, water velocity and condition of the pipelines.

Inline Inspection of Dents and Corrosion Using “High Quality” Multi-Purpose Smart-Pig Inspection Data

2006 ◽  
Author(s):  
Steffen Paeper ◽  
Bryce Brown ◽  
Thomas Beuker
Keyword(s):  
New Technology ◽  
Operational Performance ◽  
Sensor Technology ◽  
High Quality ◽  
Operational Conditions ◽  
The Us ◽  
Integrity Management ◽  
Inspection Tasks ◽  
Inspection Data ◽  
New Generation

A new generation of geometry sensor for ILI tools has been developed. This sensor provides highly accurate geometry data of the internal pipe contour. The technology uses the benefits of a touchless distance measurement in combination with the advantages of a mechanical caliper arm. The complementary interaction allow the measurement of accurate data under demanding operational conditions. The geometry sensor technology can be combined with a navigation unit and the high resolution MFL inspection technology on so called multi-purpose ILI-tools. The merging of different inspection tasks on a single tool is an economic solution to create and add to an ILI-database for integrity management. Field experience with this new technology will be discussed, based on more than 500 miles inspected pipeline. Most inspections were performed in the US and Canada. The operational performance of the sensors justified the new design.

New Approaches to the Automated Analysis of Ultrasonic In-Line Inspection Data

2004 ◽  
Author(s):  
H. Willems ◽  
K. Reber ◽  
M. Zo¨llner ◽  
M. Ziegenmeyer
Keyword(s):  
Neural Networks ◽  
Automated Analysis ◽  
Support Vector ◽  
Metal Loss ◽  
The Past ◽  
Analysis Process ◽  
Vector Machines ◽  
Speed Up ◽  
Size Limits ◽  
Inspection Data

Inline inspection of pipelines by means of intelligent pigs usually results in large amounts of data that are analyzed offline by human experts. In order to increase the reliability of the data analysis process as well as to speed up analysis times methods of artificial intelligence such as neural networks have been used in the past with more or less success. The basic requirement for any technique to be used in practice is that no relevant features should be overlooked while keeping the false call rate as low as possible. For the task of automated analysis of in-line inspection data obtained from ultrasonic metal loss inspections, we have developed a two-stage approach. In a first step (called boxing), any defect candidates exceeding the specified size limits are recognized and described by a surrounding box. In the second step, all boxes from step 1 are analyzed yielding basically a relevant/non relevant decision. Each feature considered to be relevant is then classified according to a given set of feature classes. In order to efficiently perform step 2, we have adapted the SVM (support vector machines) algorithm which offers some important advantages compared to, for example, neural networks. We describe the approach applied, and examples as obtained from in-line inspection data are presented.

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