ILI-to-Field Data Comparisons: What Accuracy Can You Expect?

2016 ◽  
Author(s):  
Matthew A. Ellinger ◽  
Thomas A. Bubenik ◽  
Pamela J. Moreno
Keyword(s):  
Wall Thickness ◽  
Signal Analysis ◽  
Metal Loss ◽  
Data Sets ◽  
Line Segments ◽  
Failure Pressure ◽  
General Field ◽  
Inspection Systems ◽  
Data Signal ◽  
Flux Leakage

Det Norske Veritas (U.S.A.), Inc. (DNV GL) prepared this paper in order to study the expected accuracy of in-line inspections (ILI) as a function of year, depth (both reported and field measured), and length, amongst other factors. DNV GL has access to a significant amount of data that span many different pipeline operators, ILI vendors, inspection years, and inspection technologies. DNV GL is well suited to complete this study as a result of our access to these various data sets. Over 3,000 individual comparisons of ILI and field depths and lengths spanning from 2010 through 2015 from 11 operators and 68 line segments were compiled to meet the objectives of this paper. Inspection technologies include axial magnetic flux leakage (MFL), ultrasonic wall thickness (UTWT), spiral MFL, and circumferential MFL. Based on the analyses conducted in this paper, the following conclusions were generated. • Axial MFL and UTWT inspections show significant improvements over the last several years. • Axial MFL inspection systems are capable of meeting a depth accuracy of +/−10% of the wall thickness with 80% certainty, but this has not always been the case. UTWT inspection systems are capable of meeting a higher depth accuracy. • Axial MFL inspection systems report more pits and circumferential grooves than UTWT systems. This could suggest UTWT systems are less sensitive to pits and circumferential grooves than axial MFL systems. • Both axial MFL and UTWT inspection systems routinely under call defects with field measured depths greater than 50 to 80% of the wall thickness. This is contrary to a widely held notion that ILI is conservative for deep defects. • ILI reported defect lengths do not correlate well to field measured defect lengths. In general, field measured defect lengths are greater than ILI reported defect lengths. • Depth accuracy tends to decrease slightly for very short defects (less than 1-inch) and for very long defects (greater than 40-inches). Based on these conclusions, the authors make the following recommendations: • Pipeline operators should dig more than the deepest reported defects to better understand the accuracy of the inspection tools being used and to determine whether deeper anomalies are being under called. • Pipeline operators should consider methods for evaluating change in corrosion depth from ILI survey to ILI survey to lessen the dependence on the accuracy of the ILI tools. This should include a raw data signal analysis in order to determine whether the general morphology (metal loss length and width) are changing between ILI surveys. • ILI reported defect lengths should be used in conjunction with field measured defect depths (if available) when performing failure pressure calculations. • Additional accuracy, especially for deeper defects, may only come with new tool developments. Industry support of such developments will be required to bring them to fruition.

ILI to ILI Comparisons: Quantifying the Impact of Multiple Inspections

2016 ◽  
Author(s):  
Pamela J. Moreno ◽  
Matthew A. Ellinger ◽  
Thomas A. Bubenik
Keyword(s):  
Wall Thickness ◽  
Magnetic Flux Leakage ◽  
Metal Loss ◽  
Data Sets ◽  
General Corrosion ◽  
Pipeline Segment ◽  
One To One ◽  
Pipe Geometry ◽  
The Impact ◽  
Flux Leakage

Det Norske Veritas (U.S.A.), Inc. (DNV GL) prepared this paper in order to study the repeatability of inspection results between subsequent in-line inspections. DNV GL has access to a significant amount of data that spans many different pipeline operators, ILI vendors, inspection years, and inspection technologies. DNV GL is well suited to complete this study as a result of our access to these various data sets. Over 55,000 one-to-one metal loss defect comparisons were assembled from ILI-to-ILI analyses. Reported metal loss defect depths, lengths, and widths spanning from 2003 through 2015 from 13 pipeline operators and 36 pipeline segments were compiled to meet the objectives of this paper. Inspection technologies include axial magnetic flux leakage (MFL), ultrasonic wall thickness (UTWT), spiral MFL, and circumferential MFL ILI. From analyses of these data, the following conclusions were generated: • Effect of ILI vendor: ILI repeatability is generally improved when the same ILI vendor is used (when compared to using two different ILI vendors in subsequent inspections), but this is not always true. • Reported metal loss depths: ILI repeatability decreases with increasing metal loss depth. • Pipe geometry and type: ILI repeatability is better in larger diameter pipelines and with increasing wall thickness. • POF classification: ILI repeatability is better for pitting, general corrosion, and axial grooving defects as compared to the other POF classifications. Based on these insights, the authors make the following recommendations: • Pipeline operators should consider using the same ILI vendor and tool if the goal is to identify change and/or corrosion growth in the pipeline segment. A raw signal review is encouraged in order to verify the presence, or lack thereof, changes in metal loss morphologies. The raw data review is especially important when comparing inspections from two different ILI vendors. • If the goal is to identify corrosion growth, and a pipeline operator uses different ILI vendors, it is recommended that a statistical review of one-to-one matched metal loss features take place to identify candidate locations that are more likely to be growing. The candidate locations should have a raw signal review in order to verify whether or not growth is taking place.

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.

Fitness for Service Assessment of a Metal Loss Using Combination of Small-Scale Testing and DNV RP F101/API579 Procedures

2007 ◽  
Author(s):  
A. Motarjemi
Keyword(s):  
Tensile Properties ◽  
Wall Thickness ◽  
Pressure Vessels ◽  
Small Scale ◽  
Metal Loss ◽  
Remaining Life ◽  
Test Technique ◽  
Failure Pressure ◽  
Destructive Test ◽  
Tensile Data

One of the major issues in the oil and gas industries is occurrence of corrosion on equipments in-service such as tanks, pressure vessels, piping, etc. Metal loss (general/localized) and pitting are amongst the typical corrosion damages. For assessing the significance of metal loss, information such as (a) geometry of the component, (b) a record of thickness measurements (point or profile readings) and (c) tensile properties such as Yield and Tensile strengths, preferably in the vicinity of the metal loss, are required. This information are usually fed into a Fitness for Service (FFS) assessment guideline/recommended practice such as DNV RP-F101 or API579, and a minimum required wall thickness (tmin), failure pressure or remaining life is derived. In the absence of actual tensile data (obtained from a conventional tensile test), specified minimum values (lower-bound), as suggested in the design codes, are currently the only other alternative. However, this paper is aimed at presenting two more alternative techniques; non-destructive test technique called Instrumented Indentation Testing (IIT) or Automated Ball Indenter (ABI) and a semi-destructive test technique, called Micro-Flat tensile (MFT). Both techniques are capable of determining the local tensile properties of the material in the vicinity of the metal loss. Values of the minimum required wall thickness (tmin), failure pressure and remaining life, using tensile data obtained from the IIT, MFT and specified minimum values are compared with the predictions based on the actual tensile data.

Oblique Field Magnetic Flux Leakage Inline Survey Tool: Implementation and Results

2010 ◽  
Author(s):  
James Simek ◽  
Jed Ludlow ◽  
Phil Tisovec
Keyword(s):  
Experimental Data ◽  
Magnetic Flux ◽  
Full Range ◽  
Magnetic Flux Leakage ◽  
Metal Loss ◽  
Data Sets ◽  
Data Set ◽  
Survey Tool ◽  
Oblique Field ◽  
Flux Leakage

InLine Inspection (ILI) tools using the magnetic flux leakage (MFL) technique are the most common type used for performing metal loss surveys worldwide. Based upon the very robust and proven magnetic flux leakage technique, these tools have been shown to operate reliably in the extremely harsh environments of transmission pipelines. In addition to metal loss, MFL tools are capable of identifying a broad range of pipeline features. Most MFL surveys to date have used tools employing axially oriented magnetizers, capable of detecting and quantifying many categories of volumetric metal loss features. For certain classes of axially oriented features, MFL tools using axially oriented fields have encountered difficulty in detection and subsequent quantification. To address features in these categories, tools employing circumferential or transversely oriented fields have been designed and placed into service, enabling enhanced detection and sizing for axially oriented features. In most cases, multiple surveys are required, as current tools do not incorporate the ability to collect both data sets concurrently. Applying the magnetic field in an oblique direction will enable detection of axially oriented features and may be used simultaneously with an axially oriented tool. Referencing previous research in adapting circumferential or transverse designs for inline service, the concept of an oblique field magnetizer will be presented. Models developed demonstrating the technique are discussed, shown with experimental data supporting the concept. Efforts involved in the implementation of an oblique magnetizer, including magnetic models for field profiles used to determine magnetizer configurations and sensor locations are presented. Experimental results are provided detailing the response of the system to a full range of metal loss features, supplementing modeling in an effort to determine the effects of variables introduced by magnetic property and velocity induced differences. Included in the experimental data results are extremely narrow axially oriented features, many of which are not detected or identified within the axial data set. Experimental and field verification results for detection accuracies will be described in comparison to an axial field tool.

A Feature-Specific Probabilistic Assessment of Pipeline Defect Size From ILI MFL Signal Using Convolutional Neural Network

2020 ◽  
Author(s):  
Jenny Jing Chen ◽  
Stephen Westwood ◽  
David Heaney
Keyword(s):  
Risk Assessment ◽  
Standard Deviation ◽  
Wall Thickness ◽  
Error Tolerance ◽  
Burst Pressure ◽  
Metal Loss ◽  
Failure Pressure ◽  
Novel Approach ◽  
Pressure Estimation ◽  
Specific Error

Abstract When estimating pipeline burst pressure, one of the prevalent sources of uncertainty that needs to be factored into the calculation is the model error in the estimation of feature depth and length from the in-line inspection tool. Due to modeling technique limitation, as of today many ILI vendors have feature specific error bounds depending on the morphologies of the corrosion, this error can only be reported to operators as an overall error known as the ILI tool tolerance which is usually obtained from samples of excavation data or pull test data. At the most, the error is reported by classes based on corrosion morphologies specified by Pipeline Operators Forum. For example, a commonly reported corrosion depth sizing specification is ±10% of pipe wall thickness at 80% confidence for the General type of corrosion. This can be interpreted as that the error of each reported depth estimations is assumed to fall in a normal distribution with a mean equal to 0 and standard deviation equal to 7.8% of wall thickness. The shape of the distribution, the mean and standard deviation will then be used as constants to factor in the burst pressure calculation. However, these factors are never constant for a sample of defects in reality. In fact, they ought to be variables on an individual feature basis. An example of such an approach would be a feature specific error tolerance, this could be that the estimated depth of a feature is 36%wt in an interval of [30%, 48%] of wall thickness with 80% confidence. This is believed to greatly reduce the level of uncertainty when it comes to failure pressure estimation or other type of pipeline risk assessment. The advancement in Machine Learning today, deep learning with deep neural networks, allows feature-specific error tolerance to be obtained after analyzing visual imagery of MFL signal. In this paper we will describe a novel approach to predict the size of metal loss defects and more importantly the distribution associated with each prediction. We will then discuss the benefits of this approach has with respect to risk assessment such as failure pressure estimation.

scholarly journals Pipeline In-Line Inspection Method, Instrumentation and Data Management

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3862
Author(s):  
Qiuping Ma ◽  
Guiyun Tian ◽  
Yanli Zeng ◽  
Rui Li ◽  
Huadong Song ◽  
...  
Keyword(s):  
Data Management ◽  
Petroleum Products ◽  
Metal Loss ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Inspection Method ◽  
Safety Issues ◽  
Pipeline Inspection ◽  
Inspection Systems ◽  
Non Destructive

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Full Well Corrosion Insight – Case Studies in the Added Value of Electromagnetic Thickness Measurements During Well Interventions

2021 ◽  
Author(s):  
Andrew Imrie ◽  
Maciej Kozlowski ◽  
Omar Torky ◽  
Aditya Arie Wijaya
Keyword(s):  
Case Studies ◽  
Wall Thickness ◽  
Added Value ◽  
Metal Loss ◽  
Outer Diameter ◽  
Metal Thickness ◽  
True Condition ◽  
Pass Through ◽  
External Corrosion ◽  
The Individual

AbstractMonitoring pipe corrosion is one of the critical aspects in the well intervention. Such analysis is used to evaluate and justify any remedial actions, to prolong the longevity of the well. Typical corrosion evaluation methods of tubulars consist of multifinger caliper tools that provide high-resolution measurements of the internal condition of the pipe. Routinely, this data is then analyzed and interpreted with respect to the manufacture's nominal specification for each tubular. However, this requires assumptions on the outer diameter of the tubular may add uncertainty, and incorrectly calculate the true metal thicknesses. This paper will highlight cases where the integration of such tool and electromagnetic (EM) thickness data adds value in discovering the true condition of both the first tubular and outer casings.These case studies demonstrate the use of a multireceiver, multitransmitter electromagnetic (EM) metal thickness tool operating at multiple simultaneous frequencies. It is used to measure the individual wall thickness across multiple strings (up to five) and operates continuously, making measurements in the frequency domain. This tool was combined with a multifinger caliper to provide a complete and efficient single-trip diagnosis of the tubing and casing integrity. The combination of multifinger caliper and EM metal thickness tool results gives both internal and external corrosion as well as metal thickness of first and outer tubular strings.The paper highlights multiple case studies including; i) successfully detecting several areas of metal loss (up to greater than 32%) on the outer string, which correlated to areas of the mobile salt formation, ii) overlapping defects in two tubulars and, iii) cases where a multifinger caliper alone doesn't provide an accurate indication of the true wall thickness. The final case highlights the advantages of integrating multiple tubular integrity tools when determining the condition of the casing wall.Metal thickness tools operating on EM principles benefit from a slim outer diameter design that allows the tools to pass through restrictions which typically would prevent ultrasonic scanning thickness tools. Additionally, EM tools are unaffected by the type of fluid in the wellbore and not affected by any non-ferrous scale buildup that may present in the inside of the tubular wall. Combinability between complementary multifinger caliper technology and EM thickness results in two independent sensors to provide a complete assessment of the well architecture.

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