Anomaly Assessment and Repair Panel
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Transcript of Anomaly Assessment and Repair Panel
8/8/2019 Anomaly Assessment and Repair Panel
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Tool Tolerances
October 22 nd , 2008Stephen Westwood
Manager New Product Development
BJ Pipeline Inspection Services
Anomaly Assessment and
Repair Panel
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Pipe Wall
MFL Tool –
Backing Bar
Magnet Magnet
BrushBrush
Leakage Flux
Sensor Head
Defect
Axial Magnetic Flux Leakage
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MFL Technology
• Inferred Measurement
– Not Measuring the Desired Quantity• Has Limitations
– Vendors Aware of – Operators Should be
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Tool Tolerances Why ?
• Wide Range of
Shapes, Sizes, WTand Defect Location• Wide Range of
Steel’s• Line Conditions
• Tool Performance• Process Performance
0
5000
10000
15000
20000
25000
0 50 100 150 200 250Magnetic Field H (Oersteds)
F l u x
D e n s
i t y B
( G a u s s
)
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Tool & Data VerificationPull Rig
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Defect Size (Length, Width, Depth)
Calculating Defect Size
Apply Regression
Techniques
Real Corrosion and more...
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Defect Signal & Wall Thickness
Defect Dimensions – 70mm x 70mm x 60%
Wall Thickness – 11.8mm Wall Thickness – 18.6mm
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Velocity Affected Defect2.3m/s – 5.1mphReported Size
45m x 55mm x 78%
6.8m/s – 15.2mphReported Size
42mm x 54mm x 40%
Actual Defect Size45mm x 45mm x 79%
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Real Corrosion ClusterPipe Area
75’’ long
26’’ wide
63 Metal LossFeatures
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Communication
• Before Inspection
– Threats – Limitations
• During the Inspection – Line Conditions
• After the Inspection – Digs – Feedback
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Significance of ILIUncertaintiesOctober 22 nd , 2008
Mark StephensSenior Consultant
C-FER Technologies
Anomaly Assessment and
Repair Panel
C-FERTechnologies Inc.
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Context for Evaluation
• Reliability
– Probability that a prescribed length of the pipelinewill not fail within a certain period of time
– General approach & guidelines developed thruPRCI* provide objective basis for assessingintegrity/safety
• Adopted by CSA (Z662 – Annex O)• Under consideration by ASME for inclusion in B31.8
Reliability = 1 – Failure Probability
*Pipeline Research Council International
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Defect Specific Probability
Estimation
Data on materialproperties and
dimensions
Operatingpressure
InspectionData
Inspection-relateduncertainties
Burst modeland test results
Data fromsingle / multiple
inspections
Failure probabilityas a
function of time
Model results
Test results
x xxx
xx
xx
xx
x
modeluncertainties
Feature dimension
corrosioncharacteristics
Probability density
Growth Rate
corrosiongrowth rates
Yield stress
Frequency
pipeproperties
Over pressure
Likelihood
loadcharacteristics
Probability density
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ILI Uncertainties
Sources of uncertainty consistent with API 1163
– Probability of detection (POD)• probability of feature being detected
– Probability of false call (POFC)• probability of non-existing feature being reported as feature
– Probability of identification (POI)• probability that detected feature will be corrected identified
– Sizing accuracy (measurement error)• accuracy with which feature dimension is reported
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Key ILI Uncertainties
0
10
2030
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50
Depth (% wall)
P O D ( % )
without detection floorEg: POD = 90% at threshold depth & Threshold depth = 10% wall POD - Basis for inferring
density & size distribution ofnon-detected features
-20 -15 -10 -5 0 5 10 15 20
Error (% wt)
P r o
b a b i
l i t y D e n s i t y
Probability that error iswithin error band, p int
(confidence level)Mean Error(bias)
E min E max
Error band
minustolerance in API 1163
plustolerance in API 1163
certainty in API 1163
-20 -15 -10 -5 0 5 10 15 20
Error (% wt)
P r o
b a b i
l i t y D e n s i t y
Probability that error iswithin error band, p int
(confidence level)Mean Error(bias)
E min E max
Error band
minustolerance in API 1163
plustolerance in API 1163
certainty in API 1163
minustolerance in API 1163
plustolerance in API 1163
certainty in API 1163
Tool tolerance &Confidence
Interval – basis formeasurement error
distribution
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Comments on Sizing Accuracy
Perfect tool
Actual size X i
I L I s
i z e
Y i
45°
β
Actual size X i
I L I s
i z e
Y i
Real tool
- with bias ( β) and random error (e)
±e
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Comments on Field Verification
Actual size, X i
I L I s
i z e ,
Y i
45 °
±e
β
Real tool- with bias ( β ) and random error (e)
Field size, X i
I L I s
i z e ,
Y i
45 °
±e
β0
β1
Real tool- with bias and random error
characterized by field tool with random error and possibly bias
Interpret unity plots with caution!
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Implications for Reliability
*Growth rate - independent of measured defect size
1%
10%
100%
1000%
POD 90% at10%t
Size Error±5%t
Size Error±10%t
Size Error±15%t
Size Error±20%t
Type of Uncertainty
I n c r e a s e
i n P O F
( r e l a t i v e t o p e r f e c t
t o o
l )
5 year 10 year 15 year
Class 1 natural gas line 30 NPS @ 940 psi, Grade X60 Hypothetical measured defect population*
S i z i n g e r r o r a n d
i m p a c t o n r e l i a b
i l i t y e s t i m a t e
POD and impacton reliability
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Comments on ILI Uncertainties
Impact on reliability• Sources of ILI uncertainty well understood
– Some can conservatively be ignored (e.g. POFC) – Some can have negligible impact (e.g. POD for HR tool*) – Some have more significant impact (e.g. sizing
uncertainty)• Tool performance often better than specified
– Field verification results must be interpreted with caution
* performing to capability for intended features
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Comments on ILI Uncertainties
Process
– ILI results (interpreted with acknowledgment ofinherent uncertainties) inform decisions onwhat to excavate
– Operators must understand significance of ILIuncertainties
• Verify they are consistent withclaims/assumptions
• If not make appropriate adjustmentsTrends
– Technology constantly improving• Tending towards situation where impact of
uncertainties is potentially minimal
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Addressing Corrosion Anomalies asReported by ILI Tools
October 22 nd , 2008Sergio Limon-Tapia
Pipeline Integrity Group
Williams Gas Pipeline
Anomaly Assessment and
Repair Panel
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Outline
• Our Common Goal
• The Role of MFL Tools for Addressing Corrosion• Addressing MFL Tool Performance• Elements of an ILI Performance Validation Program• Summary
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Our Common Goal
• We all share a common goal: preventing any failures in the pipelines we manage – Leaks or ruptures – Operators, regulators and ILI providers together
• External Corrosion is one of the primary threats to mostpipeline systems – We spend more time, effort and money addressing this threat
• External Corrosion Program – Prevention, control, assessment and mitigation – Stay focused on our normal good practice: CP surveys and
appropriate mitigation responses (ground beds/recoats) – ILI program: an integral part of the corrosion program
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MFL Tools for Addressing
Corrosion• The basic fundamentals of this technology have not
changed – Induce a magnetic field that saturates the pipe wall – Coil and Hall sensors capture magnetic flux leakage produced
by the metal loss present – Perform all of these tasks in a single opportunity while traveling
in one direction at about 4-7 MPH• Let’s not forget to recognize and understand the
limitations in the technology
– Characterization of various corrosion shapes – Pipeline Operators Forum classification chart – POI, POD specs
• MFL tools, analysts and operators have done a good jobin reporting and ranking corrosion features – We need refinement and fine tuning in certain areas
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Addressing MFL Tool
PerformanceElements of an ILI Performance Validation Program:• Manage the uncertainties using reliability engineering based
methods – Based on well established statistical & probabilistic principles – Quantify and document the probability of failure of a corroded area as
reported by MFL• Consider using a form of the Probability of Exceedance
concept
– The probability that a corrosion feature as reported by MFL exceeds athreshold level – Determine an acceptable POE threshold level
• Manage integrity using POE/Reliability Engineering – Allows for the prioritization of response and remediation
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Addressing MFL Tool
PerformanceElements of an ILI Performance Validation Program:
continuation …• Validate MFL performance
– Accurate in-ditch sizing of corrosion is essential – Create depth unity plots and Probability of Failure curves – Provide detailed feedback to the ILI analyst
• Understanding when adjustments to ILI data are needed – Identify correcting factors – Implement adjustments – Document that changes/adjustments made are effective
• Issue a final dig list
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Summary• We all share the common goal to continue to maintain
safety• We have experienced good success with the MFL/Analyst
work in addressing corrosion• MFL tool is an integral part of our corrosion program
• The basics of the MFL technology have not changed• We are learning to better handle the uncertainties inherentin the process (MFL-Analyst-Prioritization-Response-Remediation)
• An ILI performance validation program can be structuredusing reliability engineering based principles
• The POE approach can assist in quantifying theuncertainness and managing integrity
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Tool Tolerances
October 22 nd , 2008Chris Whitney
Manager, Pipeline Services East
El Paso Pipeline Group
Anomaly Assessment and
Repair Panel
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El Paso Pipeline System
• 37,000~ mile system
(CIG,EPNG,SNG,TGP)• 32,000~ miles in ILI program
• 3%~ HCA• 60%~ ILI miles to inspect HCAs
• 40%~ ILI miles w/o HCA
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EP ILI History
• Pre-2001
– various company approaches – ~10,000 miles, low-resolution
• 2001-2007 (4 Pipes) – 438 segments – 17,800 miles, 14,100 1 st time – ~6,500 actionable anomalies remediated
• 2008 – 85 segments, ~4000 miles
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ILI Process
• Deformation and Axial MFL tools
• “Clean” Pipeline segment• Tool speed within parameters
• Sensors functional – 97% coverage• Tool rotation
• Length, data quantity/quality, AGMs, etc.
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ILI Process• Final Report – 60 days• FPR with Modified ASME B31G vs MAOP• Metal loss box interaction• Align data with HCAs• Initial Response report
– Immediate action, pressure restriction
– 1 yr dents• Final Response report
– Scheduled investigations
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Tool Tolerance and Uncertainty• HCA
– 70% depth, 1.16 FPR – Immediate Action
• Scheduled Anomalies – Within 2 years of ILI – FPR < 1.39 (10 yr criteria, B31.8S, fig. 4)
• Monitor dig program to confirm expectations – Some unity plots using field reported data – Provide feedback to facilitate improvement and
relationships with vendors.
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Panel 1 Summary• MFL technology is a mature process for metal
loss inspection
• Sources of uncertainty are well understood• Various methods are employed to account forthese uncertainties
• Operator feedback to ILI providers is critical forcontinuous improvement• It is incumbent on Operators to apply
appropriate conservatism to the process• Incident statistics indicate industry is doing a
good job of managing corrosion using all of thetools at our disposal