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Benefits of Statistical Analysis of Intelligent
Pigging Data
Dr Patricia Conder
www.sonomatic.com
Current ILI Data Analysis
• Magnetic Flux Leakage (MFL) and Ultrasonic (UT) In-Line Inspection (ILI) tools
– Identifies location, depth, and length of defects to defined tolerances
– Gives a view on the current state of the pipeline but results affected by measurement error
• Corrosion rates based on depth changes in matched defects
– Ignores new defects
– Impact of measurement error needs to be considered
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Advanced Statistical Analysis
• ILI datasets consist of large numbers of
measurements
– Typical industry reporting does not include detailed
statistical analysis
• Considerable improvement possible by using
advanced statistical methods
• This presentation will discuss:
– Current limitations of ILI data analysis and how to
overcome them
– Understanding the effects of errors and how this leads to a
more representative view on pipeline condition
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Example of Sentence Plot
Corrosion
Detection
threshold
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ILI Tolerance
• Defect depth sizing accuracy is expressed in
terms of level of confidence, for relative or
fixed error
– UT 95% confidence ±0.4mm
– MFL 80% confidence ±0.1*wall thickness
• What does this mean to an individual
measured defect?
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Tolerance and Error
• Errors generally follow normal distribution
• For example: UT 95% confidence ±0.4mm
– 1000 defects same size 95% of them will lie within ± 0.4 mm of the true mean
– 25 will record depth >0.4mm of true mean
• Largest defect has the greatest error
• Tolerances do not tell you directly the error on an individual reading
– Cannot say for example 5mm defect ±0.4mm
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Measurement Error
• Real pipelines have a range of defect sizes
• Still tendency is for the largest recorded defects to be
associated with the largest error.
-50 -40 -30 -20 -10 0 10 20 30 40 500
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Measurement error (%WT)
Fre
quency
80% of rdgs within +/-15%
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
True wall loss (%WT)
MF
L m
easure
d loss (
%W
T)
Measured
Calibration
Upper confidence
Lower confidence
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Ultrasonic Inspection Verification
• Sonomatic is the leading provider of ILI verification
services for subsea pipelines
• External UT verification of ILI results is typically used
when degradation is severe i.e. on the deepest
recorded defects
• Majority of time
defects found to be
less severe than
ILI states
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Corrosion Present
• Corrosion processes can appear to be random
and unpredictable but there is often some
underlying order
– When viewed on large enough scale
• Strong basis for application of statistical
methods
– Corrosion distributions can be modelled
mathematically
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CO2 corrosion example
8 9 10 11 12 13 14 15 16 17 1810
-5
10-4
10-3
10-2
10-1
100
Example - CO2 corrosion
Thickness (mm)
Pro
port
ion o
f are
a
Normal distribution
Localised pitting
Statistical Analysis of Corrosion Behaviour
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Corrosion Rate Analysis
• Difference of matched defects
– Both above detection threshold
• Negative differences ignored
– “Negative Corrosion” not real
• But negative differences are indicative of error
• Cannot predict corrosion correctly without
this data
• Difference = Corrosion + Error
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No Corrosion
• What corrosion rate would be recorded if no
corrosion had occurred only measurement
error?
• Modelled illustration
– Two randomly generated populations
– Same mean and std deviation
– Calculate the difference between pairs of data
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10.07.55.02.50.0-2.5-5.0
99.99
99
95
80
50
20
5
1
0.01
Data
Perc
ent
4.998 1.004 1000 0.179 0.918
4.983 1.024 1000 0.172 0.930
0.01491 1.444 1000 0.397 0.369
Mean StDev N AD P
Normal 1
Normal 2
Difference No Corrosion
Variable
Probability Plot of Normal 1, Normal 2, Difference No CorrosionNormal - 95% CI
Corrosion
"Negative"
Corrosion
"Real"
Simulation of Depth Difference of Matched
Defects with No Corrosion Present
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0 5 10 15 20 25 30 35 400
5
10
15
20
25
30
35
40
Measured depth 2009 (%)
Measure
d d
epth
2011 (
%)
-15 -10 -5 0 5 10 150.003
0.01
0.02
0.05
0.10
0.25
0.50
0.75
0.90
0.95
0.98
0.99
0.997
Difference in measured depth (%)
Pro
babili
ty
Example of Corrosion Rate Estimation Based on
Comparison of Matched Features
• No strong evidence of growth for the matched features
• The differences follow closely a normal distribution with a mean of -0.5%
• Standard reporting
• Tendency to ignore “-ve corrosion”
• Corrosion rate quoted as 0.79 mm/yr
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Corrosion Present
• Modelled illustration
– Subtract exponential term to one population
– No negative terms
• No “negative corrosion”
– More heavily tailed than normal distribution
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1086420
99.99
99
95
80
50
20
5
1
0.01
Data
Perc
ent
4.983 1.024 1000 0.172 0.930
0.5027 0.4906 1000 46.659 <0.005
4.480 1.123 1000 0.174 0.927
Mean StDev N AD P
Uncorroded
Exponential Corrosion Term
Corroded
Variable
Normal
Probability Plot of Uncorroded, Corroded and Corrosion Only
Simulation of Active Corrosion
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7.55.02.50.0-2.5-5.0
99.99
99
95
80
50
20
5
1
0.01
Data
Perc
ent
0.01491 1.444 1000 0.397 0.369
0.5027 0.4906 1000 46.659 <0.005
0.5176 1.528 1000 0.524 0.182
Mean StDev N AD P
Difference No Corrosion
Exponential Corrosion
Difference with Corrosion
Variable
Normal - 95% CI
Probability Plot with Corrosion Present
Simulation of Depth Difference of Matched
Defects with No Corrosion Present
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Example of Real MFL Corrosion Rate
• Gas pipeline – two MFL pigging runs 2 years apart
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Errors
– Understanding errors allows better understanding of underlying behaviour
– Generally found to be normal in distribution
– Need to evaluated on case by case basis
– Can differ
• Between runs (improvements in instrumentation, different techniques)
• Within a run (changes in wall thickness, wax accumulation etc)
• Random or Systematic
– Nature of the errors can be taken into account
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Differing Error Distributions
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3530252015105
7000
6000
5000
4000
3000
2000
1000
0
Peak Depth(%)
Frequency
Histogram of Peak Depth(%)
Threshold
Detection
56484032241680
2000
1500
1000
500
0
Depth (%)
Frequency
Histogram of Depth (%)
Threshold
Detection
Example of Systematic Orientation
Dependent Error
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11:3509:5508:1506:3504:5503:1501:3523:55
400
300
200
100
0
Orientation
Frequency
Histogram of Orientation for Defects below Detection Threshold
• Understanding errors allowed clarification of corrosion process
Benefits of Error Analysis
Some Examples
• Allows the probability of oversizing a single
defect to be determined using order statistics
– Larger the error the lower the chance of oversizing
• Less conservative probabilistic Integrity
Assessment
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0 5 10 15 20 25 30
0.001
0.003
0.01 0.02
0.05
0.10
0.25
0.50
0.75
0.90
0.95
0.98 0.99
0.997
0.999
Data
Pro
babili
ty
1st
10th
50th
Estimation of actual flaw depth
• Simulation of data sets based on measured defect depths
• Error estimation based on quoted equipment tolerances
– σm=7.8%
• Tendency to overestimate flaw size
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10 15 20 25 30
0.001
0.003
0.01 0.02
0.05
0.10
0.25
0.50
0.75
0.90
0.95
0.98 0.99
0.997
0.999
Depth (%)
Pro
babili
ty
1st
5th
10th
25th
50th
Only 10% probability
that deepest reported
feature is undersized
Bias for average is
approx 4%
Estimation of actual flaw depth• In practise measurement error less than quoted tolerances
• Error estimation based on data – σm=3.3%
• Gives probability of undersizing defect
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Probabilistic Integrity Assessment
• Understanding true measurement errors (as opposed
to quoted equipment tolerances) allows a more
realistic approach to probabilistic assessment
according to codes such as DNV RP F101
Probability of Failure for fixed corrosion rate, variable maximum pressure
Probability of Failure for fixed maximum pressure, variable corrosion rate
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Statistical analysis – Limited Coverage
• Statistical methodology allow estimates of condition in areas not inspected
– e.g. part failure of pigging tool, restricted access, external measurements only
– Extreme value analysis when degradation present
– Compliance inspection when degradation not expected – low coverage, high sensitivity
• Use simulation techniques for planning of inspections with limited coverage
– e.g. unpiggable lines
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Benefits of Sonomatic’s Statistical
Analysis of ILI Data
• Examines data set as a whole– Both individual data and matched data sets
– Embraces error analysis
– Derives better understanding of current state
– More robust prediction of future behaviour
• Gives operators an improved understanding of actual line condition
• Allows more reliable and cost effective decisions to be made
• But only if you love your errors!
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