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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

Copyright © Sonomatic Ltd 2012

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


Example of Sentence Plot

Corrosion

Detection

threshold


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?


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


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


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


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


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


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


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


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


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


Corrosion Present

• Modelled illustration

– Subtract exponential term to one population

– No negative terms

• No “negative corrosion”

– More heavily tailed than normal distribution


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


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


Example of Real MFL Corrosion Rate

• Gas pipeline – two MFL pigging runs 2 years apart


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


Differing Error Distributions


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


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


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


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


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


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


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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