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Implementation of Statistical Analysis of Inspection Data to
Improve Integrity Management Process
7th Middle East NDT Conference & Exhibition, Kingdom of Bahrain, September
2015
Vichaar Dimlaye, Sonomatic Ltd, Abu Dhabi
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Integrity Management
• The main objective is to identify and implement
actions to avoid failure by leaks or rupture for
pressure equipment.
• It is a life cycle process which is applicable to all
stages of an asset’s life.
• Operations comes with many risks, thus cost effective
decisions are required for safety and reliability of such
assets.
• To make effective decisions, it is important to
understand the associated risks and this relies on
sound knowledge of the equipment condition and
how that may evolve over time.
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Integrity Management Decisions
• Decisions have business impacts :
a. Repair or replacement of pressure vessels,
pipework and pipelines
b. Re-rating
c. Frequency and type of inspection
d. Detailed Fitness for Purpose studies
• These decisions are based on evaluation of
information from a range of sources and with varying
levels of uncertainty.
• It is important to understand the accuracy of the
information for reliable decision making.
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• Inspection is aimed to provide direct indication of
equipment actual condition– but there are (often
significant) uncertainties associated with inspection
information.
Inputs to IM Decisions
Integrity Management
Decision Making
Corrosion Risk Assessment
Hazard Identification
Consequence Assessment
Risk Based Inspection Planning
Corrosion Control
Corrosion Monitoring
Process monitoring
Process chemistry
Flow assurance
Sand Assessment
Fabric maintenance
Inspection
Compliance and regulatory
Design and fabrication info
Effectiveness of
decisions depends on
the relevance of input
information
• Effectiveness of decisions made relies on the
accuracy of the theoretical based information and
indirect measures which comes with uncertainty.
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NDT Data and Decision Making
• Inspection provides key information for use in corrosion and
integrity management.
• Inspections will always have limitations, these being
dependent on the type of NDT techniques and condition of
the equipment.
• These limitations affect the extent to which inspection
provides information on the real condition of the equipment.
Perfect knowledge of
equipment condition
Optimal decisions
Good knowledge of
equipment condition
Poor knowledge of
equipment condition
Good decisions Poor decisions
Increasing operational costs and consequential costs of failures
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NDT Data and Decision Making
• Understanding the limitations of the inspection data and making best use
of the available information is fundamental to integrity management
decisions that are aligned to the real condition of equipment.
• The significance of this is often overlooked in practice.
• Failing to understanding the limitations of inspection data and on how to
make best use of the data often affects decisions. Hence, leading to major
business impacts as
follows:
• Unnecessarily short inspection intervals leading to additional
inspection cost
• Excessive expenditure on corrosion control
• Unnecessary or premature replacement involving large capital cost
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Impact of Inspection Limitations on IM
Decision Making
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Pipework Example
• Most operators make use of RBI. Decisions
on intervals and locations are updated on
consideration of inspection results.
Corrosion Risk Assessment Integrity Assessment
Consequence of Failure Probability of Failure
Assessment
Risk ranking
Define intervals Define locations
Inspection
Integrity review
This part of the
process is only as
good as the
inspection
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Pipework Example
• 0 degree manual ultrasonic (MUT) wall
thickness measurement is the main
form of inspection for pipework (>2”) in
most process industries.
• The approach typically involves
scanning over grids at selected
locations and recording the minimum
thickness.
• Inspection conditions are challenging
involving access restrictions and
variable surface conditions.
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Pipework Example
• Sonomatic has carried out statistical analysis of many large (1k’s -10k’s point) inspection history databases for UK offshore operators
• The graph illustrates typical in-service performance of MUT for pipework where there is no corrosion active.
• Graph shows variations for repeated measurements (over a 12 year period) at the same locations in a system where there is limited actual change
-4 -3 -2 -1 0 1 2 3 4
0.0005
0.001
0.0050.01
0.050.1
0.25
0.5
0.75
0.90.95
0.990.995
0.999
0.9995
Difference relative to mean value for location (mm)
Pro
babili
ty o
f short
fall
Measured
T-location
Approx 10% (<0.01) of readings have
underestimation of more than 1 mm.
Approx 10% (>0.99) of readings have
overestimation of more than 1 mm.
Measurement Errors (mm)
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Pipework Example
• IM system will tend to focus on areas where the readings are lowest.
• Given the high error levels in the inspection, in any situation in which there are a large number of readings, the extremes will tend to be those measurements with the largest errors.
-4 -3 -2 -1 0 1 2 3 4
0.0005
0.001
0.0050.01
0.050.1
0.25
0.5
0.75
0.90.95
0.990.995
0.999
0.9995
Difference relative to mean value for location (mm)
Pro
babili
ty o
f short
fall
Measured
T-location
Decision making can be driven by the
measurements with the largest errors.
This is what happens in many current
database driven approaches.
Measurement Errors (mm)
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Pipework Example
• Its not only current condition that is of interest. Remaining life has to be considered in decision making – rate of degradation is important.
• Impact of measurement error tends to be more significant on corrosion rate.
• Negative corrosion rate tend to be ignored-but they are indicative of error.
• Example from a system with some active degradation. -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0.0010.003
0.01 0.02
0.05
0.10
0.25
0.50
0.75
0.90
0.95
0.98 0.99
0.9970.999
Corrosion rate (mm/yr)
Pro
babili
ty o
f short
fall
Approx 35% of locations
have negative corrosion
rate – indicator of high
error levels in rate
This is where the drivers for integrity
management decisions will tend to be.
This coincides with the region in which the
largest errors can be expected.
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Pipework Example
• Presence of measurement errors can give rise to inappropriate decisions being made.
• The priorities for inspection in current practise for many RBI managed systems are defined as:– Locations where the “wall loss” relative to nominal is largest,
– Locations where the “corrosion rates” are highest,
– Locations where the shortest “remaining life” is calculated.
• All three points are often locations with largest errors.
• The above are established by direct analysis of the information in the inspection database, without any understanding or knowledge of the implication of the measurement errors.
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Pipework Example:
Improved Approaches to Reduce Measurement
Errors• Accuracy and reliability of thickness measurement methods can be
improved by:
• Inspection techniques, e.g. automated corrosion mapping vs MUT
• Inspection procedures, e.g. set-ups and locations for repeat inspection
• Minimisation of surface related error, e.g. paint thickness correction
• IM decisions are made on large datasets and hence need for appropriate statistical analysis to assess the impact of measurement errors.
• A range of statistical analysis methods can also be applied to assist in more reliable decision making, i.e. decisions better aligned to actual condition and variation within a system.
• Closer integration of inspection and analysis activities is essential to making best use of inspection within integrity management.
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Implementation of Statistical Analysis
• Statistical analysis methods are fundamental to understanding the effects
of measurement error and making appropriate allowances in decision
making.
• Statistical analysis methods are also applicable to assessments of
corrosion. Corrosion processes across a wide range of conditions are
found to be amenable a simple parametric description. For example,
analysis of Sonomatic corrosion mapping data indicates typically
regular behaviour of the corrosion process for the following:
− Pressure equipment (vessels, pipework, pipelines) with active CO2 corrosion
− Pressure equipment with H2S corrosion
− Pressure vessels and pipework with internal O2 corrosion
− Pressure vessels with external O2 corrosion (CUI)
− Pipework with naphthenic acid corrosion
− Pipework and tanks with under-deposit corrosion
− Petrochemical and minerals refinery vessels with acid corrosion
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Statistical Behaviour of Corrosion
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
Corrosion
• There are two distributions:
• Normal distribution associated with as-
manufactured wall thickness variations of the
material.
• Exponential distribution (linear on log
scale) representing the distribution over the
degradation area.(Approx. 10% of the
inspected area)
• Corrosion mapping data is from a
lower part of a separator vessel and it
has suffered from CO2 corrosion.
• The wall thickness distribution
generated from the corrosion mapping
data.
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Statistical Analysis in Non-Intrusive
Inspection (NII) of Vessels
Inspectionmm 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
µs
13
14
15
16
17
18
19
20
NII Assessment and definition of requirements
CO2
nx
i i
i
j j
j
P(X )P( )P( | X)
P(X )P( )
0 100 200 300 400 500 600 700 800 900 10004
5
6
7
8
9
10
11
12
13
14
Extent of corrosion (mm)
Allo
wable
wall
loss (
mm
)
Shell
Ends
Evaluation of inspection including
Statistical Analysis of resultsJustification
Workscope meeting
requirements
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
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NII Statistical Analysis
• Statistical analysis is a fundamental requirement to NII carried out in accordance with the HOIS Recommended Practice.
• Outputs of analysis cover– Is degradation present
– Estimates for minimum thickness
– Estimates of probabilities for limiting conditions
– Evaluation of whether the inspection meets the requirements, i.e is the coverage sufficient, is the information provided by the technique suitable
• Outputs of analysis also feed into the RBI– Updated estimates of worst degradation
– Corrosion rate estimates
– Nature of distribution of degradation
• The analysis also allows assessment of the capability and limitations of the inspection technique as deployed.
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Statistical Analysis for Inline Inspection (ILI)
• Current analysis for ILI data tends to ignore negative corrosion rates, which are due to measurement errors.
• Also, ignoring errors associated with positive corrosion rates.
• For illustration, let’s consider two ILI runs which were carried out on a pipeline.
• Figure represents the wall loss for the matched defects for the two ILI runs.
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 (
%)
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Statistical Analysis for Inline Inspection (ILI)
• Normal probability plot is generated for the matched defects data,
• Red line is the normal distribution fit to the data with mean of -0.5% and a standard deviation of approx. 4.6%.
• Negative mean, suggest on average no corrosion has occurred between 2009 and 2011 for the matched defects.
• Symmetrical nature of the differences indicates most of them are due to measurement errors,
• As such, this will strongly affect any estimation of corrosion rate.
-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
bability
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Correction of Measurement Errors
• Correction approach developed by Sonomatic based on the distribution of measurement error and use of an appropriate distribution for the depths of the matched defects.
• Deconvolution process is carried out on the measured data to produce the Weibull distribution (in red) and the measurement error (in green).
• Measured data shows an overestimation of the actual wall loss.
• With this approach and considering the measurement error, more realistic actual condition and hence remaining life can be derived.
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Summary• Integrity management relies heavily on information on equipment
condition gathered during inspection.
• Inspection approaches have limitations affecting reliability of the information.
• As such, integrity decisions can be severely affected if these limitations are not well understood and considered.
• Improvements can be made in terms of inspection approaches to provide increased accuracy and reliability for more effective decision making.
• The use of statistical analysis ensures the value of the inspection data is maximised and decisions are aligned to provide more reliable view on actual condition of equipment and its remaining life.
• Closer integration of inspection and analysis activity is essential to making improvements to integrity management practise that Operators are beginning to expect.
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Thank You!