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TRANSCRIPT
Getting More From Your Intelligent Pig Inspection
by
Roland Palmer-Jones, Prof. Phil Hopkins, and Andre Goncalves
Penspen Integrity
PPSA SeminarAberdeen
14 – Nov 2007
The Problem
Intelligent pigs are widely used Large quantities of data are collected Defects are generally reported in a
simple spreadsheet format. Simple clustering and defect sizing
leads to safe conservative assessments
Outline
Inspection Technology– MFL– UT
Defect Sizing Defect Assessment ‘Complex Shape’ Assessment Case Study
MFL Inspection
N MAGNET S
Flux Lines
Pipe Wall
© Penspen Ltd.
MFL Defect Sizing
Relative measurement Depth
– Signal amplitude– Number of sensors affected
N MAGNET S
Flux Lines
Pipe Wall
MFL Data
© Penspen Ltd.
UT Inspection
www.ndt-ag.com
UT Defect Sizing
Absolute Depth/Wall Thickness– Time difference between echo
signals
UT Data
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Individual Defects
© Penspen Ltd.
© Penspen Ltd.
Multiple Defects?
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© Penspen Ltd.
Clustering
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Cluster
Evaluation Based on ‘Actual’ Shape
RSTRENG – Riverbottom Profile DNV RP-F101 Complex Shape Method
RSTRENG
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0.00 50.00 100.00 150.00 200.00 250.00 300.00
Length
Dept
h
DNV RP-F101
Example 1
UT data
© Penspen Ltd.
Rectangular Profile Failure Pressure 219 bar
Flat Bottom Profile
0
2
4
6
8
10
12
14
16
18
20
0 20 40 60 80 100 120 140 160 180 200
Distance Along X-Axis (mm)
Rem
aini
ng W
all T
hick
ness
Riverbottom ProfileFailure Pressure 305 bar
River - Bottom Profile
0 20 40 60 80 100 120 140 160 180 200
Distance Along X-Axis (mm)
Rem
aini
ng w
all T
hick
ness
(mm
)
Example 2
Assessment Data Maximum Reported Depth (%t)
Length (mm)
Defect Profile Failure Pressure (Bar)
Standard MFL pig ‘Cluster’
63 330 Rectangular 53
Expert MFL pig ‘ Boxes’
63 330 ‘River-Bottom’ 113
Expert External UT
50.5 1760 ‘River-Bottom’ 85
Inspection/Repair Can be Expensive
© Penspen Ltd.
© Penspen Ltd.
Summary
Defect profile data gives assessment benefits
Profile data is collected anyway and so should be supplied in an accessible format with the inspection report.