Getting More From Your Intelligent Pig Inspection

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Presentation transcript:

Getting More From Your Intelligent Pig Inspection by Roland Palmer-Jones, Prof. Phil Hopkins, and Andre Goncalves Penspen Integrity PPSA Seminar Aberdeen 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

Inspection Technology 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.

Relative measurement Depth MFL Defect Sizing Signal amplitude Number of sensors affected N MAGNET S Flux Lines Pipe Wall

MFL Data © Penspen Ltd.

UT Inspection www.ndt-ag.com

Absolute Depth/Wall Thickness UT Defect Sizing Absolute Depth/Wall Thickness Time difference between echo signals

UT Data © Penspen Ltd.

Individual Defects © Penspen Ltd. © Penspen Ltd.

Multiple Defects? © Penspen Ltd.

© Penspen Ltd.

Clustering © Penspen Ltd.

Cluster

Evaluation Based on ‘Actual’ Shape RSTRENG – Riverbottom Profile DNV RP-F101 Complex Shape Method

RSTRENG

DNV RP-F101

Example 1

UT data © Penspen Ltd.

Rectangular Profile Failure Pressure 219 bar

Riverbottom Profile Failure Pressure 305 bar

Example 2

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.