Statistically Active Corrosion_ Lite

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Statistically Active Corrosion Assessments A DNV Columbus Integrity Tool.

07 August 2009


Step 1: Compare Defects in Each Joint This screen shows a preliminary analysis of 41 joints. While there is deep corrosion on three joints, the average depth and variability of the 2003 and 2008 ILI results are nearly identical. So, there appears to be no active corrosion.

Depth

Calls > 50%

Points = Average Depth for the Joint

Error Bars = Variability of Depths for the Joint

Joint Number Friday, 07 August 2009 Š Det Norske Veritas AS. All rights reserved.

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Step 2: Identify Outliers Depth Here are several joints with deep corrosion (the middle joint was sleeved). It could be active. A Statistically Active Corrosion (SAC) analysis will tell if some of the anomalies in each joint are growing.

Joint Number Friday, 07 August 2009 Š Det Norske Veritas AS. All rights reserved.

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Step 3: Look for New Corrosion Depth Here, we have new corrosion, but it’s mostly shallow. A Statistically Active Corrosion (SAC) analysis will determine an effective growth rate, which can then be used to estimate remaining lives.

Joint Number Friday, 07 August 2009 Š Det Norske Veritas AS. All rights reserved.

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Step 4: Growth Depth These indications might be growing, but the error bands overlap somewhat. It’s not clear whether there is active growth. SAC will identify joints like these for further analysis, which could include comparisons of the actual MFL signals.

Joint Number Friday, 07 August 2009 Š Det Norske Veritas AS. All rights reserved.

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Step 5. Check Areas with High Growth Rates  This looks like new deep corrosion – we check the MFL features lists and compare the MFL signals. Sometimes, they show there were defects in the earlier data that were not reported or were reported as something else (e.g., internal not external).

Friday, 07 August 2009 © Det Norske Veritas AS. All rights reserved.

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SAC Summary  SAC refers to a Statistically Active Corrosion assessment.  SAC creates and compares distributions of features from two (or more) inspections over a window.  When the distributions show evidence of change, the differences are analyzed to estimate growth rates.  Checks and balances weed out outliers and problem data.

Friday, 07 August 2009 © Det Norske Veritas AS. All rights reserved.

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Friday, 07 August 2009 Š Det Norske Veritas AS. All rights reserved.

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