Reassessment Intervals
Tom Bubenik, CC Technologies , a DNV Company April 2008
Outline
Basic considerations
Reassessment intervals for external corrosion - Bases for corrosion rate estimates - Regulations and Industry Standards - Basis for reassessment intervals - POE - In-line inspection comparisons - Other methods
Reassessment intervals for other defect types
Action items
Close
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04 September 2009
Slide 2
Basic considerations
Philosophies - Justify a minimum reassessment interval - Set a performance based reassessment interval
Reassessment intervals based on … -
External corrosion Internal corrosion Seam weld defects/fatigue SCC Mechanical damage
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04 September 2009
Slide 3
Bases for corrosion rate estimates ď Ž
Regulations (49CFR195). An operator must: Assess the line pipe at specified intervals Conduct a periodic evaluation as frequently as needed to assure integrity Base the frequency on risk factors specific to its pipeline Establish five-year intervals, not to exceed 68 months, for continually assessing integrity - Establish intervals based on the factors specified, the analysis of the results from the last assessment, and the information analysis.
-
ď Ž
49CFR195 also says an operator may: - Justify an engineering basis for a longer interval, where the justification must be supported by a reliable engineering evaluation combined with the use of other technology that provides an equivalent understanding of the condition of the line pipe
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04 September 2009
Slide 4
Bases for corrosion rate estimates ď Ž
API 1160 states that the inspection intervals may be: - Based on actual depths of metal loss, and subsequent reinspections, the operator may be able to estimate a corrosion rate. - Based on estimated rates, reinspection intervals should be not more than half the remaining life of the deepest unremoved or unrepaired corrosion metal loss
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04 September 2009
Slide 5
Bases for corrosion rate estimates ď Ž
API 570 says: - These formulas may be applied in a statistical approach to assess corrosion rates and remaining life calculations for the piping system. Care must be taken to ensure that the statistical treatment of data results reflects the actual condition of the various pipe components. Statistical analysis employing point measurements is not applicable to piping systems with significant localized unpredictable corrosion mechanisms.
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04 September 2009
Slide 6
Bases for corrosion rate estimates ď Ž
NACE RP0502 (ECDA standard) says: - The corrosion growth rate shall be based on a sound engineering analysis. - When the operator has measured corrosion rate data that are applicable to the ECDA region(s) being evaluated, actual rates may be used. - In the absence of measured corrosion rate data, the values and methods provided in Appendix D should be used for rate estimates. These corrosion rates have been based on the free corrosion of ferrous material in various soil types.
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Slide 7
Bases for corrosion rate estimates ď Ž
NACE RP0502 Appendix D also says - When other data are not available, a pitting rate of 0.4 mm/y (16 mpy) is recommended. - This rate represents the upper 80% confidence level of maximum pitting rates for long term (up to 17-year duration) underground corrosion tests of bare steel pipe coupons without CP in a variety of soils including native and nonnative backfill. - The corrosion rate may be reduced by a maximum of 24% provided it can be demonstrated that the CP level of all pipelines or segments being evaluated have had at least 40 mV of polarization (considering IR drop) for a significant fraction of the time since installation. - Polarization Resistance Measurements and coupons can be used under limited circumstances
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Slide 8
Corrosion Rate Summary
49CFR195 allows an operator to provide an engineering basis for a reassessment interval
API 1160 allows intervals based on estimated corrosion rates
API 570 allows use of a statistical approach to assess corrosion rates
NACE RP0502 allows the use of actual rates determined from successive in-line inspection runs, LPR measurements, or coupons
But what is the “real” corrosion growth rate…?
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04 September 2009
Slide 9
Bases for reassessment intervals
In general, OPS looks for two things when establishing reassessment intervals: - A basis for the assumed degradation (corrosion growth) rate - A way to account for uncertainties in the initial assessment results
CC Technologies experience - OPS accepts most methods proposed to estimate corrosion growth rates – straight line, fixed values, etc. - OPS is more interested in how the uncertainties from the initial assessment (pig run) are addressed - OPS is prone to adding conservatisms on top of conservatisms (e.g., use a conservative growth rate estimate, coupled with an assumed maximum flaw size that takes into account tool error, to calculate a half-life that is then used as a basis for the interval)
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04 September 2009
Slide 10
Using corrosion growth rates to set re-assessment intervals
Corrosion growth rates, whether they are deterministic or probabilistic, can form the basis for justifying a minimum reassessment interval or, eventually, establishing “performance based” intervals
Approaches used: - Deterministic - Probabilistic
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Slide 11
Deterministic Models
Approach - Calculate when the first defect will “fail” - Fixed growth rate, or rate based on pit-to-pit matching (or similar
Advantages - Quick and easy (deepest or most severe defect drives the analysis) - Can consider both leak and rupture with suitable model
Disadvantages - No clean way to address pig uncertainties, arbitrarily adding a tolerance to the reported depth (e.g., 10%) can be overly conservative - Pit-to-pit matching often leads to problems due to - Lack of correlation between runs - Reported depths that both increase and decrease - “Outliers” produce unrealistic growth rates
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Slide 12
Probabilistic Models
Approach - Growth rates can be deterministic, based on pit-to-pit matching (or similar), based on statistical comparisons of pig runs (SAC), or modeled statistically (Monte Carlo simulation) - Statistical model of pig accuracy (POE)
Advantages - Realistic treatment of pig inaccuracies - POE readily accepted by regulators
Disadvantages - More complex, requires “expertise” to establish corrosion growth rates - Same problems with pit-to-pit matching - SAC and Monte Carlo modeling not as well accepted by regulators
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04 September 2009
Slide 13
POE
Probability of Exceedance (POE) - Simple model – assumes pig call list includes all relevant features (potential defects) - Bayesian model – considers probabilities of detection, identification, etc.
Simple POE model -
Most often used by CC Technologies and other contractors Considers all reported metal loss defects Grows defects using deterministic growth rates Uses accuracy of the pig to calculate the likelihood a defect depth or severity will exceed a threshold value - Pig accuracy can be based on published specifications or on dig data. - Establishes a baseline likelihood that a leak or rupture today - Estimates likelihood a leak or rupture will occur in the future based on operator’s repair strategy following an in-line inspection.
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Slide 14
Pig Accuracy Estimate
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Slide 15
Pig Accuracy Estimate
A regression is used to establish pig bias based on dig results - Individual depths are adjusted based on regression or performance spec - Various approaches can used for regressions: - Linear, piece-wise linear, weighted, other
Same regression used to estimate error bands (e.g., standard deviation) - Standard deviation used to estimate likelihood defect depth or severity reaches a threshold for each pig call - “Leaks” predicted when depth reaches 80, 90, or 100% wt - Rupture predicted when failure pressure equals operating pressure - MOP typically used - Discrete point pressures can be incorporated - Results combined to give overall likelihood or likelihood along pipeline
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Slide 16
Likelihood of Exceeding a Threshold
The probability that an in-line inspection call of 50% is really 70% or deeper is numerically equal to the area under the curve to the right of 70%
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Slide 17
Combined results
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Slide 18
Output ď Ž
Basic results are typically given as plots of likelihood versus time
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Slide 19
Reassessment Interval
POE = function of repair strategy and time
Interval based on a target POE - Consider target POE relative to current likelihood of leak or rupture
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Slide 20
Bayesian POE
Not covered in detail in this presentation
Uses probability of detection and sizing accuracy to recreate the most likely set of defects for a set of pig calls - Contains more defects than pig calls, especially for more shallow defects - Can account for missing data (e.g., due to a sensor bank failing during the inspection)
Can also handle incorrect identifications (e.g., metal loss due to corrosion reported as mechanical)
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Slide 21
POE Developments (Simple or Bayesian)
Segmented POE as a function of location along the line, discrete point pressures, and number and severity of pig calls - Using a “sliding window” approach to identify highest priority areas
Variable corrosion growth rates based on SME judgment - Commonly used by most contractors and pipeline companies
Corrosion growth rates determined from successive in-line inspection results. - Should provide operators with realistic corrosion growth rates provided inherent errors in pig calls are addressed
Multiple (successive) POEs used to “adjust” corrosion growth rates - … by comparing POE calculated from most recent inspection to that from an earlier inspection …
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Slide 22
ILI based approaches
In-line inspection comparisons
Most practitioners agree that pit-to-pit depth matching is not reliable when based on feature lists
Matching signals sometimes works but even PII’s RunCom™ has had significant difficulties
These difficulties are inherent in the inspection methodologies due to: -
Changes in tools and analysis algorithms Sensor position relative to defect Variations in magnetic properties Remanent magnetization, etc. etc.
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04 September 2009
Slide 24
Why use statistically based ILI comparisons?
Corrosion is a constantly changing process - Local environments set up and change as the corrosion process takes place - It’s nearly impossible to predict
Pig data have been successfully used for years (decades) to identify and remove corrosion defects from pipelines. - “Normal” emphasis is on properly detecting and sizing the most significant defects - Results from successive inspections show trends - Comparing groups “windows” of inspection results can identify where corrosion growth is most likely: - Statistically Active Corrosion - Sliding-window analyses
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04 September 2009
Slide 25
Statistically Active Corrosion (SAC)
For successive ILI runs, identify locations with sufficient statistical evidence of active corrosion growth
Performed by progressively moving a specified window length along the pipeline and evaluating pig call depths within each window.
A median and other statistically valid measures are calculated for each valid window in each of the selected ILI pig runs using extreme value statistics.
Valid windows are then matched across the selected pig run combination, and for these matched windows a corrosion rate is determined using the median values and the time interval between the pig runs.
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04 September 2009
Slide 26
Statistically Active Corrosion (SAC) (cont’)
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Slide 27
Statistically Active Corrosion (SAC)
•For each window, fit an extreme value distribution to the max pig call depth in each 1 foot window. To use SAC, a minimum number of pig calls is needed •Determine the median value and move on to the next foot window © Det Norske Veritas AS. All rights reserved
04 September 2009
Slide 28
Statistically Valid Corrosion
Deterministic or statistically described corrosion growth rates along the pipeline could be incorporated in the POE process.
Not yet implemented
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04 September 2009
Slide 29
Multiple ILI Run Analysis
Use initial summaries for general comparison - If the overall results aren’t consistent, the local results won’t be
Perform joint matching and data alignment - Most pig runs can be aligned each other using girth welds locations - Aligning pig data with as-builts also straightforward - Aligning data with aboveground measurements often problematic - GoogleEarth™ or similar programs help greatly even when survey data are not well GIS’ed
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Slide 30
Line wide comparisons
Orphan Joints: Matched
2004 Orphan
2007 Orphan
2004 MFL
2007 MFL
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Slide 31
Line wide comparisons
Orphan joints are expected, especially as newer tools increase their sensitivity to smaller defects
Analyze orphans in both runs - Joints in first inspection but not the second should be (mostly) explainable - Joints in second inspection but not first must be analyzed - Depth, length, width, area
Using average or “typical” comparison metrics helps identify outliers - For example, if the new inspection has 5 times the number of calls as the first, but some joints have 100 times the first, the latter must be evaluated
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04 September 2009
Slide 32
Multiple ILI Run Comparisons
High-Level Comparison
Overview Category Date Tot. Length Tool Velocity Tool Coverage Tool Technology Min. Reporting Threshold #Joints #Ext. ML Calls #Int. ML Calls # Dents # Ovalities # Appurtenances # AGMs # Bends # Casings SMYS MAOP/MOP Min Burst Pressure (0.85dl) (psi) Min Burst Pressure (EA) (psi) Quality # Deposits # Echo Loss # Ext ML with quality issue # Int ML with quality issue # Dents with quality issue # Ovalities with quality issue # Bends with quality issue # Casings with quality issue # Other
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External Metal Loss
Laminations
Total Ext. Calls Top Side Ext. Calls Bottom Side Ext. Calls Max Ext ML Calls in one joint # Calls w/in 2ft of GW # of ERF values > 1 # of ERF values > 1 when MAOP = 72%SMYS Max Ext. CGR based on linear growth since manufacture Minimum Remaining Life # Immediate Condtions (CFR 195) # 60-Day Conditions (CFR 195) # 180-Day Conditions (CFR 195) # 60-Day Conditions (CFR 195)
Internal Metal Loss
Total Features # Joints w/Laminations # Laminations per mile # Laminations making surface contact # Laminations associated with a GW # Laminations w/in 2 ft of GW Max # Laminations per joint
Ovalities Total Features # Joints w/Ovalities # Ovalities per mile # Top Side (above 4-8:00) # Bottom Side (below 4-8:00) # <2% OD # >2% OD # W/ML in Prox (+/- 3ft)
Appurtenances
Total Int. Calls Top Side Int. Calls Bottom Side Int. Calls Dents Max Int ML Calls in one joint Total Features # Calls w/in 2ft of GW # Joints w/Dents # of ERF values > 1 # Dents per mile # of ERF values > 1 when MAOP = 72%SMYS # Top Side (above 4-8:00) Max Int. CGR based on linear growth since manufacture # Bottom Side (below 4-8:00) Minimum Remaining Life # <2% OD # Immediate Condtions (CFR 195) # >2% OD # 60-Day Conditions (CFR 195) # W/ML in Prox (+/- 3ft) # 180-Day Conditions (CFR 195) # Multidents # 60-Day Conditions (CFR 195) # 60-Day Coniditions (CFR-195) # 180-Day Coniditions (CFR-195)
04 September 2009
Total Features # Launcher # Receiver # Flanges # Valves # Taps # Tees # Other
Slide 33
Pit-to-pit matching
Ground rules need to be established - Clustering rules and depth thresholds must be considered
Types of matches - One to One - One to Multi - Multi to Multi
( to ) ( to ) ( to )
Considerations: - Tool tolerances - Tool orientation shift - Anomaly origin
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04 September 2009
Slide 34
Other considerations
Most successive inspections show indications that grow and shrink (often in similar numbers)
Any outliers should be evaluated (those that grow substantially as well as those that shrink substantially) - Significant differences mean the defects did change or the defects were hard to analyze or the signals changed for some unidentified reason
Basing corrosion growth rates on this type of comparisons will require active learning - Pit-to-pit matching, if successful, would allow a statistical description to be made of the corrosion growth rates
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04 September 2009
Slide 35
“Binning” can be used to further quantify corrosion rates
Is corrosion preferentially attacking welds? (girth or longitudinal seam) Do isolated pits growth faster than more generalized corrosion?
ML Dist to GW 10%
5%
MP 517 Int ML -- Individual & Clusters Cluster GW
12:00
GW
Individual
0%
11:00
0-1ft
10:00
1-2ft
2-3ft
3-4ft
4-5ft
5-6ft
9:00
Orientation (hh:mm)
8:00
7:00
6:00
5:00
4:00
3:00
2:00
1:00
0:00 517.379
517.382
517.384
517.387
517.389
517.392
Line Mile Post
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Slide 36
“Report Card”
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Slide 37
Further Capabilities (con’t)
60%
Customizable, scrollable charts
y = 0.8682x + 0.0593 R2 = 0.6628 50%
If field data available, can match tool to field measurement
Field
40%
30%
20%
10%
- Linear regression can give Probability of Exceedence (POE)…
0% 0%
10%
20%
30%
40%
50%
Reported
Depth (%)
Ext ML Depth Comparison
75%
2003 2004
50% 25% 0% 28000
28800
29600
30400
31200
32000
Chainage (ft) © Det Norske Veritas AS. All rights reserved
04 September 2009
Slide 38
Integrating ILI Data with Other Data Sets
Aboveground Survey Data - Use ILI data in conjunction with CIS (and DCVG) data to broadly categorize the pipeline - For example, use the data to identify the xx% of the line that is not likely to corrode faster than, say, 2 to 4 mills per year. - These pits, unless very deep to begin with, are not likely to fail or significantly impact POE values
Soils Data - Certain soil pH, texture, drainage, etc. characteristics can lead to coating degradation
Pressure Data - Creating pressure profiles throughout the pipeline based on elevation. - Used to define failure pressures along the line for POE calculations - Hydrotest results could also be incorporated
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04 September 2009
Slide 39
Robust Data Integration
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Slide 40
Other rationale for reassessment intervals
All methods require unusual circumstances to be identified and treated separately - Stray current corrosion - MIC
Linear polarization resistance measurements - Produces local information on corrosion rates without cathodic protection. - Rules of thumb used to address CP
Coupon monitoring (internal and external) - Placement critical, especially for internal corrosion
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04 September 2009
Slide 41
Reassessment intervals for other conditions
SCC - separate presentation - Accepts/allows “chicken scratch” as non critical - If you look hard enough, you will eventually find some - Sets reassessment and/or other mitigation as a function of conditions found - Disbonded coating only - Disbonded with reduced pH electrolyte - Non significant SCC - Significant SCC - Method is evolving - Lessons learned need to be fed back into the selection process for exploratory excavation programs
NACE SCCDA provides limited guidance - Finding SCC usually triggers a hydrotest requirement
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04 September 2009
Slide 42
Reassessment intervals for other defects
Seam weld defects - Gas guys use a one time test as basis for not reassessing - “We don’t cycle our lines” (right) - Liquid guys base assessment intervals on rules of thumb or fatigue analyses - Rules of thumb: - Baker categories of fatigue loading severity and/or - Using ASME type criteria - Fatigue analyses - Deterministic. Generally based on worst case conditions; corresponding reassessment intervals are very conservative - Probabilistic. - Basic PCStat and similar use one pressure distribution and an initial defect population based on the most recent hydrotest, etc. - More advanced methods use discrete point pressure conditions and attempt to quantify defect population present from day 1. -
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04 September 2009
Slide 43
Reassessment intervals for mechanical damage ď Ž
Some operators are attempting to use one ILI (caliper) run to remove all dents meeting OPS repair requirements, then considering the remaining dents to be static.
ď Ž
Few operators have programs in place to match subsequent pig runs to initial runs, looking for changes
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Slide 44
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Slide 45