Benefits of Statistical Analysis of Intelligent Pigging Data DrPatricia Conder.

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1 Benefits of Statistical Analysis of Intelligent Pigging Data DrPatricia Conder

2 CurrentILI Data Analysis Magnetic Flux Leakage (MFL) and Ultrasonic (UT) In-Line Inspection (ILI) tools Identifies location,depth, and length of defects to defined tolerances Gives a view on the current state of the pipeline but results affected by measurement error Corrosion rates based on depth changes in matched defects Ignores new defects Impact of measurement error needs to be considered

3 Advanced Statistical Analysis ILI datasets consist of large numbers of measurements Typical industry reporting does not include detailed statistical analysis Considerable improvement possible by using advanced statistical methods This presentation will discuss: Current limitations of ILI dataanalysis and howto overcome them Understanding the effects of errors and how this leads to a more representative view on pipeline condition

4 Example of Sentence Plot Corrosion Detection threshold

5 ILI Tolerance Defect depth sizing accuracy is expressed in terms of levelof confidence, for relative or fixed error UT 95% confidence ±0.4mm MFL 80% confidence ±0.1*wall thickness What does this mean to an individual measured defect?

6 Tolerance and Error Errors generally follow normal distribution For example: UT 95% confidence ±0.4mm 1000 defects same size 95% of them will lie within ±0.4 mm of the true mean 25 will record depth >0.4mm of true mean Largest defect has the greatest error Tolerances do not tell you directlythe error on an individual reading Cannot say for example 5mm defect ±0.4mm

7 Measurement Error Real pipelines have a range of defect sizes Still tendency is for the largest recorded defects to be associated with the largest error. Frequency % of rdgs within +/-15% MFL measured loss (%WT) Measured Calibration Upper confidence Lower confidence Measurement error (%WT) True wall loss (%WT)

8 Ultrasonic Inspection Verification Sonomatic is the leading provider of ILI verification services for subsea pipelines External UT verification of ILI results is typically used when degradation is severe i.e. on the deepest recorded defects Majority of time defects found to be less severe than ILI states

9 Corrosion Present Corrosion processes can appear to be random and unpredictable but there is often some underlying order When viewed on large enough scale Strong basis for application of statistical methods Corrosion distributions can be modelled mathematically

10 Statistical Analysis of Corrosion Behaviour 10 0 Example - CO 2 corrosion 10-1 Proportion of area Localised pitting Normal distribution Thickness (mm) CO 2 corrosion example

11 Corrosion Rate Analysis Difference of matched defects Both above detection threshold Negative differences ignored Negative Corrosion not real But negative differences are indicative of error Cannot predict corrosion correctly without this data Difference = Corrosion + Error

12 No Corrosion What corrosion rate would be recorded if no corrosionhad occurred only measurement error? Modelled illustration Two randomly generated populations Same mean and stddeviation Calculate the difference between pairs of data

13 Simulation of DepthDifference of Matched Defects with No Corrosion Present Probability Plot of Normal 1, Normal 2, Difference No Corrosion Normal - 95% CI Variable Normal 1 Normal 2 Difference No C orrosion Percent "Negative" Corrosion "Real" Corrosion Mean StDev N A D P Data

14 Measured depth 2011 (%) Example of Corrosion Rate Estimation Based on Comparison of Matched Features No strong evidence of growth for the matched features The differences follow closely a normal distribution with a mean of -0.5% Standard reporting Tendency to ignore -ve corrosion Corrosion rate quoted as 0.79 mm/yr Measured depth 2009 (%) Probability Difference in measured depth (%)

15 Corrosion Present Modelled illustration Subtract exponential term to one population No negative terms No negative corrosion More heavily tailed than normal distribution

16 Simulation of Active Corrosion Probability Plot of Uncorroded, Corroded and Corrosion Only Normal Percent Variable Uncorroded Exponential Corrosion Term Corroded Mean StDev N AD P < Data

17 Simulation of Depth Difference of Matched Defects with No Corrosion Present Probability Plot with Corrosion Present Normal - 95% CI Percent Variable Difference No Corrosion Exponential C orrosion Difference with Corrosion Mean StDev N AD P < Data

18 Example of Real MFL Corrosion Rate Gas pipeline two MFL pigging runs 2 years apart

19 Errors Understanding errors allows better understanding of underlying behaviour Generally found to be normal in distribution Need to evaluated on case by case basis Can differ Between runs (improvements in instrumentation, different techniques) Within a run (changes in wall thickness, wax accumulation etc) Random or Systematic Nature of the errors can be taken into account

20 Differing Error Distributions Histogram of Depth (%) 2000 Detection Threshold Frequency Histogram of Peak Depth(%) Frequency Detection Threshold Depth (%) Peak Depth(%)

21 Example of Systematic Orientation Dependent Error Understanding errors allowed clarification of corrosion process Histogram of Orientation for Defects below Detection Threshold Frequency :55 01:35 03:15 04:55 06:35 Orientation 08:15 09:55 11:35

22 Benefits of Error Analysis Some Examples Allows the probabilityof oversizing a single defect to be determined using order statistics Larger the error the lower the chance of oversizing Less conservative probabilistic Integrity Assessment

23 Estimation of actual flaw depth Simulation of data sets based on measured defect depths Error estimation based on quoted equipment tolerances σ m =7.8% Tendency to overestimate flaw size st 10th 50th Probability Data

24 Estimation of actual flaw depth In practise measurement error less than quoted tolerances Error estimation based on data σ m =3.3% Gives probability of undersizing defect Probability st 5th 10th 25th 50th Only 10% probability that deepest reported feature is undersized Bias for average is approx 4% Depth (%)

25 Probabilistic Integrity Assessment Understanding true measurement errors (as opposed to quoted equipment tolerances) allows a more realistic approach to probabilistic assessment according to codes such as DNV RP F101 Probability of Failure for fixed corrosion rate, variable maximum pressure Probability of Failure for fixed maximum pressure, variable corrosion rate

26 Statistical analysis Limited Coverage Statistical methodology allowestimates of condition in areas not inspected e.g. part failure of pigging tool,restricted access, external measurements only Extreme value analysis when degradation present Compliance inspection when degradation not expected low coverage, high sensitivity Use simulation techniques for planning of inspections with limited coverage e.g. unpiggable lines

27 Benefits of Sonomatic s Statistical Analysis of ILI Data Examinesdata set as a whole Both individual data and matched data sets Embraces error analysis Derives better understanding of current state More robust prediction of future behaviour Gives operators an improved understanding of actual line condition Allows more reliable and cost effective decisions to be made But only if you love your errors!