estimation of corrosion growth rates in pipelines rev 4

8/10/2019 Estimation of Corrosion Growth Rates in Pipelines Rev 4

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Determination of Corrosion Growth Rates using Pipeline Operators ForumDefect Classifications

T.Fletcher, F Samie.; Wood Group Integrity Management

Abstract

Over the service lives of pipelines, operators may check for the presence of defects using intelligent pigs. Overtime, defects may corrode and grow in size until they threaten the integrity of the pipeline. Assessing how quickly

defects may corrode can be complex due to various factors including non-linearity in corrosion growth rates,differing corrosion mechanisms for different kinds of defect and differences in inspection tool tolerances. Tosimplify such assessments, a single corrosion growth rate may be applied to all defects in a pipeline; thisapproach is frequently taken as the basis for determining a re-inspection date by intelligent pig. The PipelineOperators Forum provides guidance on classifying defects based on pipeline wall thickness, defect axial lengthand defect circumferential width. Examples include pitting-type defects, general-corrosion-type defects andpinholes. This paper examines the use of these classifications to estimate corrosion growth rates by fitting thedimensions of defects to different statistical distributions. The use of these methods helps to refine estimates ofcorrosion growth rates and refine corrosion management strategies including more accurate intelligent pig re-inspection intervals.

Introduction

Oil and gas pipelines rarely fail, however failurescan and do occur. Statistics compiled byCONCAWE

[1] indicate that mechanical failure,

corrosion and third party activity were the maincauses of oil pipeline failures in Europe since 1971.Of these, 131 (27% of the total) were related tocorrosion. An analysis of United Kingdom onshorepipeline failures by UKOPA

[2] indicate that

corrosion accounted for 39 (21% of the total) loss ofcontainment incidents between 2006 and 2010.

Assessing the degree of corrosion is needed todetermine the condition of a pipeline. Thedimensions of any metal loss corrosion anomaliespresent in a pipeline may be measured usinginspection technologies such as ultrasonic (UT) ormagnetic flux leakage (MFL) tools mounted on pigs.The acceptability of a metal loss corrosion anomalymay be assessed by estimating the stress in itsremaining ligament of pipe wall at given boundaryconditions; such as the pipelines maximumallowable operating pressure (MAOP). If theestimated stress is within specified criteria, then themetal loss anomaly may be considered to beacceptable; otherwise the anomaly is considered tohave failed and require remediation in the form offurther assessment, repair or replacement.Examples of such assessment methodologiesinclude the DNV-RP-F101 Part B Allowable StressDesign

[3]and the Detailed RSTRENG criteria

[4].

Estimating a corrosion growth rate for a pipelineenables such assessment methodologies to beused to make a prediction of the time taken for ametal loss corrosion anomaly to grow in size toreach an acceptable limit. These methodscommonly take either the reported length or widthof an anomaly as constant values and determinethe greatest acceptable depth. The differencebetween the greatest acceptable depth and

reported depth is divided by a corrosion growth rate

enabling an estimate of a failure date to be made.This approach is illustrated inFigure 1.

Predicted failure dates for metal loss corrosionanomalies may be used to prioritise corrosionmanagement measures such as re-inspectionintervals by intelligent pig, physical inspections ofexternal anomalies, use of chemical inhibition andcleaning. Refining such management measurescan result in cost savings to a pipeline operator. Forthis reason, estimating accurate corrosion growthrates makes the prediction of failure dates moreprecise, this in turn helps to lower corrosionmanagement costs.

Corrosion Growth Rate Estimation: CurrentMethods

Corrosion rates can be estimated by assuminglinear growth between two given dates. Whereintelligent pig inspection data exists, the reporteddepth of a metal loss anomaly may be used todetermine a corrosion growth rate. A basicapproach to calculate such a figure assumes thatthe anomalys depth was 0% of wall thickness atthe time of the pipeline installation and grows in alinear manner until the inspection date. Theanomalys depth is divided by the number of yearsof service and a corrosion growth rate figure

determined. Taking the deepest reported anomalyas the basis of determining a single corrosiongrowth rate to apply to the whole pipeline thereforeprovides a conservative value on which to basefailure date predictions.

If a pipeline has been inspected more than once byintelligent pig then the difference in depths for ananomaly at two or more dates may be determined.This difference may then be used to calculate alinear corrosion growth rate. Complexities arise inusing this approach due to accurately matchinganomaly locations between inspections and

accounting for tool types and tolerances.


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Where a known corrosion mechanism exists or isassumed, then a corrosion growth rate may beestimated by applying a corrosion model. Manymodels exist and are generally used in the form ofinputting data describing operating parameters andfluid composition into a software package.Examples include Shells Hydrocorr, BPsCassandra and NORSOKs M-506 corrosion model.

A number of models are in the public domain and

may be freely downloaded from the internet; manyof these are based on the work of deWaard et al[5,

6, 7].

Large differences in estimated corrosion growthrates may be obtained when comparing differentmodels; particularly at high operating temperatureswhere protective film formation may occur on theinner pipe wall.

Anomaly Classification

The Pipeline Operators Forum provides guidanceon the classification of metal loss anomalies basedon pipeline wall thickness, anomaly width andanomaly length. An illustration of the algorithm usedto classify anomalies is shown in Figure 2

[8]. The

following seven anomaly dimension classes arespecified:

Circumferential Slotting

Circumferential Grooving

General

Pitting

Axial Grooving

Axial Slotting

Pinhole

Each of these encompasses a range of differentshapes. Although the classification of anomaliesusing this algorithm may be considered arbitrary,anomalies classified as the same type may beassumed more likely to share similar corrosionmechanisms than anomalies of differing types.

Anomaly Distributions

It can be useful to inspect the depths of eachanomaly type by plotting their reported depths usinga histogram. Two examples of these are shown inFigure 3; one for general type anomalies and onefor pitting type anomalies. Note thatFigure 3 plots

frequency density as opposed to frequency on they-axis (frequency density is defined as frequencydivided by class width). A subjective inspection ofsuch histograms enables inferences to be maderegarding the likelihood of an anomaly having adepth greater than a given value, and by extension,a corresponding corrosion growth rate.

Figure 3 shows that the distribution of depths fordifferent anomaly classes may be dissimilar: in thisexample, pitting type anomalies have a distributionthat is skewed towards shallower depths, whereasgeneral type anomalies have an approximately

symmetrical shaped distribution. In this case, afterinspecting the reported depths on the x-axes of

both plots, we could infer that general typeanomalies have a higher mean corrosion growthrate compared to pitting type anomalies.

The method presented in this paper describes aquantitative method for estimating corrosion growthrates by analysing the shape of these anomalydepth distributions.

Probability Distribution Fitting

In statistics, a probability distribution describes theprobability of each outcome in a sample; forexample, the probability of scoring 5 heads whentossing a coin 10 times. The outcomes of randomlytossing a coin and recording the number of headsand tails obtained can be modelled by the widelyrecognised normal distribution with its characteristicbell shaped curve. There are many other types ofprobability distributions that are used to modelphysical phenomena; examples include the Weibulldistribution, commonly used for reliabilityengineering applications; or the gamma distribution,commonly used in weather forecasting.

Distribution fitting is defined as the process of fittinga probability distribution to variable measurementdata. The purpose of modelling a data distribution isto allow the estimation of the likelihood ofoccurrence at a given interval. For example,modelling anomaly depth distributions from an in-line inspection enables the probability of ananomaly type with a given percentage depth to bedetermined.

Two main techniques exist for modelling datadistributions. The first uses methods that evaluatethe parameters that describe a distributions shape ;for example, the mean and standard deviation of anormal distribution. The second uses regressionmethods; these apply mathematical transforms tolinearise the cumulative probability of a data set.

Many open source and commercial softwarepackages exist that enable users to easily applythese kinds of methods. Two examples of modellingusing parameter evaluation are illustrated inFigure4. For these figures, a random sample of 100normally distributed data points are plotted as twodensity histograms and a normal distribution modelis fitted to each plot. In this case, the parameters

describing the distribution of the data were known,having a mean of 5 and a standard deviation of 2.These values correspond to the estimated mean of5.2 and standard deviation of 1.77 for the redNormal distribution curve approximation.

Goodness of Fit

In statistical modelling, goodness of fit measureshow well a model describes a set of observed data.Determining how accurately distributions modeldata sets provides a measure of reliability.

Goodness of Fit by Eye

The two examples inFigure 4 show histograms ofidentical normally distributed data plotted using


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different frequency intervals (bins). In each case,inspecting the normal distribution goodness of fit toeach data set by eye appears to give a good match.However, the goodness of fit appears to be differentfor each plot. This is because, in each case, adifferent number of bins are used to plot thehistogram; even though the data sets are identical.Changing the number of bins of a density histogrammay change the apparent shape of the distribution.

For this reason subjectively fitting a probabilitydistribution to a histogram by eye can bemisleading and lead to inaccurate probabilityestimates. To help improve the accuracy of curvefitting, probability distributions may be fitted todensity plots rather than histograms as their shapesare constant (two examples of density plots areshown inFigure 5 along with probability distributionfits).

Goodness of Fit by Testing

Whilst assessing the goodness of fit of a probabilitydistribution by eye provides a useful indication of

fitting accuracy, it does not provide any quantitativemeasure. Statistical hypothesis testing enables theaccuracy of a goodness of fit to be determined.Generally, hypothesis testing for modelling worksby comparing the differences between the modelsexpected values and the data distributionsvalues.These differences are represented as a singlevalue known as a test statistic. If the probability ofobtaining this value (commonly called the p-value)is greater than the test statistic, then the hypothesisthat the model describes the data distribution maybe accepted. Otherwise it may be rejected.

There are a number of tests that may be used toassess how well a model fits a data distribution.The main hypothesis testing method presented inthis paper is known as the Anderson-Darling test

[9].

To apply this test the following procedure is used:

1. State a null hypothesis, H0that the data follow astated distribution.

2. Calculate the test statistic,A2using:

Equation 1

where:

= number of data pointsand:

Equation 2

= []

= cumulative distribution function of thestated distribution

= Data point (from ordered data)

3. Compare the test statistic with a critical value(tables of critical values are available for manydistributions such as normal, Weibull,exponential and gamma). The critical value usedis normally quoted at a given significance level(). Commonly used significance levels are 0.05(representing 5%) and 0.01 (representing 1%).

4. If the test statistic is smaller than the criticalvalue the null hypothesis is accepted, otherwise

it is rejected.

Determination of Depth Probability

In general terms, for a continuous probabilitydensity function, ) the probability that liesbetween two values, and is given by thefollowing relationship:

Equation 3

[ ]

The integral of the function is equal to one, i.e:

Equation 4

Applying these relationships to the anomalyclassifications discussed above means that theprobability of an anomaly having a depth of lessthan a given value can be determined with a certainconfidence level.

Confidence Intervals

A confidence interval provides a range betweenwhich a population parameter is likely to fall. Thelimits of a confidence interval show the upper andlower boundaries that define the range. Forexample, if a corrosion growth rate for internalgeneral type anomalies was calculated as 0.25mm/yr at a 95% confidence level; then the actualrate may be greater or less than this value. Aconfidence interval provides a way of showing howlikely a corrosion growth rate is to lie between twogiven values.

Confidence levels are commonly calculated so thatthe likelihood of the population parameter fallingwithin the given range is 95% (however, they canbe calculated at any given percentage). Using thisfigure for the example above and assuming a lowerlimit of 0.24 mm/yr and an upper limit of 0.26 mm/yrwould result in a corrosion growth rate of 0.25mm/yr 0.01 at a 95% confidence level.

Calculating confidence intervals is straightforwardfor distributions that are approximately normallydistributed and is commonly an in-built function for

many statistical software packages. Generally, lowsample sizes result in large confidence intervals


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and large sample sizes result in small confidenceintervals. This means that calculating a corrosiongrowth rate for a small population of anomaliesresults in a larger range of possible valuescompared to a large population of anomalies.

Calculating a confidence interval for someasymmetrical probability distributions such as theWeibull distribution may become complex.However, approximations may be used based on

the normal distribution or numerical methods [10, 11].

Discussion

The depth distributions of anomaly classes may bemodelled using probability distributions. Doing soenables the probability of an anomaly having adepth greater or less than a given value to bedetermined for a given confidence level.

Assuming a linear corrosion growth between twodates (for example, the installation and inspectiondates) enables depth probabilities to be used toestimate corresponding corrosion growth rates.

In general, lower test statistic values are calculatedfor anomaly classes with large populations. Thismeans that it may be impractical to successfullymodel an anomaly class with a large populationusing a probability distribution. In practice,sectioning a pipeline by distance or clock positionmay be used to subdivide anomaly classes intosmaller populations. Doing so enables successfulmodelling to be carried out using an array ofprobability distributions; however, this techniquecan result in large numbers of corrosion growthrates being estimated for a single pipeline. Whilstapplying these to remnant life calculations maybecome complex, doing so results in more refinedestimates being made.

Modelling anomaly class populations is based onthe results of in-line inspections by intelligent pig.The reported anomaly dimensions from theseinspections are themselves subject to tooltolerances and have a degree of error. In the caseof magnetic flux leakage inspections, anomalydimensions may be subject to a degree ofinterpretation by trained personnel. The methodpresented in this paper makes no allowance forthese issues, for this reason, any tolerances quoted

by an inspection tool vendor need to be included inthe classification of anomaly types.

In addition to the inspection tool tolerancesdiscussed above, the dimensions of metal lossanomalies reported by in-line inspections arenormally provided at a given confidence level. Thismeans that the length, width and depth values of ananomaly may be greater or less than the reportedfigures. The axial and circumferential positions ofanomalies reported by intelligent pig are alsosubject to error due to pig slippage. These pointsillustrate that there are uncertainties in measuring

any factor describing an anomaly characteristic. Byextension, these uncertainties also apply to the

calculation of a corrosion growth rate. Theexamples provided in this paper are calculatedusing a 95% confidence level.

Estimating corrosion growth rates using aprobability distribution fitting technique isnecessarily based on historical inspection data. Anypredicted corrosion growth rates therefore make theassumption that the past operation of the pipeline isapplicable to the future. Whilst this may in many

cases be a safe assumption to make, it may not bein all cases. The technique makes no allowancesfor any differences in future operation: for examplechanges in production fluid composition, chemicalinhibition or cleaning pigging frequency. However,given this limitation, the technique does allow analternative and more refined method of estimatingcorrosion growth rates and measuring theeffectiveness of corrosion management systems.

Conclusions

A new method of estimating corrosion growth ratesfor metal loss anomalies in pipelines has beendeveloped based on POF classifications andprobability distribution fitting.

The method can be used to determine a corrosiongrowth rate for different types of metal loss anomalyin different sections of a pipeline at a givenconfidence level and confidence interval.

A detailed assessment of corrosion growth is madepossible by estimating a range of corrosion growthrates based on the results of in-line inspection data.

A comparison of these corrosion growth rates afterany additional in-line inspections enables theeffectiveness of any corrosion managementstrategy to be evaluated in more detail compared tousing a single corrosion growth rate figure.

Example

The following example illustrates the application ofprobability distribution fitting. The data used in thisexample is taken from a hypothetical subsea crudeoil pipeline with internal corrosion. The corrosionwas considered to be caused by water drop out dueto an inefficient separator. Although the followingdata is hypothetical, this example is based on anumber of real life examples.

In-line inspection data from the first 500 m sectionof heavily corroded pipeline was analysed andclassified using the POF algorithm illustrated inFigure 2. For illustration, only general and pittingtype anomalies are analysed in this example andno subdivision by clock position or spool isconsidered. 155 general type and 57 pitting typeanomalies were classified.

The population of 155 general type anomalies werefound to have a good fit by eye to a Weibulldistribution with a shape and scale parameter of2.65 and 5.36 respectively. Similarly, the population

of 57 pitting type anomalies was found to have agood fit by eye to a gamma distribution with a


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shape and rate parameter of 4.00 and 1.27respectively. These results are illustrated in Figure5.

The goodness of fit of each model was testedagainst each data distribution. The results of thesetests are shown inTable 1.

Table 1

AnomalyClass

NullHypothesis

TestStatistic

p-value

General Weibull 0.34 0.90

Pitting Gamma 0.19 0.99

As the test statistics were lower than the p-values ineach case, the null hypotheses that the generaltype anomalies were drawn from a Weibulldistribution and the pitting type anomalies weredrawn from a gamma distribution were bothaccepted.

The Weibull and gamma models were used toestimate a depth for general and pitting typeanomalies at a 95% confidence level. This processis illustrated graphically inFigure 6.This resulted ina depth of 8.1 mm 5.4% for general typeanomalies and 6.1 mm 11.0% for pitting typeanomalies. The larger confidence interval for pittingtype anomalies reflects a lower sample size (n =57) compared to general type anomalies (n = 155).

Assuming that these anomalies had zero depth atinstallation then a linear corrosion growth ratecorresponding to the above depths could beestimated based on the in-line inspection date andused to predict the failure dates for theseanomalies.

In practice, the example described above wouldinclude an analysis of all anomaly types and includean investigation into the effect of sectioning thepipeline by distance and clock position.


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References

1. P.M. Davis, J Dubois et al. Performance of European cross-country oil pipelines. Statisticalsummary of reported spillages in 2010 and since 1971. CONCAWE, Brussels, Belgium,December 2011;

2. R.A McConnell, Dr J. V Haswell. UKOPA Pipeline Product Loss Incidents (1962-2010).UKOPA/11/0076, Ambergate, UK, November 2011;

3. DNV Recommended Practice, DNV-RP-F101, Corroded Pipelines, October 2006;4. PRCI Report PR 3-805, A Modified Criterion for Evaluating the Remaining Strength of

Corroded Pipe, 22 December 1989;5. C. deWaard, U. Lotz, D.E. Milliams, Paper 577, CORROSION/91, 1991. Predictive Model

for CO2 Corrosion in Wet Natural Gas Pipelines;6. C. deWaard et al., Paper 69, CORROSION/93, 1993, Prediction of CO2 Corrosion of

Carbon Steel;7. C. deWaard et al., Paper 128, CORROSION/95, 1995, Influence of Liquid flow Velocity on

CO2 Corrosion: a Semi-empirical Model;8. Anon, Specifications and requirements for intelligent pig inspection of pipelines. Version

2009, Pipeline Operators Forum, 2009;9. Stephens, M.A. EDF Statistics for Goodness of Fit and Some Comparisons, Journal of the

American Statistical Association, 69, pp.730-737, 1974;10. Hong,Y, Meeker, W, Escobar, L. Normal Approximations for Computing Confidence

Intervals for Log-Location-Scale Distribution Probabilities, Iowa State University,Louisiana State University, June 2006;

11. Symynck, J, De Bal, F. Monte Carlo Pivotal Confidence Bounds for Weibull Analysis withImplementations in R,The XVI

thInternational Scientific Conference, Tehnomus, Romania,

May 2011.


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Figures

Figure 1

Corrosion Growth Required to Cause Failure

Figure 2

Illustration of Pipeline Operators Forum Metal Loss Anomaly Classification Algorithm

Feature Depth

Increase in Depth Required to Cause Failure

0

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8

CircumferentialSlotting

Axial Slotting

CircumferentialGrooving

Axial GroovingPitting

General

Pinhole

L/A

W/A

If Wall Thickness < 10 mm, A = 10 mm

If Wall Thickness 10 mm, A = Wall Thickness

W/A =Anomaly Width / A


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Figure 3

Density Histograms of General Type and Pitting Type Anomaly Depths

Figure 4

Distribution Fitting


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Figure 5

Density Plots of General Type and Pitting Type Anomalies with Probability Distribution Fits

Figure 6

Depth Comparison of General and Pitting Type Anomalies at a 95% Confidence Level

estimation of corrosion growth rates in pipelines rev 4

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