Determining Corrosion Growth Accurately and Reliably

Louis Fenyvesi TransCanada Pipelines

Calgary, Alberta

Scott Dumalski BJ Pipeline Inspection Services

Calgary, Alberta

ABSTRACT

High resolution Magnetic Flux Leakage (MFL) inspection tools are very adept at showing the current static state of corrosion in a pipeline. A single inspection shows only a partial reflection of the actual corrosion activity. Multiple inspections and an accurate corrosion growth analysis are required to have a more complete understanding of the corrosion activity. It should then be possible to find features growing at a potentially critical rate and to differentiate between areas of new growth and areas of continued growth. The principal advantage of this is to more accurately predict future corrosion growth in a line. This paper is a highlight of two corrosion growth analyses and a method that has been developed to provide a more complete understanding of corrosion activity and growth. The method that has been developed enables a minimization of the primary errors involved in a corrosion growth analysis which has been through the usage of the raw signal.

INTRODUCTION The knowledge of the rate at which corrosion grows in a given pipeline is key to determining the optimal time between MFL inspections, finding hot spots of high corrosion growth, and possibly preventing catastrophic failure of the pipeline. BJ Pipeline Inspection Services inspected a 36 inch pipeline in 2003 for TransCanada PipeLines (TCPL). This inspection was performed five years after a previous inspection of the same pipeline. At the request of the pipeline operator, a corrosion growth analysis comparing the two inspections was carried out. The first calculation method that was utilised to estimate corrosion growth rates involved analysing the original reported box data (length, width, depth) for each inspection. Even though both sets of data were accurate [1], it was found that the results from this method were dependent on sizing methods and models that had been changed due to improvements over time. The second calculation method involved comparing certain aspects of the raw signal data collected by the MFL tool. Utilizing the raw MFL signal data between inspections has

led to improvements in accuracy and has dealt with many of the short falls of the simple ‘box’ matching method. This method significantly reduces errors in the analysis and brings this type of calculation to a higher level of accuracy and reliability. To date, 300 km of pipeline have undergone a growth analysis involving 36 and 42 inch pipelines. Corrosion growth rates have been calculated for the complete dataset totalling over 90,000 features.

ERRORS AFFECTING CORROSION GROWTH ACCURACY There are several challenges in producing an accurate corrosion growth analysis. They vary from simple technical difficulties to a broad range of possible errors [2]. It has been the industry momentum to deal with some of these errors by moving corrosion growth analysis from simple box-to-box comparison to a method aided by the comparison of the raw signal itself. Irrespective of the method, it is the errors that are introduced into the analysis that inhibits the accuracy of a corrosion growth rate analysis. The primary errors [3] in an analysis assisted by the comparison of the raw signal are due to differences between the two inspections which generally include: Differences in the Inspection Tools To perform a growth analysis assisted with a signal comparison, the differences between the inspection tools has the potential to dominate all other sources of error. These differences can directly alter the observed magnetic flux leakage signal of a feature, even one that has experienced a negligible amount of growth. If the magnetics of the inspection tool has changed significantly between inspections, features need to be scaled in order or to make a reasonable comparison. A corrosion growth analysis must then be based upon the comparison of raw MFL data to the scaled MFL data. Differences between Defect Sizing Models/Algorithms

In general, sizing algorithms will have evolved over time resulting in an improvement in the algorithm between the two inspections. One possible error resulting from different sizing algorithms is due to the fact that sizing algorithms are generally not as optimized for shallow defects as they are for deep defects. This may lead to the possibility of one sizing algorithm over or under estimating the depth of these shallow defects compared to the other algorithm. This will lead to a systematic error in the analysis. This error may show growth in feature depth where there has been little to no growth or show no growth where there has been growth.

Differences in Analyst Sizing Methods

The method of sizing is the manner in which the features’ parameters are adjusted before

being passed to the sizing algorithm. The sizing of features can introduce error in two separate ways. In general, it is unlikely that two or more analysts will size a feature in the same exact manner. More importantly, sizing of features has evolved for the entire industry. Large areas that were previously sized as one feature are now more commonly broken into many separate features [4]. By basing a corrosion growth analysis on original box-to-box data the sizing method error alone will introduce error in the growth of new features.

Although not a comprehensive list of errors, these represent the primary sources of errors in this type of corrosion growth analysis. It is this list of errors which we attempted to control in this analysis. The combination of these errors can give poor results when reporting the growth of corrosion. In the past these errors had not been readily observed or addressed due to the difficult nature of verifying growth rates, as compared to defect depth.

MANAGEMENT OF CORROSION GROWTH ERRORS

It is the management of the errors which are introduced into a corrosion growth analysis that limits the analysis accuracy, reliability and usefulness. The errors in growth rate and growth rate distribution can be as serious as under estimating the depth of a deep feature. In any case, there can be a false sense of safety held for the future integrity of a pipeline.

To perform a growth analysis aided with the comparison of the raw signal, the inspection tools used for the two inspections need to be magnetically similar or a scaling of the features is required. For this analysis, there is no need to scale the features. Even though many elements of the tools have improved over time, they are nearly identical magnetically to earlier tools. As a result, there is no need to attempt to base a corrosion growth analysis on the comparison of scaled to non-scaled features. The error introduced by using different sizing models can be addressed by resizing initial inspection features with the current and most accurate sizing algorithm, which is the same sizing algorithm that was used on the 2nd inspection. Errors introduced as a result of different analysts, and more significantly between pipelines inspections, can be addressed by using automatic feature detect on both lines.

Although we have found ways to address the identified primary errors, we have also found methods to handle other possible sources of error in this type of analysis. Automatic feature matching of a large number of features can be problematic. Two solutions are to take a sampling of features to represent the entire line or to develop a corrosion matching algorithm. Either method can be a source of error as they can affect the overall growth rate distribution and/or individual growth rates. Due to the accuracy of our inertial system and inertial processing [5], feature matching is a simple process. Features are not matched by a feature matching algorithm but by simply matching features by chainage and clock position. Another predominate error of calculating corrosion growth is the assumption of feature development time of new features. Collection of the entire MFL dataset allows the separation of features that developed after the first inspection from features that existed previously but did not meet the reporting threshold. It is a separation of features where there is confidence in the growth rate from features that may potentially have a much higher growth rate. If access to the raw MFL data is not possible, a corrosion growth analysis must be based upon a simple box-to-box comparison from old to new data. This corrosion growth analysis methodology cannot account for tool variability, sizing algorithms or sizing method.

CORROSION GROWTH ANALYSIS METHODOLOGY

The corrosion growth analysis methodology that has been developed, attempts to minimize the most significant errors involved in a growth analysis. To remove analysts’ bias and sizing algorithm error, it is necessary to resize the original inspection data set. There are two separate ways to resize the original data.

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I. The original inspection data can be treated as any other line and have analysts size the data. This method would produce two recently sized datasets using the current sizing algorithm. This method would also be fairly slow and still contain some analyst bias.

II. Automatic feature detection can be utilized on both lines. This would produce an unbiased sizing using the current sizing algorithm. This would have the additional benefit of being relatively fast.

The method using automatic feature detection has been chosen in this analysis to minimize bias

between the two inspection data sets. Feature detection does have some limitations. It does not fully enclose the full length or width of area containing complex features. It misses some shallow features and produces a number of false feature identifications. The number of shallow features is relatively minimal and not overly important, thus can be ignored. The fact that feature detection does not always fully define the length or width of all defects is not an overly significant factor for a corrosion growth analysis as it pertains to a minimal number of features. The extra false features that feature detection can produce is a very serious problem that must be addressed so they can be removed or, at least, greatly reduced by filtering out these features. The goal of the filtering is to eliminate the effects of false features that feature detection produces. A two stage filter has been devised. The first stage is a passive pre-filter that goes though the entire data set and removes obvious false features that feature detection may produce. The second stage of the filter makes use of the chainage and clock position of every feature in the analyst sized dataset and creates a box around that feature. This box is superimposed on the two feature detected datasets and all features that fall within the box are then passed to another series of tests to ensure it is comparable to the reference feature after taking into account the tool tolerance. Figure 1 shows the depth distribution of the feature detection data. It clearly shows the limitations of feature detection as there are more deep features in 1998 than in 2003. Figure 2 shows the feature detection data after the filtering process. It is clearly more reasonable and gives confidence in the filtering process.

Figure 1: Depth distribution of the raw feature detected data for the two inspections

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Figure 2: Depth distribution of the raw feature detected data for the two inspections after the filtering process

REANALYZED INITIAL INSPECTION DATA COMPARISON

Sizing of features has changed significantly in the pipeline inspection industry. In general, more features are sized [4] and large areas are broken into more individual features. Table 1 summarizes the number of features that were reported in the first two runs.

Table 1: Number of features compared for the reanalyzed data

From Table 1, it is clear if one were to perform a corrosion growth analysis based on the original number of features that the analysis would be seriously flawed. It would report an increase in the number of features far in excess of the real growth in the number of new features.

Number of Original Features

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36” inspection 1,084 25,292 42” inspection 1,182 13,119

SEVERITY RANKING OF FEATURES WITH SIMILAR GROWTH RATES

There is the potential in any growth analysis to incorrectly report the growth rate of a feature. Due to this potential, it is possible that defects with similar reported growth rates have growth rates that are drastically different.

The source of this potential discrepancy is due to the assumption of when the feature began to develop. Having collected complete datasets for all the MFL inspections, it is possible to separate out these defects. Figure 3 shows a defect that has grown to 45% of the wall thickness between the two inspections. Even though the area was not sized in the initial inspection, it is clear that the feature began to develop before the initial inspection. Figure 4 shows no feature in the first inspection implying the growth occurred at some point in time after the first inspection. This feature may have begun to develop right after the inspection or at any time in between the two inspections. The features in Figures 3 and 4 will be reported to have similar growth rates. The feature in Figure 4 is far more serious. It is unknown when the feature initiated, thus there is no upper bounds on the growth rate of the feature. This distinction of the severity of these two features is only possible due the collection of the complete MFL dataset for both inspections.

Figure 3: Screen caption showing area where a feature exhibits significant growth. The feature existed in 1998 but not sized.

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Figure 4: Screen caption showing area where a feature exhibits significant growth. Feature did not exist in 1998.

CORROSION GROWTH RESULTS

In a corrosion growth analysis many parameters may be reported that are useful to understand the activity in a given line. Figure 5 shows the number of corrosion per chainage at a binning of 1000m. The green line represents the 2004 data and the blue line is the 1998 data. From Figure 5 one can clearly see the areas that are highly corroded and areas containing significant corrosion growth.

Figure 5: Number of Corrosion per Chainage (1000m intervals)

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One of the goals of our growth analysis is to find areas which might affect the integrity of a pipeline. This can be achieved by analysis of a number of parameters such as growth of new corrosion or growth of peak/average depths. Since there are a large number of parameters to analyze, it is convenient to combine several of these parameters into an ‘Area of Interest’ factor. There were two ‘Area of Interest’ factors created. One is tailored to point out possible areas were pipeline integrity is an immediate concern. The other is tailored to indicate areas which are not an immediate concern but an area of possible future concern. Figure 6 shows the result of the ‘Area of Interest’ calculation that points out areas of possible future concerns. Since the result is a combination of several parameters, the scale of the peaks may not be overly important. It is meant to merely identify these locations rather than to rank their severity of location.

Figure 6: Area of Interest - Future (1m intervals) The area of pipe that corresponds to the tallest peak in Figure 6 is shown in Figure 7. The area has no significant features in terms of depth. The number of features and the number of new features is also not overly significant compared to other areas of the pipe. The combination of these parameters has been used to create this specific ‘Area of Interest’ parameter. It has clearly indicated an area of future concern which was not identified by any other single parameter.

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Figure 7: Screen caption showing an area picked out by Area the Interest -Future plot

The actual data being displayed in Figure 7 is called a differential sensor view. It is a combination of the output of the radial, axial, and circumferential sensors and closely represents the total magnetic flux leakage vector. The darker the red indicated more magnetic flux has moved out of the pipe wall. The vertical red lines are girth welds and the scattered red spots are locations of corrosion. The boxes indicate where corrosion has been identified and sized.

GROWTH RATE DISTRIBUTION

We have successfully been able to automatically match the complete set of defects between the two inspections without the need for a sophisticated matching algorithm. The matching of features is a simple process due the accuracy of the inertial system and inertial processing [5]. Features are easily matched using chainage and clock position and not by a feature matching algorithm.

Figure 8 show the growth rate distribution for the ~35,000 features in a 42 inch pipeline. There are two distributions displayed in Figure 8. The distribution on the left is a distribution resulting from features that have experienced little to no growth. This distribution will be discussed in the next section and is a natural result of tool tolerances and is vital to understanding the error in growth rate of individual defects. The distribution on the right is the growth rate distribution resulting from the growth of features. Figure 9 shows the same distribution as in Figure 8 only showing features with growth rates greater than 0.4mm/yr.

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Figure 8: Distribution of Growth Rates –Feature Detected Data

Figure 9: Distribution of Growth Rates –Feature Detected Data (0.4mm/yr and greater)

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GROWTH RATE ERROR ANALYSIS

There are several methods that can determine error for the calculated growth rate of a feature. The solution that will result in the highest confidence is using several methods to verify consistency among each other. The first and most simplistic method is a simple error propagation based upon the reported tool error. If one assumes the reported specification of the depth of +/-10% with 80% confidence, a simple calculation can give some insight into the size of the error.

Growth Rate = (DepthInspection2 – DepthInspection1)* WT / NofY [1]

Where WT is the wall thickness and NofY is the number of years between inspections

Absolute Growth Rate Error= (Depth ErrorInspection2 + Depth ErrorInspection1)* WT/ NofY [2]

In general the probabilistic error gives a more realistic error. Probabilistic Growth Rate Error= SQRT((Depth ErrorInspection2)2 + (Depth ErrorInspection1)2)* WT / NofY [3] Using the assumed tool tolerance, equation [3] gives a probabilistic error of .24 mm/yr. Substituting a value of 0.078 for 0.1 to represent the standard deviation corresponding to 0.1 error with 80% confidence gives a probabilistic error of .19 mm/yr. These values can be verified at the same time giving proof for the assertion that first distribution in Figure 8 is due to tool tolerance of features experiencing little to no growth. Figure 11 shows two simulated distributions of a set of features that have exhibited no growth. They differ only by a probabilistic distribution simulating the tool error and resulting depth error.

Figure 11: Two simulated depth distributions

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Figure 12 shows the subtraction and thus the simulated measured growth rate of these two distributions. It is evident that is very similar to first distribution in Figure 8.

Figure 12: Simulated measured growth rate of a set of features exhibiting no growth

The standard distribution of the growth rate shown in Figure 12 is 0.19 mm. This number is an exceptionally consistent error obtained from the simple probabilistic error propagation of 0.19 mm/yr shown. The error propagation method is a simple calculation and generally when there are ample statistics the data should be a better indicator of the size of the error.

It is impossible to have negative growth rates, and it has been shown in the distribution on the left in Figure 8 is a result of the inspection tool tolerances on the growth rate of features that have experienced little to no growth. From this distribution we can estimate the error in the corrosion growth for features that have experience negligible growth. This error can then be applied as a close approximation for the growth rate error of individual defects.

Figure 13 shows the first distribution from Figure 8. It shows the maximum error can be slightly in excess of .2mm/yr in agreement equation [3], but as discussed before the maximum error rarely occurs and is generally unrealistic and not used. The realistic or probabilistic error is considerably less. The standard deviation of the distribution in Figure 13 is approximately 0.06 (mm/yr). Assuming an 80% confidence, similar to the reported depth, would give and error on the growth rate close to 0.1 (mm/yr). This number is significantly lower than the error predicted by either of the first two methods. The size of this error is a result and a validation of the accuracy of the sizing algorithm and the methodology of this growth analysis. The growth rate error, of 0.1mm/yr, can used to calculate the inspection tool sensitivity to defect depth change between inspections. A 10mm wall thickness and the time between inspections of 5 years corresponds to an inspection tool sensitivity to defect depth change of only 5% of the wall thickness.

Distribution of Growth Rates

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Figure 13: First distribution in Figure 8 resulting form tool tolerance from Defects that have exhibited little to no growth

GROWTH RATE DEPTH CORRELATION A correlation between original depth and growth rate would be extremely usefully in predicting future growth rate based upon the observed initial depth. Figure 14 shows the growth rate vs. initial defect depth for approximately 35,000 features of a 42” gas line. With the exception of one feature it appears that deeper features do not grow as fast as smaller features. Similar plots have been used to put upper bounds on the growth of features as a function of depth [6].

In general greater insight in observed quantities and correlations of observed quantities are often obtained though simulations. A recently published paper [7] reports the results of Monte Carlo simulations where different growth models were compared. The paper compares one model where there is a correlation between depth and growth rate, the low growth-rate model, and one where there is no correlation, the stochastic growth model. It is clear from the results that the stochastic growth rate model clearly reproduces observed depth distribution considerably better than the model with a correlation. Making the assumption that little to no depth/growth rate correlation exists allows the possibility of deep features growing at a high rate. This assumption may explain the observed feature in Figure 14 with an initial defect depth of 45% and a growth rate of nearly 0.8 mm/yr. The feature has grown at a rate beyond any likely upper bounds on the growth rate of features as a function of depth for that pipeline.

Based upon the observed data and published simulation results it is reasonable to make the assumption that little to no correlation exists between depth and growth rate. This assumption allows the possibility of deep defect to growth at high rates but these features are rarely observed. This can be attributed to the result of two overlapping probability distributions. The defect depth distribution, for a given pipeline, shows that it is statistically unlikely for a given feature to be deep. A typical growth rate distribution implies it is improbable for a given defect to grow at a high rate. The combination of these two distributions makes it unlikely, but not impossible to observe deep features growing at a high rate. If it were possible to observe the growth rate of a large number of deep features it is likely that a portion would be growing at a high rate.

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INTELLIGENT LINE SEPARATION This analysis has grouped the line in simple (1, 5, 50…) m intervals. Another method is to divide the line into more intelligent bins. This can be achieved by using a regression tree. A regression tree looks at the depth distribution of the complete line and begins by breaking the line into sections. The technique makes the decision where to section the line in an attempt to minimize the standard distribution of each section.

This technique can be used to break the line in any number of intervals. Figure 15 shows a sample separation of the line where the displayed variable x is the chainage.

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Using a minimum split of 5000 features a tree regression can be used to split the line into the segment listed below. The tree was pruned to allow only the following divisions. The divisions are shown approximately in figure 16 which is a semilog plot of the area of interest now similar to Figure 6.

Figure 16: Semilog plot the Area of Interest-Now figure. Showing several regression tree separations (1m intervals)

CONCLUSION

The errors that are introduced into a corrosion growth rate analysis are what limits its accuracy, reliability and usefulness. It has been the industry momentum to deal with some of these errors by moving corrosion growth analysis from simple box-to-box comparison to a method aided by the comparison of the raw signal itself. As with any type of analysis the errors in an analysis aided by raw signal comparison must be managed. The sizing model, method, and tool differences can contribute to significant errors in a corrosion growth analysis. These errors may be relatively unimportant when reporting the static condition of a line but can be very significant in reporting the rate of corrosion growth. Simple box-to-box comparison is not as aptly suited to deal with the many systematic errors. Due to the difficult nature of verifying growth rates as compared to defect depth, these errors are not readily observed or addressed.

In this corrosion growth analysis we have been able to largely remove the effects of the sizing algorithm. The removal of the sizing algorithm effect is a goal of signal to signal comparison to enable an accurate comparison to determine corrosion growth. We have also been able to deal with sizing bias and differences between inspections over time. Using two complete sets of MFL data allows complete analysis of the two inspection data sets making the growth analysis more accurate. The two complete MFL data sets allows severity ranking of key features with similar growth rates by examining if the corrosion existed at the time of the first inspection or was completely new growth. We have been able to identify areas of immediate and future concern. More importantly we have produced an accurate corrosion growth rate for every automatic feature detected corrosion feature. Dealing with factors that introduce error into the analysis leads to a higher standard in accuracy and reliability for the corrosion growth analysis. In addition to increased accuracy of our calculation this method is largely automatic, fast, and reliable, is able to produce growth rates for every automatic feature detected corrosion feature, and produces large amounts of statistical information to enable further analysis and perspective.

REFERENCES

1) S. Westwood, S. Cholowsky, “Independent Experimental Verification of the Sizing Accuracy of Magnetic Flux Leakage Tools”, 7th International Pipeline Conference, Mexico, CIN-042

2) R. Worthingham, L. Fenyvesi, T. Morrison, G. Desjardins, “Analysis of Corrosion Rates on a Gas

Transmission Pipeline”, Pipeline and Gas Technology Magazine, Nov/Dec 2002, pg 45. 3) B. Gu, R. Kania, S. Sharma, M. Gao “An Approach to Assessment of Corrosion in Pipelines”,

IPC 2002 Paper 27243 4) L. Fenyvesi, I. Colquhoun, B. Gu, R. Kania, “A Risk-Based approach to maintenance Planning

Utilising In-Line Inspection Data”, IPC 2004, IPC04-0178

5) J. A. Czyz, C. Cole, “ High Accuracy Pipeline Depth of Cover Survey in Channel Crossing Using Inertial Navigation, NACE International Corrosion/2001 Conference, March 2001, Houston .

6) B. Gu, R. Kania, M. Gao, K. Keith, R. Coote, “Advances in Corrosion Growth Analysis and

Future Assessment of Pipelines”, NACE Paper No. 3180, Corrosion 2003. 7) L. Fenyvesi, H. Lu, T. R. Jack, “Prediction of corrosion defect growth on operating pipelines”, IPC

2004, IPC04-0268