29th Pipeline Pigging & Integrity Management Conference, Houston, 1-2 March 2017 Automated signal comparison and normalization - an advanced method of comparing repeat ILI data Johannes Palmer, ROSEN Group, Lingen, Germany Artur Miller, ROSEN Group, Lingen, Germany John Knudsen, ROSEN Group, Lingen, Germany 1 Abstract Pipeline integrity planning requires reliable corrosion prognosis. Both under and over-estimation of corrosion growth can have significant consequences. Depth differences from two consecutive ILI runs help in this regard, but individual values may suffer scattering and increase repair costs. Such costs can be reduced by intelligent averaging, which may suppress individual aggressive anomalies. Whichever strategy the individual optimization process of an integrity analysis prefers between the conflicting priorities of accepting either individual or generalized errors, the highest quality ILI input brings significant benefit in any case. The paper describes effects such as decreasing probability of detection with increasing growth rate and quantifies the numerical benefit of MFL signal comparison compared to sizing box matching. The modern computer algorithmic methods are elaborated, which were applied to overcome these obstacles by allowing for high coverage and high accuracy automation of ILI depth difference calculation. The result is a manually validated and corrected individual ILI depth difference value for each anomaly. 2 Introduction Future corrosion growth rate is a vital input factor for pipeline integrity management planning. Underestimation of the growth of corrosion anomalies may lead to inappropriate mitigation actions and associated risks, while exceedingly conservative estimation of the growth can lead to unnecessary inspections and significant mitigation costs. Therefore, highly accurate ILI depth difference values are important and valuable input to integrity assessments, improving the Corrosion Growth Rates (CGR) and subsequent conclusions. A widely-accepted approach is the evaluation of the depth difference between features reported by two consecutive ILI runs. These two observations are simply describing two points in time, not necessarily the processes between and after these observations. They have to be considered accordingly, ”Although the application of linear growth rate calculated by comparing depths from two successive ILI is a common practice in the pipeline industry, research has shown that the growth of corrosion anomaly is non-linear and anomaly-specific.” [Al-Amin et al., IPC, 2016]. Nevertheless, consecutive ILI information is vital, as it is often the only available and also a valid input to understand more complex processes. However, the normal tolerances of the most frequently applied ILI technology, the Magnetic Flux Leakage (MFL), have the same order of magnitude as depth changes that may be caused by active corrosion. Therefore deriving individual or generalized growth rates suggests a deeper analysis than just comparing box entries in two independent ILI sizing tables.

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3 Motives The numerical corrosion growth analysis is affected by sizing tolerances of comparable scale as the desired output result itself. Thus, a complex scenario is created, where individual and statistically generalizing approaches have to be optimized, taking into account the individual circumstances of a pipeline and the available inspection data. This is done by integrity and corrosion specialists. The opposite aspects can be illustrated by two citations from 2016 IPC, both of which are correct in principle: • On the one hand, there is the need for statistical generalization in order to reduce individual errors,

“It is a common misconception that growing each feature at its individually calculated CGR will provide the most accurate corrosion growth predictions. The influence of sizing errors and the potential for matching errors are amongst many reasons why this approach is inadvisable.” [Smith et al., IPC, 2016]

• On the other hand, there is the necessity to optimize mitigation costs against risks, “When a segment-specific growth rate is applied to all anomalies, the majority of the anomalies that are hardly growing are grown at rates higher than the actual rates; thus, leading to many unnecessary future integrity excavations. On the other hand, the growths of the aggressive anomalies are under-estimated, which may lead to pipeline failure.” [Al-Amin et al., IPC, 2016]

Whichever strategy the individual optimization process of an integrity analysis prefers between the conflicting priorities of accepting either individual or generalized errors, the highest quality ILI input brings significant benefit in any case. 3.1 Detection Performance The numerical analysis of ILI metal-loss box report-table populations is highly developed. With regards to Magnetic Flux Leakage, the additional benefits of directly comparing MFL signals have been known for many years. This can eliminate interpretation imprecision and fine-tune calibration bias [Dawson et al.,

PRCI, 2009]. Nevertheless, the signal-based approach is more labor intensive than simple feature box matching. By nature, box matching allows for multiple and weakly defined links [Figure 5], while signal comparison requires unique, unambiguous correlation, which can create significant manual work requirements. Focusing this effort on suitable sub-populations requires robust selection criteria, which allow for identification of single aggressive anomalies a.m. The clearest selection criterion is the feature depth, which is a criticality-related characteristic anyway. But as exemplified [Kariyawasam et al., ASME, 2009] in Figure 1, increasing depth is not necessarily an indicator of increasing CGR.

Figure 1 – Depth Growth Rate vs. Depth [Kariyawasam et al. , ASME, 2009]

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The implicit consequence of this type of distribution becomes evident when remapping (Figure 3) a similar Monte Carlo test population vs a typical MFL – ILI POD sensitivity curve as shown in Figure 2.

Figure 2 – Exemplary Growth Rate decreasing with depth and typical ILI-MFL POD curve.

The consequence shown in Figure 3 is an apparently decreasing POD with increasing growth rate, which implicitly suggests to improve the selection criteria or increase the coverage of the individual growth rate assessment, i.e. lowering the threshold.

Figure 3 – Remapping Figure 2 Growth Rate distribution vs. POD

This observation additionally confirms the benefit of a signal-based investigation, because the re-identification of smallest signals in the old data set, even below the noise level, is possible in the measurement data, but not in the old box tables. 3.2 CGR Accuracy After recognizing the benefit of a proper and perhaps high CGA sensitivity, the expected accuracy of the CGR itself shall be examined. Normalization With time, also calibration procedures and algorithms are changing. This affects tabled ILI boxes implicitly and cannot be corrected easily. With the measurement data, this is an easier task. Primal measurement data are archived and can be treated exactly following the recent procedural sequence, which eliminates this error source. Figure 4 shows the results of this process.

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Figure 4 – Normalization of raw uncalibrated measurement data of previous and recent data set with the same, recent algorithms.

Data interpretation MFL magnetic field disturbances can be ambiguous. Experience helps to sort out most probable and accurate solutions, but the need for interpretation remains. For example, as shown in Table 1, the circumferential groove in Figure 4 may consist of one larger, shallower (interpretation A) or two small deeper (interpretation B) metal loss structures.

Interpretation Metal Loss 2008 Metal Loss 2014

A 26%t 35% t

B 34% t 46% t

Resulting σ ±5.7 ±7.8

Table 1 – Different interpretations of a complex metal loss signal The principal difference between box matching and signal comparison is that generally only one interpretation can apply to the signals, while independent tabled box interpretations can differ. This is shown in Table 2.

Possible Matches 2008-2014

Depth Difference [%t] possible with signal matching

Depth Difference [%t] possible with box matching

AA 9 9

BB 12 12

AB - 20

BA - 1

Resulting σ ±2.1 ±7.9

Table 2 – Example of principal difference between box and signal matching and resulting higher accuracy of depth difference from signal matching

This simple example gives rise to two remarkable observations: • The standard deviation (σ) of the depth difference of the signal matching is much lower than the

box matching.

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• The standard deviation (σ) of the depth difference of the signal matching is lower than both sizing tolerances itself.

This can be easily clarified when estimating the contributing error components by dividing them into two relative error components, i.e. the interpretational aspects and the signal and data related repeatability, which is principally valid for MFL. The resulting formulae are outlined in the Addendum (Section 5) “Error Estimation of Box vs. Signal Matching Depth Difference”. They show the need for high signal repeatability, which is one of the major design targets of modern ILI tools and therefore typically significantly lower than the interpretational scattering. The formulae hence show that the box matching depth difference accuracy is below the depth sizing itself, whilst the signal match provides significantly better values. Also, it becomes evident that box matching is typically not suitable for individual growth assessments whilst signal based methods may allow for these. The Addendum outlines an example using typical interpretation variability and signal repeatability. The example feature of 50%, with 40% at the earlier ILI, has an absolute sizing error in the order of 6%, with subsequent box matching absolute depth difference error of ±10%, compared to ±3.4% based on signal matching. These estimations are in line with practical experiences and the simplistic example a.m. Also, they support the appropriateness of using relative errors to describe MFL scattering. Box matching and reporting thresholds’ influence on box match depth difference accuracy Even for perfectly correlated features the depth difference accuracy of box matching is below signal matching. The correlation process itself is worsening the box matching quality additionally, because it more generously searches a partner for a recent feature in the former inspection box table. Therefore it can happen, that:

• A new feature is correlated with a neighboring previous ILI box feature, generating a corrupt, principally too low depth difference (see Figure 5 right, 3% instead of 29%).

• A preexisting feature below the reporting threshold in the previous ILI box table will be identified as “new”. The result is a principally too high depth difference.

3.3 Motivation We observed above: • A high coverage of anomaly comparisons as provided by box matching is desirable. • Signal comparison is vital to allow for individual depth difference values at acceptable accuracy. • Signal based assessments allow identifying new features. • Box matching depth difference accuracy is negatively affected by erroneous correlations. • Signal-based repeat ILI comparisons require significantly more effort than box matching. • Even for generalized, segmented growth rate assessments, signal-based comparisons are beneficial,

because of the significantly higher accuracy, especially for shallower features.

A highly automated, full-coverage repeat ILI signal comparison is desirable. 4 Technical solution for high coverage automated signal comparison Inline inspection tools and their set-up on the one hand and pipelines and their operational and running conditions on the other hand are subject to well devised and rigid quality standards and measures. Nevertheless, ILI inspections are individual events where a tool meets a pipeline under certain conditions. These can slightly vary at the various points in time of the repeat ILI. Variations of even

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millimeters in the measurement data fields can make automated signal correlation a challenge in complex corrosion situations, especially if shape changes occur. But modern artificial intelligence and image processing algorithms together with high performance computing provide tools to cope with these challenges. 4.1 Correlation points Current industry standards translate the metal loss geometry in rectangular boxes. These boxes are mainly defined by their outer shape, i.e. shallow parts of the metal loss occurrence and the maximum depth, found somewhere within the rectangle. This parameterization is amongst the least suitable processes imaginable for correlation purposes because it uses weakly defined characteristics of the according signals. Figure 5 shows a classical example of the limited value of box matching due to its weak definition and overly generous reduction of a large mass of information.

Figure 5 – Limited value of box matching as reference points: Weak definition on left, information reduction and corruption on right [Dawson, EPRG, 2009]

The preferable alternative is to use as many of the most significant characteristics of the signal as possible and input them into modern computer science algorithms. In the following, three computer vision disciplines are explained. Key point detection: Even minor signals can be found by computer vision algorithms such as “Feature from Accelerated Segment Test” (FAST) [Roston, Cambridge 2005], which is a rapid “key point” detector. A sketch of the parameterization is provided in Figure 6. FAST is from a computational perspective very efficient. Around the candidate signal subject to analysis a circle spanning 16 signals points is analyzed. If the response difference between the candidate signal and contiguous circle signals fulfill certain predefined criteria, the candidate point is classified as a predefined sized set of adjacent signals on the circle is either higher or lower than a threshold, the candidate signal can be classified as a corner or key point. Another alternative relates to proprietary “Automatic Feature Search” algorithms.

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Figure 6 – Key point detection with FAST [Roston, Cambridge, 2005] Key point description: For the purpose of describing the signal profile surrounding the detected key point, a suitable template is chosen. This is not a box in the established sense, but simply an area around the key point that defines a template area to be correlated with the recent run. This is shown in Figure 7.

Figure 7 – Template area around any significant signal.

In order to derive a unique description of the template area, these key points are characterized by “feature descriptors”, like “Binary Robust Independent Elementary Feature” (BRIEF) [Calonder, EPFL, 2010] or “Normalized Cross Correlation” (NCC) [Lewis, Interval Research, 1995]. Figure 8 illustrates the binary description of BRIEF. BRIEF is a feature descriptor that can be applied to describe a patch of data surrounding a key point. For the entire analysis random but fixed point pairs around the key point are defined. The intensities of the point pairs are compared and stored as a binary arrayGiven data characteristics a suitable pattern of test signal pairs is chosen for the entire analysis. For each key point this pattern of signal pairs is applied and the intensities between the signals pairs are compared in a defined order and enter in a bit array. Similarity of two descriptors is Correlations are determined by comparing the binarybit arrays of the key points.

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Figure 8 – Unique binary template area description with BRIEF [Calonder, EPFL, 2010]

In the original data, the resulting correlations look like the example in Figure 9.

Figure 9 – Example of automatic individual correlations derived with FAST and BRIEF

Key point matching: The correlation of these binary description template areas of Figure 6 delivers the fine-tuned location offset between the two data sets at this position. For the high number of correlations, wrong assignments – so-called outliers - are possible. These outliers can be removed by outlier detection algorithms like “Random Sample Consensus” (RANSAC) [Fischler, SRI, 1980], “Kernel Density Estimation” (KDE) [Emanuel, Stanford University, 1965] or parametric density based outlier detection algorithms. These algorithms allow robust model fits even with considerable numbers of outliers, sketched in Figure 10.

Figure 10 – Correlation model fit with RANSAC [Fischler, SRI, 1980]

RANSAC separates correct and wrong correlations, so called “inlier” and “outlier”, and fits a model to all inliers. It consists of two steps. consist of two steps. In the first step a random subset of the correlations is selected to which a model is fitted. In the second step all the correlations are tested against this model. The set of correlations, which fit to the fitting the model by a certain amountgiven a threshold,

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is called the “consensus set”. or the inliers and the other correlations are outliers. This process is repeated until the consensus set contains a sufficient number inliers.

The final result is a finely resolved and fine-tuned area of displacement between the two runs, which directly and accurately correlates the two data sets.

These small displacements have natural origins, such as smallest individual odometer wheel slippage, differences in the sagging of the tool in the pipeline or little differences in mechanical elements like springs or the tool’s response to different inspection velocity. Even not easily visible also sub inch location differences should be eliminated before correlation. The vectors shown in Figure 11 are exaggerated by a factor 6. The red vectors were identified as being implausible, outliers as mentioned above, and are not considered any further.

Figure 11 – Fine-tuned displacement vectors of two repeat ILI runs, red arrows identified as implausible

4.2 Signal comparison

The data field correlation and comparison is a straightforward process. It makes direct use of the highly- resolved displacement information. The correlation uses nearest-neighbor techniques, which produce robust quality results because of the highly-resolved displacement information and the low residual displacement between the two data sets. Figure 12 shows an exemplary half square meter (5 square feet) area of complex corrosion. The automated signal comparison correlates the two data sets and automatically derives the apparent depth difference on the basis of the data interpretation of the recent data set, the automatic correlation, and the observed renormalized signal differences. This automatic calculation result is illustrated in Figure 13.

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Figure 12 – Complex corrosion example data

Figure 13 – Automatically generated result in the area of Figure 12, Depth difference vs. Depth

In this example (Figures 12 and 13) both the deepest feature and the fastest growing feature seem to have an individual character - information valuable for further consideration in integrity assessments. Although the example highlights the localized nature of corrosion growth, it cannot provide sufficient information to predict how individual features will grow in the future. Therefore, applying a growth rate to a segment or subset of corrosion features, normally provides the best balance between accuracy and conservatism [Smith et al., IPC, 2016]. That means high quality individual growth rates perhaps allow for, but do not indicate individual feature analysis. 4.3 Manual result validation and output

The automatically generated results come with implicit quality characteristics derived from the signal shape and displacement properties. Together with the calculated results, these allow for manual validation ranking. Especially the signal correlation link pair and the length and width difference are subject to revision. The latter can have a significant influence on the depth difference eventually. For this purpose, an advanced Graphical User Interface is required for the manual validation process. The manual validation shows that the rate of correct automatic correlations within the body of pipe is > 98.5% and even higher for significant anomalies. 4.4 Static feature correction

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Two consecutive MFL runs can be affected by minimal deviations in each calibration, which will affect principally all depth difference values in the same manner. This effect can be compensated by making use of so-called static features [Dawson et al., PRCI, 2009], which are typically corrosion anomalies like recoated or non-metallic sleeve repairs. These anomalies are simply marked in the process and undergo exactly the same procedure to calculate an eventual small growth value, which will be used to compensate for all anomalies.

5 Addendum - Error Estimation of Box vs. Signal Matching Depth Difference

Where the two relative error components, interpretation variability δI and signal repeatability δR, affect the depth sizing accuracy, the depth difference from isolated sizing (“box”) and combined sizing (“signal”) can be outlined analytically as below. This approach describes the error sources of MFL quite well.

The absolute error “Δ” (in % of wall thickness) calculates principally as outlined in Formula 1 for the relative wall loss“r”, in Formula 2 for the box matching depth difference, and in Formula 3 for the signal based depth difference.

∆(𝑟) = √𝛿𝐼2𝑟2 + 𝛿𝑅2𝑟2 (1)

∆𝐵𝑜𝑥(𝑟2 − 𝑟1) = √𝛿𝐼2(𝑟1 + 𝑟2)2 + 𝛿𝑅2(𝑟1 + 𝑟2)2 (2)

∆𝑆𝑖𝑔𝑛𝑎𝑙(𝑟2 − 𝑟1) = √𝛿𝐼2(𝑟2 − 𝑟1)2 + 𝛿𝑅2(𝑟12 + 𝑟2

2) (3)

These formulae confirm the need for high repeatability, i.e. low δR, which is one of the major design targets of modern ILI tools. Therefore, ILI δI typically exceeds δR. The formulae hence show that the box matching depth difference accuracy is below the depth sizing itself, while the signal match provides significantly better values, perhaps even better than the single sizing accuracy itself. Also, it becomes evident that box matching is typically not suitable for individual growth assessments, while the signal-based approach may allow for these. Using a typical δI 10% and δR 5% and an example feature of 50%, with 40% at the earlier ILI, the errors lay in the order of ∆(𝑟) = 5.6%; ∆𝐵𝑜𝑥(𝑟2 − 𝑟1) = 10.1%; ∆𝑆𝑖𝑔𝑛𝑎𝑙(𝑟2 − 𝑟1) = 3.4%. These results are in line with

practical experiences. 6 Abbreviations

BRIEF Binary Robust Independent Elementary Feature CGA Corrosion Growth Assessment CGR Corrosion Growth Rate δI interpretation variability δR signal repeatability Δ absolute error EPRG European Pipeline Research Group FAST Feature from Accelerated Segment Test FFP Fitness for Purpose ILI In-line Inspection KDE Kernel Density Estimation MFL Magnetic Flux Leakage NCC Normalized Cross Correlation

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POD Probability of Detection PRCI Pipeline Research Council International r relative wall loss RANSAC Random Sample Consensus σ Standard Deviation

7 Literature [Al-Amin et al., IPC, 2016]: M. Al-Amin, S. Kariyawasam, S. Zhang, W. Zhou, Non-linear corrosion growth – A more appropriate and accurate model for predicting corrosion growth rate, Proceedings of the 2016 International Pipeline Conference IPC 2016, Calgary Alberta, Canada. [Calonder, EPFL, 2010]: M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEF: Binary Robust Independent Elementary Features, CVLab, EPFL, Lausanne, Switzerland, 2010 [Dawson, EPRG, 2009]: J. Dawson, S. Kariyawasam, Understanding and accounting for pipeline corrosion growth rates, European Pipeline Research Group (EPRG), 17th Joint Technical Meeting, Milano, Italy, 2009 [Dawson et al., PRCI, 2009]: J. Dawson (GE), J. Wharf (GE), M. Nessim (C-FER), Development Of Detailed Procedures For Comparing Successive ILI Runs To Establish Corrosion Growth Rates, Final Report PRCI Contract PR-331-063525, July 2009 [Emanuel, Stanford University, 1965]: P. Emanuel, On estimation of a probability density function and mode, The annals of mathematical statistics, Stanford University, California, 1962 [Fischler, SRI, 1980]: M. A. Fischler, R. C. Bolles, Random sample consensus: A paradigm model fitting with applications to image analysis and automated cartography, SRI Projects 5300 and 1009, Menlo Park, California, 1980 [Kariyawasam et al., ASME, 2009]: S. Kariyawasam, and H. Wang, Useful Trends for Predicting Corrosion Growths, Proceedings of the 9th International Pipeline Conference, ASME, Calgary, Alberta, Canada, 2009 [Lewis, Interval Research, 1995]: J. P. Lewis, Fast normalized cross-correlation, Vision interface, Interval Research, Palo Alto, California, 1995 [Roston, Cambridge 2005]: E. Rosten, T. Drummond, Machine learning for high-speed corner detection, Department of Engineering, Cambridge University, UK, 2005 [Smith et al., IPC, 2016]: M. Smith, C. Argent, A. Wilde, Corrosion growth and remnant life assessment – How to pick the right approach for your pipeline, Proceedings of the 2016 International Pipeline Conference IPC 2016, Calgary Alberta, Canada.