[ASME 2008 7th International Pipeline Conference - Calgary, Alberta, Canada (September 29–October...

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Proceedings of IPC 207th International Pipeline Conferen

September 29- October 3, 2008, Calgary, Alberta, Cana

IPC2008-6446

QUALIFICATION OF ILI PERFORMANCE IN ACCORDANCE WITH API 1163 AND THE POTENTIAL IMPACT FOR MANAGEMENT OF PIPELINE INTEGRITY

Munendra S Tomar Applus RTD

Houston, TX, USA

Martin Fingerhut Applus RTD

Houston, TX, USA

Deli Yu Applus RTD

Edmonton, Alberta, Canada

Proceedings of IPC2008 7th International Pipeline Conference

September 29-October 3, 2008, Calgary, Alberta, Canada

IPC2008-64469

ABSTRACT

Characterizing the performance of In Line Inspection (ILI) continues to be a subject of ongoing debate. Knowing the accuracy and reliability of ILI measurements is important for determining optimum integrity rehabilitation project scope, re-inspection interval and the risk associated with the pipeline section examined. The introduction of API 1163 (In-Line Inspection Systems Qualification Standard) is a positive step in defining the key metrics for qualification of ILI results.

Qualifying these metrics requires verification measurements having accuracies an order of magnitude higher than in-line-inspection. LasersureSM ♦ provides a process which uses high resolution, high accuracy, in-the-ditch, Laser Profilometry measurements which satisfies these requirements and therefore is well suited for this purpose.

This presentation will identify the components of the LasersureSM process and through example the potential impact of this process for management of pipeline integrity.

INTRODUCTION In-Line Inspection (ILI) Magnetic flux leakage (MFL) ILI is commonly used by pipeline operators to determine the location and extent of metal loss on their systems. The MFL technology continues to improve and ILI tools have accomplished some major strides in the last decade or so. However, a significant uncertainty still remains as to the accuracy of the ILI data. The tool accuracy is affected by factors such as the cleanliness of the pipe, chemical and physical steel properties, residual stresses, and wall thickness changes among others.

ILI vendors normally define both the accuracy and certainty associated with their data. In some cases these are defined for multiple geometrical categories such as in the

♦ Patent pending

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Pipeline Operator’s Forum and API 1163. In other cases, it is provided as an aggregate metric. For depth measurements the accuracy is typically on the order of +/-10% with a certainty of 80%. Simply put the ILI depths’ error would have an 80% chance of being within the 10% tolerance. With this a large amount of uncertainty still remains which must be taken into account during decision making.

The uncertainty in the data has a direct impact on the risk level that an operator assumes with a pipeline system. The monetary effect on any integrity management program can be significant as conservative estimates cause an increase in the number of areas to be inspected and excavations to be performed and a decrease in the re-assessment interval.

The presented paper describes a pilot project using the LasersureSM process carried out by Applus RTD for a major North American pipeline operator. The objectives of the project were to:

• Demonstrate that LasersureSM can be utilized to determine the accuracy of ILI data.

• Demonstrate that LasersureSM can be used to estimate the systematic error as found in ILI data and the corresponding correction factors with a given confidence in the results.

• Demonstrate that the application of LasersureSM process can provide improved accuracy and confidence in the ILI data.

LaserscanSM LaserscanSM is a service provided by Applus RTD wherein

the proprietary LPITTM tool uses laser profilometry for mapping the external surface of the pipe and thereby measures, with millimeter level resolution, the extent of external corrosion present. As of now, it is a benchmark technology and has been proven to be very accurate and reliable in the field. It is capable of providing a high-definition, three-dimensional map of the pipe surface in a digital format. Due to the nature of

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the dataset, the data from LasercanSM is ideally suited for detailed analysis and comparison.

API 1163 API 1163 (In-Line Inspection systems qualification

standard) was introduced in August 2005 as an umbrella document defining the manner in which the ILI data was to be reported and qualified. While it does define the specifications for anomalies belonging to different geometrical categories, it does not offer a robust process for comparing the ILI data with the field measurements. It also does not provide any guidelines for anomaly length and width comparison.

A metal loss defect, as measured in the field or by an ILI tool, usually comprises of multiple indications or pits (each of the same or different geometrical shape) combined together using a pre-defined interaction criteria. Similarly, ILI data also undergoes a “clustering process.” To classify the indication into different geometrical categories, one has to use the unclustered data.

PREVIOUS EFFORTS Although several correlation attempts have been made

between MFL and field measurements, most efforts, although insightful, had some common characteristics.

• The field data was collected manually limiting the number and accuracy and measurements

• Measurement and indexing error was introduced through manual methods

• Data resolution was generally low, limiting the number of points to match data by geometrical shape

• Most such studies focus on the verification and analysis of the depth measurement only

• Most such studies have considered clustered data only. Furthermore, most such efforts used only the most severe

metal-loss locations as the dataset. This tends to introduce an inherent bias in the data as most of the data points as obtained from the tool are actually not as severe.

LasuresureSM LasersureSM process exploits the phenomenon that being a

magnetic measurement, the signal obtained from the metal loss is a function of not only the volume of the metal loss, but also the geometrical shape of the anomaly. To do so, we have used the seven geometrical categories as defined by the Pipeline Operators Forum (Figure 1). They are as:

• Extended/ General • Pitting • Pinholes • Axial Grooves • Axial Slots • Circumferential Grooves • Circumferential Slots

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These categories are based on the ratio of length to width and the area of the anomaly. In effect, by using the boxed data, LasersureSM doesn’t allow the ‘normalization of the systematic error that may occur when a given cluster might comprise of multiple pits belonging to more than one of these geometries.

There are a few key characteristics that differentiate LasersureSM from the previous works in this direction. They are summarized as follows:

• The data points were matched on an individual pit basis

• The ILI data used was boxed data (prior to the application of interaction rules)

• The analysis was performed on data sets belonging to each geometrical category individually

• All data points were taken into consideration, including corrosion with very small depth measurements (>= 5% wall thickness)

Also, some assumptions were made to apply the process: • It was assumed that the error in the LPITTM

measurements was negligible • It was assumed that the measurements from the ILI

data were reliable enough to determine the geometrical category a particular indication belonged to.

PROCESS The LasersureSM process consists of the following steps: • Data Assimilation • Classification • Data Point Matching • Data Extraction • Analysis • Validation • Re-clustering • Failure Pressure Calculations

Data Assimilation The boxed data from a previous ILI (MFL) inspection was

provided for this study by a major North American pipeline operator. The in-line inspection was performed over two valve sections. In addition, LaserscanSM service had been utilized during the rehabilitation project previously carried out by the operator. The data from the locations inspected using the LaserscanSM and the MFL data from the corresponding pipe segments was located and organized.

46 locations along the two valve sections were identified that had laserscan data available. 25 of which were used for applying the process

Classification Each MFL data point (indication) was then classified into

one of the seven categories based on the ILI reported length and width using the criteria as defined in the POF document.


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The data corresponding to the locations which had laserscan data was extracted for analysis.

Data Point Matching One of the biggest challenges in trying to obtain a

comparable dataset from ILI and verification data is to match data-points from one dataset to another. When trying to compare the two datasets on a pit by pit basis, the anomalies are often small and closely spaced which makes it hard to determine if the two measurements actually are for the same feature.

To overcome this, semi-automated pattern matching software (Figure 2) was developed. In situations where a single indication in either the ILI or LaserscanSM data corresponded to multiple indications in the other data set, the multiple indications were combined to form a one to one relation. This was necessary because typically, the LaserscanSM sensitivity is much higher than the ILI sensitivity.

Data Extraction The data from the matching data points was then

accumulated and verified to ensure consistency. The parameters for consideration (Length, width and Depths) for these indications were extracted from the pool of data and arranged in a comparable format. Some of these matching data points were set aside as a control group at random for validation purposes.

A total of 1860 data points (a set comprising the ILI data and corresponding Laserscan measurements) were extracted in this manner and used for analysis. 513 data points were set aside as a control group for validation purpose. No matching data points were found in the pinholes, axial slotting or axial grooving categories.

Analysis Statistical analysis was performed on each geometric

category individually. Furthermore, analysis was performed with respect to the there parameters of interest, i.e. Length, Width and Depth for each of the four categories i.e. Pitting, General, Circumferential grooving and axial grooving. The data was evaluated to determine a constant bias, positive or negative, at a 95% confidence level.

Validation The unmodified control data set was compared with the

Laserscan measurements to determine the range of error with 90% confidence (tables 1 and 2). Once the bias(es) were determined, a correction factor was calculated and applied to the MFL data points from the control data set in each of the three parameters and for each geometrical category. These corrected measurements were then compared against the Laserscan measurements again to validate that the correction factors were effective in improving accuracy.

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Re-clustering After applying the correction factors and validating the

improvement, the data was then re-clustered using the interaction criteria. At this stage, the impact of the correction factors becomes more evident as the corrections in length and width change the formation of clusters as described in the following diagram (Figure 3).

Failure Pressure Calculations After re-clustering the data, the length, width and depth of

the resultant clusters are calculated and failure pressures determined.

Results The results for this project can be summarized as: A distinct, measurable bias did exist for length, width

and depth in ILI data. The bias, as determined in this project was different

for different geometrical categories. The correction factors, as calculated using lasersureSM

did improve the accuracy of the ILI data (Figures 4 – 15).

The largest bias was determined to exist in the width measurements

Re-clustering after applying the bias factors resulted in more differentiated clusters. E.g. for this set of data, applying lasersureSM resulted in further splitting the clusters so as to add 10000 new clusters to the original data.

Conclusions LasersureSM can be used to measure the accuracy of

ILI data based on high accuracy, high resolution Laserscan data.

Improved accuracy in length and width results in more accurate clustering of the boxes, thereby reducing the chances of having false positives or false negatives (Figure 16).

Improved accuracy in depth, coupled with more accurate cluster lengths, results in better prediction of failure pressures, thereby allowing the operators to target the maintenance activities more efficiently.

A better estimate of the accuracy in ILI data can enable operators to predict corrosion growth rates more accurately and optimize re-inspection intervals. Also, with the growing emphasis towards a more reliability based, quantitative risk assessment and integrity management initiatives, LasersureSM

process could provide the means to avoid the need to incorporate costly assumptions about the uncertainty in the data.

REFERENCES 1. Statistical methods for assessing agreements

between two methods of clinical measurement. Bland J.M., Altman D.G.


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2. Individual comparisons by ranking methods. Wilcoxon F. (1945)

3. A general regression procedure for method transformation. Passing H., Bablok W.

4. Principles and procedures of exploratory data analysis. Behrens, J.T. (1997).

5. NIST/SEMATECH e-handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook.

6. Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics) 2005.


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ANNEX A

TABLES AND FIGURES Figure 1: POF defined geometrical categories

Figure 2: Snapshot of the utility used for pit-matching


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Figure 3: Impact of length and width correction on clusters

Fig 4: Bias plot for pitting depth

Fig 5: Bias plot for pitting length

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Fig 6: Bias plot for pitting width

Fig 7: Bias plot for general, depth

Fig 8: Bias plot for general, length

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Fig 9: Bias plot for general, width

Fig 10: Bias plot for circumferential grooving, depth

Fig 11: Bias plot for circumferential grooving, length

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Fig 12: Bias plot for circumferential grooving, width

Fig 13: Bias plot for axial grooving, depth

Fig 14: Bias plot for axial grooving, length

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Fig 15: Bias plot for axial grooving, width

Figure 16: An illustration of the impact of uncertainty in length and depth on predicted burst pressures

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Table 1: Control group error before correction

Length Width Depth

80% 80% 80%

Uncorrected @ 80% CL (mm) Min Max Min Max Min Max

Overall -38 34 -32 13 -0.6 1

General -53 36 -51 7 -0.5 1.14

Pitting -34 32 -26 12 -0.6 0.9

Axial Grooving -60 40 -24 29 -0.4 0.8

Circumferential Grooving -28 46 -51 0.7 0.02 1.27 Table 2: Control group accuracy after correction

Length Width Depth 80% 80% 80% Corrected @ 80% CL

(mm) Min Max Min Max Min max

Overall -38 34 -24 19 -0.6 0.9

General -53 36 -37 20 -0.6 1.05

Pitting -34 32 -21 18 -0.7 0.9

Axial Grooving -59 42 -24 29 -0.4 0.7

Circumferential Grooving -30 44 -35 16 -0.09 1.16


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