Summary of the rewrite of HSE’s PIPeline INtegrity (PIPIN ... · PDF fileZoe Chaplin...

Prepared by the Health and Safety Laboratory for the Health and Safety Executive 2015

Health and Safety Executive

Summary of the rewrite of HSE’s PIPeline INtegrity (PIPIN) model

RR1039Research Report

Zoe ChaplinHealth and Safety LaboratoryHarpur HillBuxtonDerbyshire SK17 9JN

The Health and Safety Executive (HSE) uses the PIPIN (PIPeline INtegrity) model to determine failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure of pipelines (pinhole, small hole, large hole, and rupture). PIPIN uses two approaches to determine failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, ground movement and other events, corrosion, and third party activity); and a predictive model that uses structural reliability techniques to predict the failure frequency due to third party activity only. The predictive model uses historical data in the form of damage data distributions and strike rates as inputs to the fracture mechanics equations. HSE asked the Health and Safety Laboratory (HSL) to rewrite PIPIN using a Monte Carlo solution approach, to update the science in the model based upon peer review recommendations, to update the damage data used in the predictive model, and to update the historical operational experience data. The effect of the revised model on the results generated from a set of 584 pipelines has been investigated and it was shown that the combined effect of all the modifications is to reduce the failure rates, on average, for all hole sizes compared to the original model.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.


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First published 2015

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CONTENTS

1 INTRODUCTION ..................................................................................... 1

2 SOLUTION METHOD.............................................................................. 3 2.1 Fracture Mechanics ................................................................................. 3 2.2 Monte Carlo Solution Method .................................................................. 5

3 SCIENCE MODIFICATIONS ................................................................... 6

4 DATA CHANGES.................................................................................... 8 4.1 Damage Data........................................................................................... 8 4.2 Strike Rates ............................................................................................. 9 4.3 Operational Data...................................................................................... 9

5 RESULTS.............................................................................................. 10 5.1 Results from the development versions of the model ............................ 10 5.2 Results from the operational versions of the model ............................... 16

6 CONCLUSIONS AND RECOMMENDATIONS ..................................... 19

7 REFERENCES ...................................................................................... 20

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EXECUTIVE SUMMARY The Health and Safety Executive (HSE) uses a computer code PIPIN (PIPeline INtegrity) to determine failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure of pipelines (pinhole, small hole, large hole and rupture). The failure rates are used in other tools, such as MISHAP (Model for the estimation of Individual and Societal risk from HAzards of Pipelines), which calculates the level of risk surrounding a major hazard pipeline and from this determines the extent of land use planning zones around such pipelines. PIPIN uses two approaches to determine failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, ground movement and other events, corrosion and third party activity (TPA)); and a predictive model that uses structural reliability techniques to predict the failure frequency due to TPA only. The predictive model uses historical data in the form of damage data distributions and strike rates as inputs to the fracture mechanics equations.

A programme of improvements to PIPIN was considered and three main areas of work were identified:

PIPIN was written in the late 1990s and there have been some issues regarding its stability identified when using the code. These issues relate to the solution method used in the model. HSE asked the Health and Safety Laboratory (HSL) to investigate whether an alternative solution method could be implemented in the code.

It was decided that the science behind the model should undergo an independent review. Any modifications suggested by the review should be tested and a decision made on whether it was appropriate to implement such changes.

The data used within the model required updating. This was both in terms of the damage distributions and strike rates used by the TPA model, and also the historical failure rates used for the other failure mechanisms.

This report summarises all three aspects of the model updates (solution method, science changes and data updates) and provides an overview of the results obtained compared to the original version of PIPIN.

Objectives

The objectives of this project were to investigate the impact of modifying the solution method, the science and the data on the failure rates calculated by PIPIN.

Main Findings

This report summarises the impact of all the proposed modifications to PIPIN and compares the failure rates generated by a development version of the code incorporating all the changes against a development version of the model that replicates the original version of the code. It was found that, for ruptures, large holes and small holes in the TPA only case, the failure rate decreased for all of the pipelines investigated. For pinholes, an average increase of 46% was seen, when compared to the development version of the model that replicates the original version of the code. When the historical failure rates for mechanical, corrosion and natural/other mechanisms were also included, it was found that the failure rates for all the hole sizes decreased on average, although, for ruptures and pinholes, increases in the failure rates were seen for some pipelines.

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The impact on the land-use planning zones was also assessed from the variation of the failure rates generated by PIPIN when incorporating all the changes. It was found that, on average, no change was seen to the distances to the inner zone and small decreases in the distances calculated for the middle and outer zones. Some variation was seen, however, with the size of the zones increasing in a few cases and decreasing in others.

Comparisons were also carried out for the ‘operational’ versions of the model, where 10 runs are performed for each pipeline and the result generated from the mean value calculated from these 10 runs. The ‘operational’ versions of the code are MCPIPIN, currently used as the standard pipeline failure rate model in HSE, and PIPINV3, which is the equivalent version with all the science and data changes implemented. The results generated by the ‘operational’ versions of the code replicated those seen by the development versions of the model to a high degree of accuracy.

Recommendations

It is recommended that the changes to PIPIN as summarised in this report should be incorporated into the model. This revised version should then become known as PIPINV3 to distinguish it from the original version of the model. PIPINV3 should be released and used as the standard pipeline failure rate model in HSE, superseding all previous versions of the code.

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1 INTRODUCTION

1. The Health and Safety Executive (HSE) uses a computer code PIPIN (PIPeline INtegrity) [1,2] to determine the failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure of pipelines (pinhole, small hole, large hole and rupture). The failure rates are used in other tools, such as MISHAP (Model for the estimation of Individual and Societal risk from HAzards of Pipelines) [3], which calculates the level of risk surrounding the major hazard pipeline and is used to determine the extent of land use planning zones around such pipelines. PIPIN uses two approaches to determine the failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, ground movement and other events, corrosion and third party activity (TPA)); and a predictive model that uses structural reliability techniques to predict the failure frequency due to TPA only. The predictive model uses historical data in the form of damage data distributions and strike rates as inputs to the fracture mechanics equations.

2. A programme of improvements to the PIPIN model was devised looking at the solution method, the science behind the model, and updating the data used in the model.

3. PIPIN was written in the late 1990s and in use there have been some issues identified regarding its stability. These issues relate to the solution method used in the model. HSE asked the Health and Safety Laboratory (HSL) to investigate whether an alternative solution method could be implemented in the model. This work is detailed in a report by Chaplin [4].

4. The science behind the original PIPIN model was reviewed by an external reviewer and suggestions were made as to areas where the model could be improved. HSL assessed the suggestions made from this review and determined where it was appropriate to implement changes. A Monte Carlo (MC) approach was considered to be the appropriate solution method to provide the code with better stability. The updates to the model arising from this work are detailed in Chaplin [5].

5. The final stage in developing the model entailed updating the data used. There are two separate types of data used within PIPIN, the first of which is the historical information used for the non-TPA failure rates. The updates to this data are detailed in Chaplin [6]. The second set of data relates to the damage distributions and strike rates that form inputs to the structural reliability models. This work is detailed in Chaplin [7] and incorporates the effects of all the modifications to PIPIN.

6. This report summarises the information contained within the four PIPIN reports [4, 5, 6, 7] and provides an overview of the results obtained from a version incorporating all of these modifications when compared to the initial Monte Carlo version of PIPIN (as detailed in [4]). The two versions of the model illustrating this comparison are called the “All changes” version, where all the science and data changes have been implemented, and the “MC PIPIN” version, which is the initial Monte Carlo version of PIPIN. It should be emphasised that both of these versions used are development versions of the model, and only 1 run is performed for each pipeline. This report also compares results between the HSE operational model, MCPIPIN, where 10 runs are performed for each pipeline, and an equivalent version, PIPINV3, where the science and data changes have been incorporated. These are the final operational versions of the “MC PIPIN” version and the “All changes” version respectively.

7. The remainder of this report is structured as follows:

• Section 2 summarises the changes made to the solution method within PIPIN;

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• Section 3 summarises the changes made to the science behind the model;

• Section 4 summarises the data changes made, both to the historical information and also to the statistical inputs required by the fracture mechanics models;

• Section 5 presents the results of all the modifications from Sections 2, 3 and 4; and

• Section 6 lists the conclusions and recommendations.

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2 SOLUTION METHOD

8. PIPIN was written in the late 1990s and there have been some issues regarding its stability identified during the use of the model. In a number of situations the model has failed to produce results, leading to the necessity of interpolation of results from pipelines similar to the case in question. HSE asked the Risk Assessment Team of HSL investigate whether an alternative, more robust solution could be implemented into the model. The key aim of this work was to produce a reliable version of PIPIN with no stability issues. HSE did not stipulate what type of solution method they would like for the code, but left it to HSL to determine the best way to produce this new, more reliable version of PIPIN.

9. The main issue with the current version of PIPIN is the solution method used, FORM/SORM [8, 9, 10]. FORM is the first order reliability method and SORM is the second order reliability method. Within PIPIN the probability of failure of a pipeline is calculated using the FORM method. For a significant proportion of pipelines this solution technique fails to converge. Alternative methods to find a solution include using SORM, direct numerical integration or Monte Carlo (MC) simulation. SORM still only produces an approximate solution to the problem and there could still be issues over its convergence on an answer. Direct numerical integration would be highly complex, given the number of variables involved, whereas a Monte Carlo simulation would keep the problem relatively simple, whilst still allowing for a high degree of accuracy. Although Monte Carlo simulation can be computationally time consuming, this approach was chosen for further investigation. It was considered that the increase in speed of computer processors over the last 10 years would mean that a Monte Carlo approach would be usable. It is also more intuitive to work with than direct numerical integration.

10. In order to understand how the solution method was modified, it is first necessary to describe some of the structure of the model and the underlying science behind the model. Only the TPA version of the model is discussed.

2.1 FRACTURE MECHANICS

11. It has been observed that there are two primary mechanisms by which a pipeline may be breached as a result of external impact damage. In either case, if the breach is unstable, a rupture may result.

12. The first mechanism is by a surface gouge, which can be created, for example, as a result of contact by excavating machinery. This can lead to a rounded profile gouge and a statistical distribution has been fitted to data on the length and depth of such gouges found in practice. If the gouge depth is greater than the wall thickness then the pipeline is assumed to have been punctured. Diagram A in Figure 1 illustrates a gouge.

13. The second mechanism is by a dent-gouge. These occur if the impact energy is high enough to lead to significant tensile bending stresses at the root of the gouge, resulting in a dent, which increases the probability of a breach of the pipeline wall. Diagram B in Figure 1 illustrates a dent-gouge.

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Figure 1 Diagram illustrating a gouge (A) and a dent-gouge (B)

14. PIPIN and the new MC model have three main fracture mechanics models:

• a gouge model that models the plastic collapse of the pipeline using either gouge data or, with a slight modification, dent-gouge data (plastic collapse refers to the deformation of the material as the load upon it becomes too large);

• a dent-gouge model that models failure by fracture (a fracture is a brittle failure); and

• a rupture model that models the likelihood of a leak, resulting from either of the above failures, leading to a rupture.

15. Two of the fracture mechanics models are run twice with different sets of data, giving a total of five fracture mechanics submodels.

16. In all cases the results are compared with the R6 Rev. 3 fracture assessment procedure [11] to determine whether the pipeline fails. This is a curve such that, if a point lies above it then the pipeline has failed, whilst if it lies on or beneath the curve, then the external impact will not have led to a failure.

17. Each of the fracture mechanics models requires a number of inputs, both in terms of the pipeline characteristics (e.g. diameter, wall thickness, operating pressure etc.) and also for the damage data (i.e. gouge length, gouge depth, dent-gouge length, dent-gouge depth and impact force). All of these parameters are described using statistical distributions (Weibull distributions for the damage data, and a combination of normal and lognormal distributions for the other parameters). Using statistical distributions allows for the fact that the actual values for the

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damage data and the pipeline characteristics, may vary slightly from those input (due to inaccuracies in recording methods etc.). In addition, the original version of the model also incorporated modelling uncertainties, which were factors applied to some of the fracture mechanics equations. These were also described by normal or lognormal distributions. In total, in the original version of the model, there were 19 statistical distributions required.

18. The solution method within the model determines how the 19 statistical distributions are handled and hence how the equations are solved, in order to determine probabilities of failure for a particular pipeline.

2.2 MONTE CARLO SOLUTION METHOD

19. The original version of PIPIN used a FORM/SORM approach to solve the equations within each submodel (gouge, dent-gouge and rupture). The revised version of the model, which is fully described within Chaplin [4], incorporates a Monte Carlo method.

20. The approach involves randomly sampling each of the input variables, defined by the statistical distributions described in Section 2.1 (e.g. pipeline parameters and damage distributions), to determine whether, for a particular set of values, a failure occurs. A random number generator is used as an input to statistical functions to generate the input variables for the model. These input variables are then input to the fracture mechanics equations to ascertain whether this particular combination of parameters would cause a failure point.

21. By repeating this process a large number of times (iterations) the probability of failure for a specific pipeline can be calculated for each of the five failure models. The probability of failure is simply the number of cases where failure occurred divided by the number of iterations. The process is repeated until the failure probabilities have converged, i.e. do not change significantly with further iterations. Initially the failure probability will change significantly as more failure points are identified. As more and more iterations are performed then these changes will become smaller and smaller. A convergence criterion has been set that terminates this part of the code once certain conditions have been met.

22. Once all five of the submodels have converged, then the individual failure probabilities are combined to produce the overall failure frequencies apportioned by hole size. Should any part of the model fail to converge, it is possible to increase the number of iterations performed, or decrease the convergence criteria, which should ensure that the model converges when it is rerun.

23. The Monte Carlo method is more stable than FORM/SORM and allows results to be obtained for pipelines that previously had to be estimated using alternative methods. It also replicates, to a reasonable degree of accuracy, the results obtained from the original version of PIPIN. For a full comparison of results, see Chaplin [4].

24. It should be noted that, by the nature of the solution method, rerunning the same pipeline will produce slightly different results. It has been shown, however, that once the TPA values have been added to the failure rates due to other causes (mechanical, corrosion and ground movement/other), the variation in rates becomes negligible and will not generally impact on the LUP (land-use planning) zones ultimately produced by MISHAP using these failure rates as inputs (see Chaplin [4] for more details).

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3 SCIENCE MODIFICATIONS

25. An independent review was carried out on the science used in the code. This was to determine whether the science used in the model, originally developed in the 1990s, was still fit for purpose, or whether there were any modifications that could improve the model. This review was carried out on the version that had been rewritten to use a Monte Carlo solution method. The review [12] identified a number of key areas that were worth further investigation, which could potentially improve the validity of the model. Full details are given within Chaplin [5]. A summary of the main areas investigated are detailed in this section.

26. Within the original code, a number of modelling uncertainties were applied to various equations to represent the level of uncertainty involved in trying to model whether a failure will occur when a pipe is damaged. The independent review considered that this was a case of adding uncertainty onto uncertainty and that these modelling uncertainties should be removed. On investigation it was shown that doing this has a small impact on the results of the model (9% for ruptures, 3-4% for other hole sizes, for TPA only) but significantly improves the speed of the code as the number of statistical distributions required to be sampled within the model reduces from 19 to 11. It was therefore decided that this change should be implemented.

27. A simplified version of one of the fracture mechanics equations was used when a dent and gouge were formed through plastic collapse compared to when just a gouge was formed through plastic collapse. The review suggested that, as the same mechanism is involved in both cases, the use of a simplified equation was unnecessary; the same equation should be used in both cases. Test runs found that this change had a negligible impact on the results. It was therefore decided that this change should be implemented within the code.

28. The Charpy energy-fracture toughness correlation reflects a material’s ability to resist fracture and relates the energy imparted when a material is struck to its toughness. The correlation is used as it is not possible to directly measure the fracture toughness. The independent reviewer suggested that the equation currently incorporated within PIPIN may be unrepresentative and that the equation over-predicts the toughness. It was suggested by the reviewer that a published relationship, such as that in BS7910 [13] should be used in preference. The impact of moving to BS7910 was found to be very large (increasing the rupture failure rates by an average of over 500%). On further investigation and after discussions with fracture mechanics experts, it was found that the Charpy energy-fracture toughness correlation within the British Standard is considered to be overly conservative and that the one within the original version of PIPIN is thought to be more representative of reality. It was therefore decided that this correlation in the code should not be changed.

29. Part of the calculations in the model involves combining the failure probabilities from each of the fracture mechanics models and then apportioning them to the individual hole sizes. The review suggested that the calculation to generate the rupture failure rates was inaccurate. A revision was suggested that ensured no double counting occurred when the gouge or dent-gouge length was greater than a 110 mm equivalent diameter hole. This modification decreased the rupture failure rates on average, and had no effect on the other hole sizes. It was decided to include this change in the code.

30. In recent years, the role of micro-cracks, which form at the base of a dent, has become of increasing importance to fracture mechanics. No experimental data exists to quantify the exact effect they have on fault propagation, but it is possible to derive a correlation using the experimental data that does exist. To do this, a model containing equations with micro-cracks, is

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run using the experimental data. The micro-cracks can be back-calculated and a correlation derived based on the pipeline parameters.

31. The micro-crack correlation within PIPIN was originally calculated by comparing a subset of results generated by PIPIN with those from FFREQ, the industry standard failure frequency model. The FFREQ model had been tuned to the experimental data available but does not, itself, contain a micro-crack model. The experimental data was not specifically used to derive PIPIN’s micro-crack correlation. In addition, in the original development of the micro-crack model, only 29 of the possible 124 experimental points were used to derive the correlation. The existing correlation also had an inconsistency in that if the dent depth is zero then there should be no possibility of a micro-crack but this does not hold true with the existing correlation. The combination of these factors led to the conclusion that the micro-crack correlation should be revisited and the original, published, test data should be used to derive the correlation rather than runs of the industry FFREQ model.

32. The effect of using the revised correlation was to increase failure rates by a factor of 2, on average, for ruptures. Despite this, it was decided that the new micro-crack correlation should be incorporated into the model due to the increased transparency in the way it was generated and due to it being directly based on experimental data rather than on another model.

33. The overall impact of incorporating all the changes that were implemented following the analysis of the independent review recommendations was to increase the rupture failure rate by a factor of 2. This change was dominated by the modification to the micro-crack correlation.

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4 DATA CHANGES

34. There are two types of data used within PIPIN. The first set of data define the damage distributions i.e. the gouge length and depths, the dent-gouge length and depths and the impact force, which are used within the fracture mechanics models. Strike rates are also input, which determine the frequency with which a pipeline will be struck by a third party. The second type of data is used to derive failure rates for the other failure mechanisms i.e. mechanical, corrosion and ground movement/other. Both sets of data required updating as they were based on information that was several years out of date. A brief description of what this entailed is given in this section.

4.1 DAMAGE DATA

35. The damage data consists of five statistical distributions that define the gouge length, gouge depth, dent-gouge length, dent-gouge depth and the impact force, together with strike rates that define the frequency with which a pipeline will be hit. These are all based on historical data and had not been updated for several years.

36. It was also recognised that the Weibull distribution, currently used for all five of the damage distributions, may not provide the best fit to the data. An aim of this work was to consider whether there were any other distributions that would provide a better fit to the data and implement any improved fit distributions within the model. Full details of the methodology followed are described in Chaplin [7].

37. A spreadsheet was obtained from UKOPA (UK Onshore Pipeline operators’ Association) that contained details of all recorded faults, due to third party activity (TPA) along the pipeline network between 1967 and 2009. Each fault or failure is categorised as to whether it is a dent, a gouge, or a crack. Some dents have one or more gouges associated with them, and hence these gouges can be classified as dent-gouges. The dent, or gouge, depth and length are recorded in the spreadsheet and hence it is possible to generate a list of all gouges or dent-gouges that have occurred on the network since 1967. It should be noted that the impact force is calculated using an equation based on the dent depth.

38. Once all the relevant incidents had been identified, it was possible to fit a statistical distribution to each set of data. Two main distributions were considered, the Weibull and the lognormal, both of which are commonly used in engineering. If neither of these were found to fit the data then alternative distributions were considered but none were found to improve on either the lognormal or the Weibull for the data in this case.

39. For each set of damage data, test statistics were derived to provide an indication of how well the distribution fit the data. From the statistics, it was found that neither the Weibull nor the lognormal fit the gouge length or gouge depth. However, a visual inspection suggests acceptable fits are given by a Weibull distribution for the dent-gouge depth and the impact force, and by a lognormal distribution for the dent-gouge length, the dent-gouge depth and the impact force.

40. Given the predominance of Weibull distributions in engineering and the historical use of them within PIPIN, it was decided that a Weibull distribution should be used for consistency for cases where there was no obvious better fit using a lognormal distribution. The lognormal distribution did provide a significantly better fit to the data for the impact force and hence it was decided that it should be used in preference to the Weibull distribution in this case. It was also decided to use the lognormal distribution for the dent-gouge length where the Weibull distribution did not fit the data particularly well.

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41. In summary, the Weibull distribution was recommended for the gouge length and depth and the dent-gouge depth, whilst the lognormal distribution was recommended for the dent-gouge length and the impact force.

4.2 STRIKE RATES

42. The strike rates, or incident frequencies determine the frequency with which a pipeline will be struck. These are multiplied by the failure probabilities calculated by the fracture mechanics models to generate an overall failure frequency, i.e. the frequency that the pipeline is hit is multiplied by the probability that it will fail if it is hit by an object.

43. There are currently two different values used for the strike rates, the first refers to gouge incidents and the second to dent-gouge incidents. As in the case of the damage distributions, the strike rates are based on historical data and can be calculated from the UKOPA spreadsheet discussed in Section 4.1. Population data, in the form of the number of km years that the pipelines have been operational, is also required for the calculation.

44. The revised values for the strike rates are both smaller than the current values, suggesting that a reduction in the overall failure rates is likely to be seen when they are implemented.

4.3 OPERATIONAL DATA

45. As well as calculating failure rates due to third party activity, PIPIN also generates frequencies for mechanical, corrosion and ground movement/other mechanisms. These rates are derived from historical datasets and are substance specific. In the current version of PIPIN, a combination of UKOPA, CONCAWE (CONservation of Clean Air and Water in Europe) and EGIG (European Gas pipeline Incident Group) data is used. The most recently available data was reviewed in order to provide more up-to-date failure rates. Tables showing the failure frequencies to be used for each substance and each failure mechanism can be found in Chaplin [6].

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5 RESULTS

5.1 RESULTS FROM THE DEVELOPMENT VERSIONS OF THE MODEL

46. The effects of all the changes briefly described in Sections 2, 3 and 4 have been assessed by running 584 pipelines through the Monte Carlo version of PIPIN (the “MC PIPIN” version) that replicates the original FORM/SORM version of PIPIN, and the fully modified version (the “All changes” version). Both of these are development versions of the model. It should be noted that, unlike the operational version of MCPIPIN which uses the mean value of 10 runs for each pipeline, only 1 run is performed for each pipeline in both the “MC PIPIN” and the “All changes” versions. Statistics have been derived based on dividing the results from the “All changes” version by the “MC PIPIN” version and these can be seen in Table 1 for the TPA only case and in Table 2 for when the historical failure rates for the mechanical, corrosion and ground movement/other failures are also included. A value greater than 1 indicates that the new model is producing higher failure rates than the original version of PIPIN, whilst a value less than one indicates that the new model is producing lower failure rates.

Table 1 Summary statistics of the comparison of the “All changes” version against the “MC PIPIN” version – TPA

Hole size Pin Small Large Rupture Mean 1.46 0.70 0.65 0.60 Minimum 0.05 0.03 0.03 0.03 Maximum 2.05 0.98 0.97 0.89 Standard deviation 0.43 0.20 0.17 0.15

Table 2 Summary statistics of the comparison of the “All changes” version against the

”MC PIPIN” version – total rates


47. The results for the TPA only case from Table 1 indicate that, for all the pipelines, the changes decrease the rupture, large holes, and small holes failure rates. The pinhole failure rates increase on average, although a decrease is seen for some pipelines

48. From Table 2 it can be seen that the total failure rates for all the hole sizes decrease on average and, for small and large holes, this decrease is seen across all the pipelines. For ruptures and pinholes, some pipelines see an increase in failure rates (up to 21% for ruptures) whilst others see a decrease.

49. Figures 2 and 3 graphically compare the rupture failure rates, including operational failures, from the “MC PIPIN” version and the “All changes” version, on a linear and log-log scale respectively. If a point lies above the line then this indicates that the failure rate is lower in the “All changes” version than in the “MC PIPIN” version. The opposite is true if a point lies below the line.

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Graph showing total rupture failure rates from the MC PIPIN case vs. the all science and data changes case

0.00E+00

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0.00E+00 1.00E-07 2.00E-07 3.00E-07 4.00E-07

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Figure 2 Plot comparing rupture failure rates (including operational data) from the

”MC PIPIN” version and the “All changes” version on a linear scale

Graph showing total rupture failure rates from the PIPIN MC PIPIN case vs. the all science and data changes case (log-log plot)

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Figure 3 Plot comparing rupture failure rates (including operational data) from the

”MC PIPIN” version and the “All changes” version on a log-log scale

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50. From the plots it can be seen that the majority of points lie above the line, indicating that the failure rates using the revised version of the model are lower than those from the “MC PIPIN” version. From the log-log plot it can be seen that there are a few points at the lower failure rate end where the “All changes” version produces higher failure rates than the “MC PIPIN” version. Given that these are at rates between 1 x 10-8 and 1 x 10-9 per km per year then it is unlikely that this increase in failure rates will have a large impact on pipeline assessment.

51. All of the 584 test cases have been run through MISHAP [3] via the Pipeline RISKAT spreadsheet, for both the “All changes” version and also for the original version of PIPIN (“Original” version). The land-use planning zones calculated for the two cases have been compared and, as before, summary statistics have been calculated showing the mean, maximum and minimum values for the inner, middle and outer zones, together with the standard deviation. The values quoted are based on results from the “All changes” version divided by results from the “Original” version, and can be seen in Table 3.

Table 3 Summary statistics for the comparison of land-use planning zones generated from the “All changes” and the “Original” versions of PIPIN

Inner zone Middle zone Outer zone Mean 1.00 0.82 0.90 Maximum 1.00 1.00 1.06 Minimum 0.48 0.05 0.13 Standard deviation 0.03 0.22 0.12

52. The results shown in Table 3 indicate that, on average, there is no significant change to the inner land-use planning zone by moving to the revised version of PIPIN, and there is a decrease to the middle and outer zones. On further investigation, there are only two cases where the inner zone changes. This is not surprising given that, in the majority of cases, the inner zone is set to the BPD (Building Proximity Distance) [3]. For the middle zone, there are 152 pipelines where the factor is less than 0.75 and 63 pipelines where the factor is less than 0.5 (i.e. the zone has more than halved in size). For the outer zone, there are 61 pipelines with a factor less than 0.75 and 9 pipelines with a factor less than 0.5. There are also 4 pipelines for which the outer zone has increased in size using the “All changes” version of PIPIN. For all 4 of these pipelines, the increase is less than 10%.

53. The 9 cases where there was a decrease of more than 50% in the size of the outer zone are detailed in Table 4. For each of these cases, the “MC PIPIN” version has also been run, i.e. the Monte Carlo version that replicates the original FORM/SORM version of the code (“Original” version).

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Table 4 Pipeline parameters and outer zone values from the “All changes”, ”MC PIPIN” and “Original" versions of PIPIN

Outer zone (m) Pipeline id

Diameter (mm)

Wall thickness (mm)

Grade of steel

Max. Operating Pressure (barg)

Depth of cover (m)

Original “MC PIPIN” version

“All changes” version

130 609.6 15.9 API5L X52

38 0.83 75 55 36

139 609.6 15.9 API5l X46

37.2 1.1 40 11 17

165 609.6 14.3 API5L X60

26.2 1.1 7 4 3

270 457.2 9.5 API5L X52

19 1.1 48 41 15

281 406.4 15.9 API5L X52

59 1.1 13 10 3

341 323.8 12.7 API5L X52

38.6 0.7 19 9 3

404 323.8 9.5 API5L X46

24 1.0 32 25 15

471 273 12.7 API5L X46

67 1.1 31 14 4

525 219.1 12.7 API5L X42

67 1.1 10 4 4

54. In some of the cases, the change in zone size appears to be related to the change in solution method rather than the change to the science and data in the model. On further investigation, these particular pipelines also correspond to some of those that saw the largest changes when moving to the new solution method [4]. These pipelines also have failure rates that are at the lower end of the failure rates seen for pipelines.

55. The mean distance to the outer zone has been calculated from the results for the 584 test cases for the various versions of PIPIN. The mean outer zone distances were found to be 87 m, 85 m and 79 m for the “Original”, “MC PIPIN” and “All changes” versions respectively. The maximum values obtained for the outer zone distance from the 584 test cases are 390 m, 390m and 385 m for the “Original”, “MC PIPIN” and “All changes” versions respectively. In all cases in Table 4, the outer zone is relatively small when compared to the mean and maximum distances obtained for the 584 test cases. Therefore, a relatively small change in terms of the number of metres actually has a large impact in terms of percentages for these 9 cases detailed in Table 4.

56. Figure 4 plots the values for the inner zone from the original case against the “All changes” version. If a point lies above the line then the value obtained was higher in the “Original” version than in the “All changes” version. Figure 5 compares the values for the middle zone from the “Original” version and the “All changes” version. Figure 6 plots the outer zone on a linear scale for these cases and Figure 7 also plots the outer zone, but on a log-log scale. Figure 7 more clearly shows the outliers described in Table 4.

57. From the figures it can be seen that, in the majority of cases, the middle and outer zones are smaller using the new version of PIPIN. In a few cases, this difference is more significant but there are very few cases where the zones have increased in size by moving to the new version of

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PIPIN. The figures show that there are only two cases with a noticeable difference in the size of the inner zones; in both of these cases, the zones are smaller in the “All changes” version.

58. It is not possible to explain in detail why the middle zone shows a much greater variation for the different PIPIN versions than the outer zone. This variation is due to the calculation methodology within MISHAP, which is not being investigated in this study.

Comparison of all changes case to original case for the inner zone

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

All changes (m)

Orig

inal

(m)

Figure 4 Plot comparing the inner zone generated by the original PIPIN (“Original” version) and the “All changes” version

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Comparison of all changes case to original case for the middle zone

0

50

100

150

200

250

300

350

400

0 50 100 150 200 250 300 350

All changes (m)

Orig

inal

(m)

Figure 5 Plot comparing the middle zone generated by the original PIPIN (“Original” version) and the “All changes” version

Comparison of all changes case to original case for the outer zone

0

50

100

150

200

250

300

350

400

450

0 50 100 150 200 250 300 350 400 450

All changes (m)

Orig

inal

(m)

Figure 6 Plot comparing the outer zone generated by the original PIPIN (“Original” version) and the “All changes” version

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Comparison of all changes case to original case for the outer zone - log log scale

1

10

100

1000

1 10 100 1000

All changes (m)

Orig

inal

(m)

Figure 7 Plot comparing the outer zone generated by the original PIPIN (“Original” version) and the “All changes” version on a log-log scale

5.2 RESULTS FROM THE OPERATIONAL VERSIONS OF THE MODEL

59. The analysis performed in Section 5.1 was repeated using the operational versions of the model, where 10 runs were carried out for each pipeline and the mean values obtained from these 10 runs reported. These versions are:

• MCPIPIN, which is the model that replicates the original version of PIPIN, i.e. the operational version of the “MC PIPIN” version of the code; and

• PIPINV3, which includes all the science and data changes, i.e. the operational version of the “All changes” version of the code.

60. MCPIPIN is currently the standard model used by HSE; the “Original” version of PIPIN used in the testing was the version used by HSE at the start of this programme of work. PIPINV3 is the proposed replacement for MCPIPIN.

61. Statistics have been derived based on dividing the results from PIPINV3 by those from MCPIPIN, which can be seen in Table 5 for the TPA only case and in Table 6 for the case when the historical failure rates are also included. A value greater than 1 indicates that PIPINV3 is producing higher failure rates than MCPIPIN, whilst a value of less than one indicates that PIPINV3 is producing lower failure rates.

Table 5 Summary statistics of the comparison of PIPINV3 with MCPIPIN – TPA only


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Table 6 Summary statistics of the comparison of PIPINV3 with MCPIPIN – total rates


62. If Table 5 and Table 6 are compared with Table 1 and Table 2, it can be seen that there is very little difference in the results. In other words, the differences between the “MC PIPIN” and “All changes” versions are replicated by the change from MCPIPIN to PIPINV3.

63. As before, all of the 584 test cases have been run through MISHAP [3] via the Pipeline RISKAT spreadsheet for PIPINV3 and the “Original” version of PIPIN. The land-use planning zones calculated for the two cases have been compared and, as before, summary statistics have been calculated showing the mean, maximum and minimum values for the inner, middle and outer zones, together with the standard deviation. The values quoted are based on results from PIPINV3 divided by the “Original” version. These values are shown in Table 7.

Table 7 Summary statistics for the comparison of land-use planning zones generated from PIPINV3 and the “Original” version of PIPIN

Inner zone Middle zone Outer zone Mean 1.00 0.82 0.90 Maximum 1.00 1.00 1.06 Minimum 0.48 0.05 0.10 Standard deviation 0.03 0.22 0.12

64. If the results in Table 7 are compared with those in Table 3 it can be seen that there are only very minor differences in the two sets of comparisons. In other words, PIPINV3 is replicating the results seen in the development version of PIPIN, the “All changes” version. The numbers of pipelines where the factor is less than 0.75 or less than 0.5 in both the middle and outer zones has also only changed slightly. For the middle zone, there are now 151 pipelines with a factor less than 0.75 (compared to 152 pipelines for the “All changes” comparison) and 63 pipelines with a factor less than 0.5 (the same as for the “All changes” comparison). For the outer zone, 59 pipelines have a factor less than 0.75 (compared to 61 pipelines for the “All changes” comparison) and 7 pipelines have a factor less than 0.5 (compared to 9 pipelines for the “All changes” comparison).

65. The pipelines showing the greatest variation in outer zone have been investigated further as in the “All changes” version comparison (the results of which are illustrated in Table 4). The results of this comparison for PIPINV3 against the results from MCPIPIN and the “Original” version of PIPIN are shown in Table 8. The 9 pipelines that produced the largest variation in the “All changes” version comparison (as described in Section 5.1) were considered in this analysis. All 9 pipelines were considered for illustrative purposes, even though pipelines 130 and 139 no longer show a reduction of 50% or less in the outer zone distance generated by PIPINV3 when compared to the “Original” version.

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Table 8 Pipeline parameters and outer zone values from PIPINV3, MCPIPIN and the original version of PIPIN

Outer zone (m) Pipeline id

Diameter (mm)

Wall thickness (mm)

Grade of steel

Max. Operating Pressure (barg)

Depth of cover (m)

Original MCPIPIN PIPINV3

130 609.6 15.9 API5L X52

38 0.83 75 55 38

139 609.6 15.9 API5l X46

37.2 1.1 40 11 20

165 609.6 14.3 API5L X60

26.2 1.1 7 4 3

270 457.2 9.5 API5L X52

19 1.1 48 41 15

281 406.4 15.9 API5L X52

59 1.1 13 10 3

341 323.8 12.7 API5L X52

38.6 0.7 19 10 3

404 323.8 9.5 API5L X46

24 1.0 32 24 15

471 273 12.7 API5L X46

67 1.1 31 14 3

525 219.1 12.7 API5L X42

67 1.1 10 5 4

66. By comparing Table 8 with Table 4, it can be seen that the results:

• are identical for pipelines 130, 139, 165, 270, 281 and 471 when comparing MCPIPIN with the “MC PIPIN” version of the code;

• are identical for pipelines 165, 270, 281, 341, 404 and 525 when comparing PIPINV3 to the “All changes” version of the code;

• vary by 1 m for pipelines 341, 404 and 525 between MCPIPIN and the “MC PIPIN” version;

• vary by 1 m for pipeline 471 between PIPINV3 and the “All changes” version; • vary by 2 m for pipeline 130 between PIPINV3 and the “All changes” version; and • vary by 3 m for pipeline 139 between PIPINV3 and the “All changes” version.

67. The mean distance to the outer zone distance derived from the 584 test cases is 79 m for PIPINV3 and 85 m for MCPIPIN. These distances correspond to the mean values obtained when running “MC PIPIN” and the “All changes” versions of the code (Paragraph 55).

68. In summary, it appears that the operational versions of PIPIN, i.e. PIPINV3 and MCPIPIN, replicate to a high degree of accuracy, the results seen when the “All changes” version and “MC PIPIN” versions of the model are run. PIPINV3 and MCPIPIN perform 10 runs for each pipeline and calculate the mean of these 10 runs to generate a result. The development versions, “MC PIPIN” and “All changes” only carry out one run for each pipeline. The replication of the results from the use of these different versions provides confidence that the operational versions have implemented all the modifications correctly.

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6 CONCLUSIONS AND RECOMMENDATIONS

69. PIPIN has been rewritten to incorporate a new solution method, improvements to the science behind the model and updates to the data used in the model. The advantages of this are:

• Each stage of the rewrite has been clearly documented in an HSL report [4, 5, 6, 7] allowing for transparency of the methodologies used, and for ease of updating of the code in the future should further revisions to the science or data be required;

• The model is more robust than the original version of the model;

• The model has been reviewed by an independent expert in fracture mechanics;

• The model has gained independence from the industry standard model, FFREQ, through the modification to the micro-crack correlation;

• The source code is now available should further updates be required in the future, unlike the original version of PIPIN where HSE have never had access to the code.

70. The overall impact of these changes has been to reduce the failure rates for the majority of the cases investigated. In terms of the land-use planning zones, on average, there is a negligible impact on the distance to the inner zone generated. The impact of the changes may be more significant in the middle and outer zones, with the middle zone showing the greatest variation. For the outer zone it has been shown that those pipelines which show the greatest percentage difference in zone size, have relatively small zones and so a large percentage change equates to a relatively small actual change in the distance generated.

71. In conclusion, it is recommended that HSE adopt the revised version of PIPIN as their standard pipeline failure rate model. It is suggested that this model is named PIPINV3 to distinguish it from the original model.

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7 REFERENCES

1. Linkens D (1997). Gas pipeline failure frequency predictions – probabilistic fracture models. WSA Report No. AM5076/RSU8000/R1.

2. Linkens D, Shetty NK and Bilo M (1998). A probabilistic approach to fracture assessment of onshore gas-transmission pipelines, Pipes and Pipelines International Vol. 43 (No 4), pp5-16.

3. Kinsman P and Lewis J (2000). Report on a study of international pipeline accidents. HSE Contract Research Report 294/2000.

4. Chaplin Z (2012). Rewriting the PIPIN code to use a Monte Carlo solution approach. HSL report MSU/2012/40.

5. Chaplin Z (2013). Science updates to HSE’s PIPeline INtegrity model (PIPIN). HSL report MSU/2013/02.

6. Chaplin Z (2012). Update of pipeline failure rates for land use planning assessments. HSL report MSU/2012/38.

7. Chaplin Z (2013). Data updates to HSE’s PIPeline Integrity model (PIPIN). HSL report MSU/2011/34/1.

8. Thoft-Christensen P and Baker MJ (1982). Structural reliability theory and its applications, Springer-Verlag 1982.

9. Shetty NK, Gierlinski JT, Liew SK and Mitchell BH (1996). Reliability of an offshore platform under pool and jet fires, 15th Int. Conf. On Offshore Mechanics and Arctic Engineering, Florence, 1996.

10. Shetty NK, Gierlinski JT, Smith JK and Stahl B (1997). Structural system reliability considerations in fatigue inspection planning, Int. Conf. On Behaviour of Offshore Structures, BOSS-97, Delft, 1997.

11. CEGB (1996). Assessment of the integrity of structures containing defects. Report R/H/R6.

12. AFAA (2009). Technical review of PIPIN. Andrew Francis And Associates, AFAA-R0145-09, Revision 02.

13. British Standard (2007). Guide to methods for assessing the acceptability of flaws in metallic structures. BS 7910:2005.

Published by the Health and Safety Executive 12/15



RR1039

www.hse.gov.uk

The Health and Safety Executive (HSE) uses the PIPIN (PIPeline INtegrity) model to determine failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure of pipelines (pinhole, small hole, large hole, and rupture). PIPIN uses two approaches to determine failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, ground movement and other events, corrosion, and third party activity); and a predictive model that uses structural reliability techniques to predict the failure frequency due to third party activity only. The predictive model uses historical data in the form of damage data distributions and strike rates as inputs to the fracture mechanics equations. HSE asked the Health and Safety Laboratory (HSL) to rewrite PIPIN using a Monte Carlo solution approach, to update the science in the model based upon peer review recommendations, to update the damage data used in the predictive model, and to update the historical operational experience data. The effect of the revised model on the results generated from a set of 584 pipelines has been investigated and it was shown that the combined effect of all the modifications is to reduce the failure rates, on average, for all hole sizes compared to the original model.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

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