Science updates to HSE’s PIPeline INtegrity model (PIPIN) · PDF fileScience updates to...

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

Health and Safety Executive

Science updates to HSE’s PIPeline INtegrity model (PIPIN)

RR1037Research Report

Zoe ChaplinHealth and Safety LaboratoryHarpur HillBuxtonDerbyshire SK17 9JN

The Health and Safety Executive (HSE) use the PIPIN (PIPeline INtegrity) model to determine failure frequencies of major hazard 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); and a predictive model that uses structural reliability techniques to predict the failure frequency due to third party activity (TPA) only. The science underlying the TPA model has undergone a peer review with a number of recommendations made for improvements. HSE asked the Health and Safety Laboratory (HSL) to investigate the recommendations of the peer review and ascertain which recommendations improved the scientific basis of the model. HSL considered each of the recommendations in turn and the impact on the failure rates calculated for a set of 584 pipelines. Following discussions between HSL and other experts, one of the recommendations was rejected. The effect of implementing the remaining recommendations is to increase the failure rates, on average, although some pipelines see a decrease in the failure rate calculated.

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 DESCRIPTIONS OF THE VARIOUS CASES ......................................... 3 2.1 Description of the Base Case and the Test Runs .................................... 3 2.2 Removing the Modelling Uncertainties .................................................... 4 2.3 Modifying the Limiting Hoop Stress ......................................................... 4 2.4 Using a Different Charpy energy-fracture toughness Correlation ............ 4 2.5 Revision to the Rupture Calculation......................................................... 5 2.6 Revising the Micro-crack Correlation ....................................................... 7 2.7 Discussion ............................................................................................. 10 2.8 All Changes Combined .......................................................................... 11

3 RESULTS.............................................................................................. 12 3.1 Removing the Modelling Uncertainties .................................................. 12 3.2 Modifying the Limiting Hoop Stress ....................................................... 13 3.3 Using a Different Charpy energy-fracture toughness Correlation .......... 13 3.4 Modifying the Rupture Calculation ......................................................... 14 3.5 Revised Micro-crack Correlation............................................................ 15 3.6 Applying all the changes simultaneously ............................................... 17 3.7 Discussion ............................................................................................. 17

4 CONCLUSIONS AND RECOMMENDATIONS ..................................... 18 4.1 Recommendations................................................................................. 18

5 APPENDICES ....................................................................................... 19 5.1 Appendix A – Pipeline Parameters for the 584 Test Cases ................... 19 5.2 Appendix B – The Charpy V-notch impact energy values...................... 36 5.3 Appendix C – The British Gas test data used to derive the Micro-crack correlation......................................................................................................... 37 5.4 Appendix D – The graphs of Micro-crack depth against various parameters used to derive the Micro-crack correlation in section 2.6............... 45 5.5 Appendix E – Additional information obtained on the Micro-crack Correlation........................................................................................................ 49

6 REFERENCES ...................................................................................... 51

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EXECUTIVE SUMMARY The Health and Safety Executive (HSE) use a computer code PIPIN (PIPeline INtegrity) to determine failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure (pinhole, small hole, large hole and rupture) of pipelines, which are used in other tools, such as MISHAP (Model for the estimation of Individual and Societal risk from HAzards of Pipelines) to calculate the level of risk and is used to set land use planning zones around pipelines. PIPIN contains two approaches to determine failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, natural 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 (TPA) only. This report discusses modifications and improvements to the science within the TPA model based on the Monte Carlo version of PIPIN.

The Monte Carlo version of PIPIN, written by the Health and Safety Laboratory (HSL) in 2009, was designed to replicate the science within the original version of PIPIN, which was written in the late 1990s, and which used a FORM/SORM (First or Second Order Reliability Method) approach to solve the fracture mechanics equations. It was recognised at the time that HSL rewrote the model, that there was a need to review some of the assumptions and equations used. An independent review of the model was carried out which identified several key areas to be investigated and changes that could be made to improve the validity of the model. This report discusses these modifications and the results that are obtained when they are applied.

Objectives

The objectives of this project were to investigate the impact on the calculated failure rates as a result of changes to the science within PIPIN. The changes had been suggested after the model was independently reviewed and were:

• Removing the modelling uncertainties used on a number of equations;

• Modifying the limiting hoop stress equation in the dent-gouge model;

• Using an alternative Charpy energy-fracture toughness correlation;

• Modifying the rupture calculation; and

• Revising the micro-crack correlation.

Main Findings

This report details the effects of implementing each of the changes independently, and also from implementing all of these changes simultaneously.

The largest impact is seen from using the Charpy energy-fracture toughness correlation published in the British Standard, BS 7910. Further investigations into this correlation, however, have revealed that it is considered to be overly conservative and that the correlation currently used within PIPIN is more representative of what actually occurs when a pipeline is struck.

Of the remaining areas that were considered, modifying the micro-crack correlation had the most significant impact on the results. The effect of this has been to increase failure rates for ruptures on average by a factor of 2 with smaller increases seen for the other hole sizes.

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Removing the modelling uncertainties also on average increased failure rates for all the hole sizes. This ranged from a 2% increase for pinholes up to 8% for ruptures, when the TPA only results are considered. These reduce to 1% and 7% when the operational failure rates are included as well.

Modifying the rupture calculation decreased the rupture failure rates in all cases. This slightly offsets the changes seen by removing the modelling uncertainties and changing the micro-crack correlation when looking at all of the changes applied simultaneously.

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Recommendations

It is recommended that all the changes described in this report, with the exception of the revision to the Charpy energy-fracture toughness correlation, are incorporated into the Monte Carlo version of PIPIN and that this version becomes the standard model for use within HSE

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1 INTRODUCTION

1. The Health and Safety Executive (HSE) use a computer code PIPIN (PIPeline INtegrity) [1, 2, 3] to determine failure frequencies of major hazard pipelines. PIPIN calculates the failure rates for four categories of failure (pinhole, small hole, large hole and rupture) of pipelines, which are used in other tools, such as MISHAP (Model for the estimation of Individual and Societal risk from HAzards of Pipelines) [4] to calculate the level of risk and is used to set land use planning zones around pipelines. PIPIN contains two approaches for the determination of failure rates: an approach based on operational experience data, which generates failure rates for four principle failure modes (mechanical failures, natural 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 (TPA) only. This report discusses modifications and improvements to the science within the TPA model based on the Monte Carlo (MC) version of PIPIN [3].

2. The Monte Carlo version of PIPIN, written by the Health and Safety Laboratory (HSL) in 2009 [3], was designed to replicate the science within the original version of PIPIN, which was written in the late 1990s, and which used a FORM/SORM (First or Second Order Reliability Method) approach to solve the fracture mechanics equations. It was recognised at the time that HSL rewrote the model, that there was a need to review some of the assumptions and equations used. An independent review of the model was carried out by Francis [5] which identified several key areas to be investigated and changes that could be made to improve the validity of the model. A list of each of these areas follows and they will be dealt with in more detail within the subsequent sections of this report.

• 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 they should therefore be removed.

• A simplified equation for the limiting hoop stress was used when a dent and gouge were formed through plastic collapse compared to when just a gouge was formed through plastic collapse. The same mechanism is involved in both cases so the use of a simplified equation was considered unnecessary; the same equation should be used in both cases.

• 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. It is used as it is not possible to directly measure the fracture toughness. The equation currently incorporated within PIPIN was considered to be unrepresentative and over-predicts the toughness. The independent review considered that a published relationship, such as that in BS 7910 [6] should be used in preference.

• The rupture calculation was considered to be 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.

• The micro-crack correlation was originally calculated by comparing results with those from FFREQ, the industry standard failure frequency model, rather than using existing data. It was also noted 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

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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.

3. The objectives of the project were to investigate each of the areas listed above, consider their validity and to assess their impact on a set of test cases. For all cases, failure rates were calculated using both the TPA only model and again but also including the operational failure rates from mechanical, natural/other and corrosion failures.

4. The remainder of the report is structured as follows:

• Section 2 describes the base case and each of the science and code changes that were investigated. The test cases that have been used to assess the impact of each change are also described;

• Section 3 presents the results from each of the runs;

• Section 4 presents the conclusions and recommendations; and

• Section 5 contains the appendices.

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2 DESCRIPTIONS OF THE VARIOUS CASES

2.1 DESCRIPTION OF THE BASE CASE AND THE TEST RUNS

5. The Monte Carlo (MC) version of PIPIN [3] was used for all runs detailed in this report. Its science replicates that of the original version of PIPIN [1, 2] with the only major difference being in the solution method i.e. Monte Carlo as opposed to FORM/SORM [7, 8, 9]. The MC version of the model reproduces, to a high degree of accuracy, the results using the FORM/SORM solution method and hence it was used as the base case for all the subsequent tests. For a full description of the science within PIPIN and the assumptions made, refer to Chaplin [3]. Where changes were made to the base case to test a specific assumption or area of the science then details are given.

6. There are three fracture mechanics models within the PIPIN code, two of which are run with two different types of data, giving a total of five models. Failure probabilities are calculated for each of these, which are briefly described below:

• A gouge model that models the plastic collapse of the pipeline using either gouge data or, with a slight modification, dent-gouge data (i.e. this model is run twice with two different types of data and hence can be considered as two models);

• A dent-gouge model that models failure by fracture; and

• A rupture model that models the likelihood of a leak, resulting from either plastic collapse or fracture, leading to a rupture (i.e. this is run with two different types of data and hence can be thought of as two models).

7. In all cases, to determine whether or not a particular pipeline will fail or not, a comparison of the values calculated in the fracture mechanics models is made with the R6 Rev. 3 fracture assessment procedure [10], otherwise known as, and hereafter referred to, as the failure assessment diagram (FAD) curve. 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.

8. The Monte Carlo solution method produces an overall probability of failure by performing millions of iterations for each pipeline under consideration. Each of the iterations will have slightly different values for the pressure, wall thickness etc. which vary according to statistical distributions applied to each of the parameters in question. For each iteration, the fracture mechanics models will be run and an assessment made against the FAD curve to determine if, for this particular parameter set, the pipeline would fail or not. A total is kept of the number of failures together with all the iterations performed and a probability is then determined by dividing the former by the latter. Once the value of the probability ceases to change for each iteration, by more than a small error margin that can be set by the user (currently 10-5), then the model is said to have converged and the final value of the probability is used to derive the failure frequencies.

9. The failures are divided into four different types according to the hole size and the probabilities calculated from each section of the fracture mechanics are apportioned appropriately. The different sizes are:

• pinhole: ≤ 25 mm diameter;

• small hole: > 25 to ≤ 75 mm diameter;

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• large hole: > 75 to ≤ 110 mm diameter; and

• rupture: > 110 mm diameter.

10. The test runs considered 584 sets of pipeline parameters that are representative of pipelines within the UK natural gas transmission system. The pipeline parameters for each of the test runs are listed in Appendix A.

2.2 REMOVING THE MODELLING UNCERTAINTIES

11. A number of modelling uncertainties were applied throughout the original PIPIN code to represent the level of uncertainty involved in the approximations that have had to be made to the science. These were factors that were applied to several of the equations in the gouge, dent-gouge and rupture models, that were either modelled as normal or lognormal distributions with specified means and coefficient of variance (see Chaplin [3] for the full details). The independent review by Francis [5] stated that there appeared to be no scientific basis, either for their use or for the distributions chosen. It was felt that these uncertainties were unnecessary and were, in fact, adding further uncertainty to the model. For this test, all the modelling uncertainties were removed.

2.3 MODIFYING THE LIMITING HOOP STRESS

12. The equation for the limiting hoop stress in the gouge model is:

)(11

1

ρ

σσ

Mtd

td

y

L

−

⎟⎠⎞

⎜⎝⎛ −

= (1)

where: • σL is the limiting hoop stress (MPa); • σy is the yield stress (MPa); • d is the gouge depth (mm); • t is the pipeline thickness (mm); and • M(ρ) is the Folias bulging magnification factor (described later in equation 9).

13. If a dent-gouge forms from plastic collapse, which implies the gouge model is run using dent-gouge data, then the limiting hoop stress equation, in the original version of PIPIN, is simplified to:

⎟⎠⎞

⎜⎝⎛ −=

td

yL 1σσ (2)

14. A gouge, however, may be thought of as a special case of dent-gouge with the dent depth set to zero and so, according to Francis [5] there is no need to apply this simplification. The 584 test runs were therefore performed assuming that equation 1 could be applied in both cases, i.e. to both versions of the gouge model.

2.4 USING A DIFFERENT CHARPY ENERGY-FRACTURE TOUGHNESS CORRELATION

15. It has been stated by Francis [5] that the Charpy energy-fracture toughness correlation used in the existing model over-predicts the toughness by a considerable margin. It has therefore been

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suggested that the correlation published in BS 7910 [6] be used instead. In practice, this entails replacing the following equation:

cc A

ECVNK 100008334.0121

×××= (3)

where: • K1c is the fracture toughness (MPa √m); • CVN is the 2/3 Charpy energy (J); • E is the Young’s modulus (210 GPa); and • Ac is the Area of Charpy (mm2), assumed to be 66.7 mm2.

with:

( )( ) 20/252012 25.01 +−= tCK vc (4)

where: • Cv is the lower bound Charpy V-notch impact energy at the service temperature (J);

and • t is the pipeline thickness (mm).

16. An upper bound is also provided in BS 7910 such that K1c does not exceed the value given by:

5554.01 += vc CK (5)

17. It should be noted that the definition of the Charpy energy varies between the equations 3 and 4. The definition in equation 4 entails consulting the relevant standards for the different grades of steel to ascertain the minimum Charpy V-notch impact energy. This is then fed into the revised version of PIPIN and the distribution used to sample the values remains unchanged (i.e. a lognormal distribution with a coefficient of variance of 0.25). In contrast, equation 3 uses a fixed value for the 2/3 Charpy energy of 20 J.

18. The values for the Charpy V-notch impact energy are given in Appendix B. It should be noted that the values for the grades of steel API5L X46 and X56 have been estimated, as the literature does not report the actual values for these cases.

2.5 REVISION TO THE RUPTURE CALCULATION

19. In the current version of PIPIN, once the probabilities from the individual fracture mechanics models have been calculated, overall values for the different hole sizes and ruptures are calculated. The equation used for the ruptures is:

( )( )

( ) ( )drupturemm110dgougedentdg

grupturemm110gougeg

drupturedgougedentdg

grupturegougegrupture

P1wdgPPf

P1wgPf

PPPf

PPfFailure

−××+×

+−×××

+×+×

+××=

>

>

(6)

where: • Failurerupture is the overall probability that a rupture will occur;

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• fg is the gouge incident frequency, or strike rate, which is currently assumed to be 1.29×10-6 m-1 yr-1;

• fdg is the dent-gouge incident frequency, or strike rate, which is currently assumed to be 2.07×10-7 m-1 yr-1;

• wg>110mm is the probability of a hole size greater than 110 mm for gouges; • wdg>110mm is the probability of a hole size greater than 110 mm for dent-gouges; • Pgrupture is the probability of rupture from a gouge; • Pdrupture is the probability of rupture from a dent-gouge; • Pdent is the probability of a failure from a dent-gouge; • Pgouge is the probability of a failure from a gouge using gouge data; and • Pdgouge is the probability of a failure from a gouge using dent-gouge data.

20. Equation 6 assumes statistical independence between each of the probabilities which is unlikely to be true. Francis [5] derives an equation for the probability of failure from a rupture from first principles and ascertains that equation 6 is incorrect. Instead, he proposes:

( )( )

( ) ( ) )(

)(

mm110cdrupturemm110dgougedentdg

mm110cgrupturemm110gougeg

drupturedgougedentdg

grupturegougegrupture

llHPwdgPPf

llHPwgPf

PPPf

PPfFailure

−×−×+×

+−×−××

+×+×

+××=

>

>

(7)

where: • lc is the critical crack length; • l110 mm is the equivalent length for a hole of diameter 110 mm; and • H is the Heaviside step function, which is 0 if l110 mm is greater than lc and 1 otherwise.

21. The third and fourth parts of equation 7 are zero if the critical crack length is less than the equivalent length for a hole of diameter 110 mm, which is correct when it is considered that an unstable leak that leads to a rupture will already have been captured by the first two terms of equation 7 (i.e. the rupture model will already have calculated that they are ruptures). The last two terms of the equation capture all stable leaks where the critical crack length is greater than 110 mm as these will be classed as ruptures by HSE. They will not, however, have been calculated to be ruptures by the rupture model and so will not have been included in the first two terms of the equation. The last two terms in the equation remove those leaks above 110 mm, which have already led to a rupture (and hence will have been calculated by the rupture model), to avoid double counting them. This still assumes a degree of independence between each of the probabilities but it involves fewer assumptions than for equation 6.

22. The use of equation 7 implies the need to calculate the critical crack length. The critical crack length is implicit within the equation for K1, the stress intensity factor, which is shown in equation 8.

1000/)(1 cMK h πρσ= (8)

where: • σh is the pressure hoop stress (MPa); • c is the gouge semi-length (mm); and • M(ρ) is the Folias bulging magnification factor given by:

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5.02 )61.11()( ρρ +=M (9)

where the dimensionless gouge length is:

5.0)/(Rtc=ρ (10)

where: • R is the pipeline external radius (mm); and • t is the pipeline thickness (mm).

23. When the value of Kr (the fracture ratio due to the applied primary and secondary stresses) lies on the failure assessment diagram (FAD) curve [10], K1 is at its critical value and the value of c, the gouge semi-length within the equation for K1, becomes the critical crack semi-length, lc. Kr is given by:

( )( )665.02 7.03.014.01 rLrr eLK −+−= (11)

where Lr is the ratio of the pressure hoop stress to the limiting hoop stress, and is a measure of how close the pipeline is to plastic collapse.

24. In practice, this means solving a cubic equation for every iteration of the model in order to calculate the critical crack semi-length. This is shown in equation 12 where K1 = Kr × K1c (where K1c is given by equation 3).

0100061.1

2

21

3

=−+h

cc K

lRt

lπσ

(12)

25. The critical crack semi-length calculated in this way needs to be multiplied by 2 to generate the full critical crack length. The mean value is calculated across all the iterations and compared against the equivalent length for a hole of diameter 110 mm.

26. An analysis of the critical crack length values calculated for eight different cases implies that a statistical distribution can perhaps be applied to the values and that this may be more appropriate than using the mean value in the rupture calculation. The plots of the values calculated are similar to a normal distribution although they are slightly skewed, therefore a Cauchy distribution may be more appropriate. In this case a median value for the critical crack length should be used in the calculation rather than the mean value. This is not a simple task within the FORTRAN code and would lead to two further large arrays, which would slow the code down significantly. Also, the values from the eight cases investigated shows that the difference between the median and the mean is of the order of 10%. In either case, the critical crack length is lower than the value of the equivalent length for a hole of diameter 110 mm leading to only the first two terms in equation 7 being calculated. This implies that there would be no benefit in moving to use the median value, but there would be a significant cost incurred through the additional time taken per run. The mean value has therefore been used in the rupture calculation instead of the median value.

2.6 REVISING THE MICRO-CRACK CORRELATION

27. Micro-cracks are assumed to be present in the regions of local tensile stresses and they can contribute to the failure of the pipeline wall. In the original version of PIPIN, a correlation was derived to calculate the micro-crack based on the dent depth and the pressure hoop stress. This

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correlation was derived by using 29 runs from the British Gas (BG) model (assumed to be FFREQ), which had been tuned to test data. The original version of PIPIN was then tuned to give the same values of K1 as the BG model. In other words, PIPIN was not explicitly tuned to test data but was instead tuned to a small sample of results from another model that had been tuned to the test data. It should also be noted that the fracture mechanics models in FFREQ do not contain a micro-crack correlation and so the science between the two models is different. Tuning PIPIN to this model may not therefore be as accurate as tuning to a full set of test data.

28. Francis [5] observed that the test data originally generated by BG was publicly available [11] and recommended that PIPIN should be retuned to this data. He also stated that the current understanding of micro-cracks is that they only occur in the event of a dent. Therefore, if the dent depth is zero, the micro-crack should be zero. The current correlation within PIPIN does not satisfy this rule and it was suggested that a revised correlation should ensure that this equality holds. The BG test data can be found in Appendix C. It should be noted that, whilst 132 tests were originally conducted, full information on eight tests was not available and so 124 were used in this analysis.

29. The BG test data was generated at a time when fracture mechanics was not as sophisticated as it is now and the significance of micro-cracks had not been identified. In order to use the data to derive a micro-crack correlation it is necessary to use BG’s data as input to a cut-down version of PIPIN that does not have all the distributions associated with the damage data and input parameters and which does not calculate a probability of failure. Instead, an iterative method is performed to calculate a value of the micro-crack depth in the dent-gouge model such that the point lies within a certain margin of the failure assessment diagram (FAD). In order to do this, a first guess is made as to the value of the micro-crack. This guess is fed into the dent-gouge model and the value of Kr is calculated. This is then compared against the corresponding value of KrFAD i.e. the value of Kr on the FAD, and, if the distance between the two is greater than some permitted tolerance, the value of the micro-crack is adjusted and the Kr values are recalculated. Once the tolerance level has been satisfied then the value of the micro-crack is recorded. The tolerance was set at 0.000001.

30. Once the values of the micro-crack depth had been obtained, a number of diagrams were plotted of the micro-crack depth against various other parameters to determine if a relationship could be inferred. A regression analysis could then be performed within Excel and a curve fitted that also showed the coefficient of determination (R2) value as a measure of the goodness-of-fit. The higher the R2 value then the greater the confidence that a relationship between the plotted parameters and the micro-crack exists. Appendix D illustrates the graphs generated in this process.

31. The graphs indicate that the best fit can be found by using a relationship with pressure, the wall thickness and the dent depth. The equation thus derived for the micro-crack depth a, is:

))(2/)((0013.05518.3 gdtddDPea ++×−= (13)

where: • P is the pressure (barg); • D is the diameter (mm); • t is the wall thickness (mm); • dd is the dent depth (mm); and • gd is the gouge depth (mm).

32. This gives a coefficient of determination (R2) value of 0.7826. Unfortunately, the micro-crack depth is not zero when the dent depth is zero, which is a potential inaccuracy according to

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Francis [5], but it was found that the micro-crack depth was affected more by the pressure than by the dent depth. Francis has also stated, however, that in reality, gouging may contribute to micro-cracking but this has not been fully verified as yet. Although it has not been possible to derive a relationship that produces a micro-crack depth of zero when the dent depth is zero, the revised correlation is also dependent on the gouge depth, which Francis has stated may be of importance as well. The exponential equation given in equation 13 has therefore been used.

33. Another measure of how well the correlation is reproducing the test data is to plot the calculated values of Kr (the fracture ratio due to the applied primary and secondary stresses) against the Lr value (a measure of how close the pipeline is to plastic collapse) and compare this with the failure assessment diagram (FAD) [10]. Figure 1 plots these values for the original micro-crack correlation whilst Figure 2 reports the values for the revised micro-crack correlation. As can be seen from both figures, the revised correlation appears to more closely represent the test data in that the points lie closer to the FAD line and they appear to follow the downward trend seen in the curve.

Failure assessment diagram using the original micro-crack correlation

0

0.5

11.5

2

2.5

3

0 0.5 1 1.5 2

Lr

Kr Calculated values

FAD curve

Figure 1 Diagram showing the values of the BG test cases against the FAD for the original micro-crack correlation

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Failure assessment diagram using the revised micro-crack correlation

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2

Lr

Kr Calculated values

FAD curve

Figure 2 Diagram showing the values of the BG test cases against the FAD for the revised micro-crack correlation

34. In terms of the values of the micro-crack depth produced, for the BG test cases listed in Appendix C, the values ranged from 0.17 mm to 0.37 mm for the original correlation and from 0.002 mm to 2.58 mm for the new correlation. The mean value using the revised correlation is 0.59 mm with a standard deviation of 0.62 mm.

35. After the analysis was performed, additional information became available which is discussed in Appendix E. This did not change the form of the correlation presented in equation 13.

2.7 DISCUSSION

36. Taking each of the suggestions by Francis [5] in turn, it is necessary to decide which will genuinely benefit the science within the model and identify any arguments against their use. Each of these suggestions is now discussed further.

37. The reasoning behind the statements by Francis regarding the modelling uncertainties appear sound in that there is very little justification for adding further uncertainty to an already uncertain model. There was also no documentation to state why the specific distributions had been chosen and so they appear to be arbitrary in nature. In addition, the removal of all the uncertainties reduces the number of arrays in the model and will therefore lead to a significant decrease in the time taken for each run. Overall it is concluded that removing the modelling uncertainties will be of benefit to the model.

38. With regard to the change to the limiting hoop stress equation, the reasoning that Francis presents appears logical as a gouge from dent-gouge information is the same as a dent-gouge with the dent depth set to zero. Returning the equation to the more complicated version will therefore potentially remove an unnecessary oversimplification and improve the scientific basis of the model.

39. If Francis is correct in his assertion that the Charpy energy-fracture toughness equation overpredicts the fracture toughness of the material, then it would be necessary to consider alternative correlations. It should be noted that the phrase Francis uses is that it “appears” to overpredict fracture toughness and he implies that a more “realistic” relationship could be found in BS 7910 [6]. The correlation from a recognised and widely used standard, such as that in BS 7910, appears to be a suitable choice for a comparison with what is currently used in the model.

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Further discussions have since been held with experts in the field of fracture mechanics and with industry. In addition, the documentation for the original Charpy energy-fracture toughness correlation by Kiefner [12] has been reviewed, along with the results from PDAM (Pipeline Defect Assessment Manual) [13], a joint industry project in which HSE were involved. The PDAM project compared several correlations including that in BS 7910 and the one within PIPIN (by Kiefner, otherwise known as the NG-18 equations). All of these sources would appear to indicate that the correlations quoted in BS 7910 [6] and in other industry standards are overly conservative and not truly representative when it comes to predicting failures in pipelines, whilst that by Kiefner (or the NG-18 equations) is considered to be more realistic.

40. Another point to consider is that the aim of standards such as BS 7910 is to give guidance to the operator on whether a component can safely stay in service, which leads to a degree of conservatism in the assessment. For example, if a dent is observed with a depth of more than a certain proportion of the diameter, it will be deemed unfit for service, regardless of material or stress considerations. When PIPIN assesses whether or not a pipeline will fail, the aim is to give as accurate a prediction as possible. The decision has therefore been taken to retain the existing correlation, which is that recommended within PDAM. Results obtained by using the BS 7910 correlation will be shown in Section 3 for illustrative purposes, however.

41. The equations presented by Francis regarding the rupture calculation do appear to indicate that some ruptures are currently being counted twice. The analysis that Francis presents is mathematically correct and so it is considered that this change will be of benefit to the model and should be incorporated for accuracy.

42. On investigation of the derivation of the micro-crack correlation currently used within PIPIN, it was discovered how much reliance was placed on a small sample of results that came from runs of another model that does not contain a micro-crack within its science rather than directly from experimental data. Recalculating the micro-crack correlation based on the full experimental data set would therefore appear to be a sensible modification, particularly with regard to the justification of the science contained within the model.

2.8 ALL CHANGES COMBINED

43. An additional test was performed which incorporated all the changes detailed in the previous sections, with the exception of the change in Charpy energy-fracture toughness correlation. This allows all of the proposed modifications to be considered simultaneously so that an overall impact can be assessed. The results of this are shown in section 3.6.

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3 RESULTS

44. The results from each of the modifications considered are now discussed. In all cases, 584 runs were performed and comparisons were made against the base case (i.e. the Monte Carlo version of PIPIN with no science changes). Results from running just the third party activity model (TPA) are shown together with results from running the predictive model combined with the operational failure data for mechanical, natural/other and corrosion failure mechanisms, which gives an overall total rate. The results from each of the test cases were divided by those from the base case and then mean, maximum and minimum values were calculated across the 584 runs. A value greater than 1 means that the science change has led to an increase in the failure rates, a value of 1 indicates no change, and a value of less than one means that the modification to the science leads to a fall in the failure rates.

3.1 REMOVING THE MODELLING UNCERTAINTIES

45. The 584 test cases were run with the modelling uncertainties removed and the results were compared to those from the base case. These figures are shown in Table 1 for the third party activity model only and in Table 2 for the total rates including operational data.

Table 1 Summary statistics from the no modelling uncertainties case – TPA only

Rupture Pin Small Large Mean 1.09 1.03 1.03 1.04 Minimum 0.94 0.88 0.89 0.88 Maximum 1.16 1.10 1.11 1.11

Table 2 Summary statistics from the no modelling uncertainties case – total rates


46. From Table 1 it can be seen that, on average, there is only a 3 to 4% difference in the values calculated across all of the 584 test cases for pin, small and large holes. The difference is 9%, on average, for ruptures. For all the hole sizes, removal of the modelling uncertainties has slightly increased the average failure probabilities although the minimum and maximum values imply that there is some variation in the individual results with some showing larger failure probabilities and some smaller. The minimum values show an overall reduction in failure rates of up to 12% whilst the maximum values show an increase in failure rates of 10 to 11% for pin, small and large holes and 16% for ruptures.

47. The values in Table 2 indicate that the effects seen from the TPA only results are mirrored when all the data is considered but with the figures slightly reduced. For pinholes the mean change is now 1% whilst for small holes it is 2% and it is 3% for large holes. For ruptures the increase in mean value is 7%. The variation in the statistics is also reduced as can be seen from the minimum and maximum values.

48. A further consequence of removing the uncertainties is an increase in the speed of the model. A significant number of random variables is required to generate all the distributions used within the code, and there needs to be one set for every iteration of the Monte Carlo method performed. Removing the uncertainties reduces the number of distributions from 33 to 17, thereby nearly

13

halving the size of the array holding the random numbers and consequently increasing the speed of the program.

3.2 MODIFYING THE LIMITING HOOP STRESS

49. The results from the case where the limiting hoop stress equation was the same in both versions of the gouge model are shown in Table 3 for TPA only and in Table 4 for the total rates including the operational failure data.

Table 3 Summary statistics from the limiting hoop stress case – TPA only


Table 4 Summary statistics from the limiting hoop stress case – total rates


50. From Table 3 it can be seen that, on average, there is no difference in the values calculated using the same version of the limiting hoop stress for the gouge model when both gouge and dent-gouge data are used. The largest reduction in the values seen, across all the hole sizes, was 10% whilst the greatest increase was 12%. The results from Table 4 confirm this result and also show a smaller variation in the range of values seen, with a reduction of up to 5% and an increase of up to 4%.

3.3 USING A DIFFERENT CHARPY ENERGY-FRACTURE TOUGHNESS CORRELATION

51. The results of using the Charpy energy-fracture toughness correlation reported in BS 7910 [6] are shown in Table 5 for the TPA model only and in Table 6 for the case when the operational failure rates are also included. It should be noted that the tables are for information only as it was decided not to use this modification.

14

Table 5 Summary statistics from the change in Charpy energy-fracture toughness correlation – TPA only


Table 6 Summary statistics from the change in Charpy energy-fracture toughness correlation – total rates


52. A significant increase in the failure rates for all the hole sizes can be seen, but the largest increase occurs for ruptures. Francis [5] stated that the correlation used in the existing model over-predicted the toughness of the pipelines. The results would appear to corroborate this as the failure rates have increased. The larger difference in the rupture failure rate can then perhaps be explained by a decrease in the toughness of the material having a greater impact on the larger holes. In other words, if the toughness is reduced then a rupture is more likely to occur, whereas the smaller hole sizes are less likely to be affected. This is also borne out by the fact that the smallest change is seen in pinholes and the magnitude of the change gradually increases as the hole size increases.

3.4 MODIFYING THE RUPTURE CALCULATION

53. The results of applying a different equation for the overall calculation of rupture frequency are shown in Table 7 for the TPA only model and in Table 8 for the case where the operational failures are included as well.

Table 7 Summary statistics from the change in rupture calculation – TPA only


Table 8 Summary statistics from the change in rupture calculation – total rates


54. In most cases, the revised version of the code led to a reduction in the rupture failure frequency, with the mean reduction being 13% for the TPA model only. The values for the three hole sizes remain unchanged, on average, as would be expected. The variations that are seen in the other hole sizes are due to the nature of the Monte Carlo method where differences will be seen when runs are replicated. For more details of this, see Chaplin [3].

15

55. These results are not surprising given that it appears that some ruptures may have been double-counted in the original calculation.

3.5 REVISED MICRO-CRACK CORRELATION

56. The results from using a revised micro-crack correlation are shown in the following two tables, where Table 9 illustrates the values from the TPA model only and Table 10 illustrates the values when the operational failures are included as well.

Table 9 Summary statistics from the change in micro-crack correlation – TPA only

Rupture Pin Small Large Mean 2.41 2.03 2.28 2.37 Minimum 0.73 0.79 0.74 0.72 Maximum 4.43 3.31 4.05 4.32 Standard deviation 0.872 0.610 0.775 0.836

Table 10 Summary statistics from the change in micro-crack correlation – total rates


57. As can be seen from both tables, revising the micro-crack correlation results in an average increase in the failure rates for all hole sizes, with the rates more than doubling in value for the TPA only case. The total failure rates show a similar increase on average, with the rates somewhat lower, particularly for the pinhole, small and large hole case. The rupture values have still more than doubled on average.

58. A histogram has also been plotted which graphically represents the range of values seen. This can be seen in Figure 3 for TPA only and in Figure 4 for the total rates including the operational failure rates.

16

Histogram of TPA results

0

10

20

30

40

50

60

70

80

90

100

0.60

1.00

1.40

1.80

2.20

2.60

3.00

3.40

3.80

4.20

4.60

5.00

Failure rate relative to the base case

Num

ber o

f pip

elin

es

Rupture Pin Small Large

Figure 3 Histogram showing the variation in TPA results when compared to the base case for the revised micro-crack correlation

Histogram of Total results

0

50

100

150

200

250

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

3.00

3.20

3.40

3.60

3.80

4.00

Failure rate relative to the base case

Num

ber o

f pip

elin

es

Total Rupture Total Pin Total Small Total Large

Figure 4 Histogram showing the variation in total failure rates, including operational failures, when compared to the base case for the revised micro-crack correlation

The TPA only results show a significant spread in values for the all the hole sizes, with the pinholes showing slightly less deviation from the base case than for the other hole sizes. Figure 3 illustrates that there are very few cases where the mean value is less than the base case value. For all hole sizes, the number of such cases is less than 30 out of the total 584 test cases (to understand the graph, the values at 1.0, for example, actually cover the range 0.8 < the

17

calculated ratio ≤ 1.0). The total values seen in Figure 4 show less spread, particularly for the pinhole case where most of the values are centred around the 1.0 to 1.8 range (It should be noted that there is a very slight column in Figure 4 corresponding to a ratio of 0.8, which corresponds with the minimum ratio seen being 0.78 in Table 10).

3.6 APPLYING ALL THE CHANGES SIMULTANEOUSLY

59. A test was performed using all the changes described in sections 2.2 to 2.6, with the exception of the Charpy energy-fracture toughness correlation described in 2.4. The results are shown in Table 11 for TPA only and in Table 12 for the case showing the operational failures as well. The ratios are against the base case.

Table 11 Summary statistics from the “all change” case – TPA only


Table 12 Summary statistics from the “all change” case – total rates


60. The values are very similar to those in section 3.5, which reported on the change to the micro-crack correlation. This is not surprising given that this change saw the largest impact on the values individually (ignoring the results from the change to the Charpy energy-fracture toughness correlation). Overall the failure rates have increased for all the hole sizes by incorporating all of the changes. In all cases, some variation is seen with some values increasing and some decreasing. For the total rates, the maximum decrease is 32% for ruptures and in the range 2 to 8% for the other hole sizes, whilst the maximum increase in failure rates is 363% for ruptures and between 187% and 330% for the other hole sizes.

3.7 DISCUSSION

61. The reasoning behind all the modifications to the science that have been tested in this section have already been discussed in Section 2.7. It should be noted that a significant change in the failure rates caused by applying any of these changes should not be an argument, in itself, for not modifying the model. The previous discussion determined which of the proposed modifications enhanced the scientific rigour of the model and an increase in the failure rates should not be used as a justification for returning to a less defensible position.

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4 CONCLUSIONS AND RECOMMENDATIONS

62. The science within PIPIN has undergone an independent review, which has highlighted areas that deserve greater consideration. Investigations have been made in these areas and tests performed to assess the impact that changes in the code will have on the overall failure rates predicted by PIPIN. The largest impact is seen from using the Charpy energy-fracture toughness correlation published in the British Standard, BS 7910 [6]. Further investigations into this correlation, however, have revealed that it is considered to be overly conservative and that the correlation currently used within PIPIN is more representative. It is therefore recommended that this change is not implemented.

63. Of the remaining areas that were considered, modifying the micro-crack correlation had the most significant impact on the results. This is perhaps not surprising when it is considered how the original correlation was derived. Only a small subset of the test data was used (29 tests as opposed to 124) and PIPIN was not directly tuned to this data but to the output from a British Gas model, which did not include micro-cracking. The effect of this has been to increase failure rates for ruptures on average by a factor of 2 with smaller increases seen for the other hole sizes.

64. Removing the modelling uncertainties also on average increased failure rates for all the hole sizes. This ranged from a 3% increase for pinholes up to 9% for ruptures, when the TPA only results are considered. These reduce to 1% and 7% respectively when the operational failure rates are included as well.

65. Modifying the rupture calculation decreased the rupture failure rates in all cases. This slightly offsets the changes seen by removing the modelling uncertainties and changing the micro-crack correlation when all of the proposed changes are implemented simultaneously.

4.1 RECOMMENDATIONS

66. The work described within this report has aimed to improve the science on which PIPIN is founded. Areas such as re-calculating the micro-crack correlation based on the original test data improve the credibility of the model and also helps to make it more independent from the industry model, FFREQ. It is therefore recommended that all of the changes described in this report, with the exception of the revision to the Charpy energy-fracture toughness correlation,, are incorporated into the Monte Carlo version of PIPIN. These changes are as described in section 3.6 and are:

• Removing the modelling uncertainties

• Modifying the limiting hoop stress equation in the dent-gouge model;

• Modifying the rupture calculation; and

• Revising the micro-crack correlation.

67. The Monte Carlo version of PIPIN incorporating these science changes should be used as the standard model for use within HSE.

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5 APPENDICES

5.1 APPENDIX A – PIPELINE PARAMETERS FOR THE 584 TEST CASES

68. Table 13 lists the pipeline parameters used in the set of 584 test cases.

20

Table 13 Pipeline parameters for the 584 test cases

Run ID1 Pipeline diameter (mm)

Pipeline thickness (mm)

Material code

Material grade

Pressure (barg)

Depth of cover (mm) Location

22 1219.2 19.1 API5L X65 75 1100 Rural 23 1219.2 15.88 API5L X80 75 1100 Rural 24 1219.2 15.1 API5L X80 75 1100 Rural 25 1219.2 14.3 API5L X80 75 1100 Rural 26 1219.2 15.9 API5L X65 70 1100 Rural 27 1219.2 15.1 API5L X80 70 1100 Rural 28 1219.2 14.27 API5L X65 70 1100 Rural 29 1219.2 12.7 API5L X60 48.2 1100 Rural 31 1219.2 17.48 API5L X65 36.5 1100 Suburban 32 1066.8 14.27 API5L X60 80 1100 Rural 33 1066.8 14.27 API5L X60 75 1100 Rural 34 1066.8 14.27 API5L X60 70 1100 Rural 35 1066.8 14.27 API5L X65 70 1100 Rural 37 1066.8 19.05 API5L X65 38 1100 Suburban 38 1066.8 14.27 API5L X60 32 1100 Suburban 39 1066.8 12.7 API5L X56 26.2 1100 Suburban 40 914.4 12.7 API5L X60 85 1100 Rural 41 914.4 15.88 API5L X60 75 900 Rural 42 914.4 12.7 API5L X60 75 1100 Rural 43 914.4 12.7 API5L X60 75 1000 Rural 44 914.4 12.7 API5L X60 75 900 Rural 45 914.4 12.7 API5L X65 75 1100 Rural 47 914.4 15.88 API5L X56 70 900 Rural 48 914.4 15.88 API5L X60 70 1100 Rural 49 914.4 15.88 API5L X60 70 1000 Rural 50 914.4 15.88 API5L X60 70 900 Rural 51 914.4 14.27 API5L X60 70 1100 Rural 52 914.4 12.7 API5L X60 70 1100 Rural 53 914.4 12.7 API5L X60 70 1100 Rural 54 914.4 12.7 API5L X60 70 900 Rural 55 914.4 12.7 API5L X60 70 900 Rural 56 914.4 12.7 API5L X60 70 900 Rural 57 914.4 12.7 API5L X60 70 900 Rural 58 914.4 15.88 API5L X65 55 1100 Rural 59 914.4 12.7 API5L X60 44.8 1100 Rural 61 914.4 12.7 API5L X60 38 1100 Rural

1 These are the pipeline identifiers as provided by HSE.

21



Material code

Material grade

Pressure (barg)


62 914.4 12.7 API5L X60 38 1000 Rural 63 914.4 12.7 API5L X60 32 1100 Suburban 64 914.4 8.74 API5L X52 27.5 1100 Rural 65 914.4 12.7 API5L X60 26.2 1100 Suburban 66 762 12.7 API5L X60 75 1100 Rural 67 762 12.7 API5L X60 75 1000 Rural 68 762 11.91 API5L X52 75 1100 Rural 69 762 11.91 API5L X65 75 1100 Rural 70 762 15.88 API5L X52 70 1100 Rural 71 762 12.7 API5L X60 70 1100 Rural 72 762 12.7 API5L X60 70 900 Rural 73 762 12.7 API5L X60 70 900 Rural 74 762 11.91 API5L X52 70 1100 Rural 75 762 11.91 API5L X52 70 1000 Rural 76 762 11.91 API5L X52 70 900 Rural 77 762 11.91 API5L X60 70 1100 Rural 78 762 15.88 API5L X52 42.7 1100 Suburban 79 762 15.88 API5L X52 39.2 1100 Suburban 80 762 12.7 API5L X52 39.2 1100 Rural 81 762 11.91 API5L X52 39.2 1100 Rural 82 762 15.88 API5L X52 38 1100 Suburban 83 762 15.88 API5L X52 38 1000 Suburban 84 762 15.88 API5L X52 38 900 Suburban 85 762 12.7 API5L X60 38 1100 Suburban 86 762 12.7 API5L X60 38 1000 Suburban 87 762 12.7 API5L X60 38 1000 Suburban 88 762 11.91 API5L X52 38 1100 Rural 89 762 11.91 API5L X52 38 1000 Rural 90 762 14.27 API5L X52 37.2 1100 Suburban 91 762 15.88 API5L X60 33.1 1100 Suburban 92 762 12.7 API5L X56 33.1 1100 Suburban 93 762 12.7 API5L X56 33.1 900 Suburban 94 762 12.7 API5L X60 33.1 1100 Suburban 95 762 12.7 API5L X60 32 1100 Suburban 96 762 11.91 API5L X52 32 1100 Suburban 97 762 10.7 API5L B 19 1100 Suburban 98 762 9.52 API5L X52 19 1100 Suburban 99 762 11.91 API5L X52 12.7 1100 Suburban 100 609.6 11.91 API5L X52 75 900 Rural

22



Material code

Material grade

Pressure (barg)


101 609.6 9.52 API5L X52 75 1100 Rural 103 609.6 14.27 API5L X52 70 1100 Rural 104 609.6 11.91 API5L X52 70 900 Rural 105 609.6 11.91 API5L X52 70 1100 Rural 106 609.6 11.91 API5L X52 70 1000 Rural 107 609.6 11.91 API5L X52 70 900 Rural 108 609.6 9.52 API5L X52 70 1100 Rural 109 609.6 9.52 API5L X52 70 900 Rural 110 609.6 9.52 API5L X52 69 1100 Rural 111 609.6 9.52 API5L X52 68.9 1100 Rural 112 609.6 9.52 API5L X52 50 1100 Rural 113 609.6 14.27 API5L X52 48.3 1100 Suburban 114 609.6 14.27 API5L X52 48.3 1000 Suburban 115 609.6 12.7 API5L X52 48.3 1000 Rural 116 609.6 9.52 API5L X52 48.3 1100 Rural 117 609.6 11.91 API5L X60 46 1100 Suburban 118 609.6 12.7 API5L X52 42 1100 Suburban 119 609.6 11.91 API5L X46 42 1100 Rural 120 609.6 11.91 API5L X52 42 1100 Rural 121 609.6 11.91 API5L X52 42 1000 Rural 122 609.6 11.91 API5L X52 42 1000 Rural 123 609.6 9.52 API5L X52 42 1100 Rural 124 609.6 11.91 API5L X52 40 1100 Suburban 126 609.6 9.52 API5L X52 39.9 1100 Rural 127 609.6 11.91 API5L X52 39.5 1000 Suburban 128 609.6 11.91 API5L X52 38.6 1100 Suburban 129 609.6 15.88 API5L X52 38 1000 Suburban 130 609.6 15.88 API5L X52 38 830 Suburban 131 609.6 14.27 API5L X52 38 1000 Suburban 132 609.6 14.27 API5L X52 38 830 Suburban 133 609.6 12.7 API5L X52 38 1000 Suburban 134 609.6 12.7 API5L X52 38 900 Suburban 135 609.6 11.91 API5L X52 38 1100 Suburban 136 609.6 11.91 API5L X52 38 1000 Suburban 137 609.6 11.91 API5L X52 38 830 Suburban 138 609.6 9.52 API5L X52 38 1000 Rural 139 609.6 15.88 API5L X46 37.2 1100 Suburban 140 609.6 12.7 API5L X46 37.2 1100 Suburban 141 609.6 12.7 API5L X46 37.2 910 Suburban

23



Material code

Material grade

Pressure (barg)


142 609.6 12.7 API5L X46 37.2 900 Suburban 143 609.6 12.7 API5L X46 37.2 800 Suburban 144 609.6 12.7 API5L X46 37.2 600 Suburban 145 609.6 11.91 API5L X52 37.2 1100 Suburban 146 609.6 9.52 API5L X52 37.2 1100 Rural 147 609.6 9.52 API5L X52 37.2 900 Rural 148 609.6 12.7 API5L X46 37 1100 Suburban 149 609.6 11.91 API5L X52 37 1100 Suburban 150 609.6 11.91 API5L X52 37 1000 Suburban 151 609.6 12.7 API5L X52 34.5 910 Suburban 152 609.6 11.91 API5L X52 34.5 910 Suburban 153 609.6 9.52 API5L X52 34.5 910 Rural 154 609.6 9.52 API5L X52 33.8 1100 Rural 155 609.6 12.7 API5L X52 33.1 1100 Suburban 156 609.6 12.7 API5L X52 33.1 900 Suburban 158 609.6 9.52 API5L X52 33.1 1100 Suburban 159 609.6 7.92 API5L X42 33.1 900 Rural 160 609.6 11.1 API5L X46 32.6 900 Suburban 161 609.6 11.91 API5L X52 32.4 1100 Suburban 163 609.6 9.52 API5L X52 32 1100 Suburban 164 609.6 11.91 API5L X52 27.6 1100 Suburban 165 609.6 14.27 API5L X60 26.2 1100 Suburban 167 609.6 9.52 API5L X46 26.2 1100 Suburban 168 609.6 9.52 API5L X46 26.2 910 Suburban 169 609.6 9.52 API5L X52 26.2 1100 Suburban 170 609.6 9.52 API5L X46 24 910 Suburban 171 609.6 9.52 API5L X52 24 1100 Suburban 172 609.6 17.48 API5L X60 19 1100 Suburban 173 609.6 15.88 API5L X52 19 1100 Suburban 174 609.6 14.27 API5L X52 19 1100 Suburban 176 609.6 9.52 API5L X52 19 1000 Suburban 178 609.6 17.48 API5L X52 13.9 830 Suburban 179 609.6 12.7 API5L X52 13.9 1000 Suburban 180 609.6 12.7 API5L X52 13.9 600 Suburban 181 609.6 11.91 API5L X52 13.9 1000 Suburban 182 609.6 11.91 API5L X52 13.9 830 Suburban 186 508 11.1 API5L X46 70 900 Rural 187 508 11.1 API5L X46 36.4 900 Suburban 188 508 11.1 API5L X46 35.9 900 Suburban

24



Material code

Material grade

Pressure (barg)


189 508 9.52 API5L X46 33.8 900 Suburban 190 508 11.1 API5L X46 32.6 900 Suburban 191 508 9.52 API5L B 19 1100 Suburban 192 508 9.52 API5L X46 17.2 900 Suburban 193 457.2 11.91 API5L X52 85 1100 Rural 195 457.2 15.88 API5L X52 70 1100 Suburban 196 457.2 11.91 API5L X52 70 1100 Rural 197 457.2 11.91 API5L X52 70 1000 Rural 198 457.2 11.91 API5L X52 70 900 Rural 199 457.2 10.31 API5L X52 70 1100 Rural 200 457.2 9.52 API5L X52 70 1100 Rural 201 457.2 9.52 API5L X52 70 1000 Rural 202 457.2 9.52 API5L X52 70 900 Rural 203 457.2 11.91 API5L X52 68.95 1000 Rural 204 457.2 11.91 API5L X52 68.95 900 Rural 205 457.2 9.52 API5L X52 68.95 1100 Rural 206 457.2 9.52 API5L X52 68.95 1000 Rural 207 457.2 9.52 API5L X52 68.95 900 Rural 208 457.2 9.52 API5L X52 68.9 1100 Rural 209 457.2 9.52 API5L X52 49.6 1100 Rural 210 457.2 10.31 API5L X52 45.5 1000 Suburban 211 457.2 10.31 API5L X46 42 1100 Suburban 212 457.2 10.31 API5L X46 42 1000 Suburban 213 457.2 9.52 API5L X52 42 1100 Suburban 214 457.2 8.74 API5L X42 42 1100 Rural 215 457.2 10.31 API5L X52 41.4 1100 Suburban 216 457.2 11.91 API5L X52 39.3 900 Suburban 217 457.2 9.52 API5L X52 39.3 1100 Suburban 218 457.2 9.52 API5L X52 39.3 900 Suburban 220 457.2 11.91 API5L X52 38 1100 Suburban 221 457.2 11.91 API5L X52 38 900 Suburban 222 457.2 10.31 API5L X46 38 1000 Suburban 223 457.2 10.31 API5L X52 38 1000 Suburban 224 457.2 9.52 API5L X52 38 1000 Suburban 225 457.2 9.52 API5L X52 38 900 Suburban 226 457.2 10.31 API5L X46 37.2 900 Suburban 227 457.2 10.31 API5L X46 37.2 1100 Suburban 228 457.2 9.52 API5L X46 37.2 1100 Suburban 229 457.2 11.91 API5L X52 37 1000 Suburban

25



Material code

Material grade

Pressure (barg)


230 457.2 9.52 API5L X52 36 1100 Suburban 231 457.2 9.52 API5L X52 36 1000 Suburban 232 457.2 10.31 API5L X46 34.5 910 Suburban 233 457.2 9.52 API5L X52 33.8 1100 Suburban 236 457.2 10.31 API5L X46 33.1 1100 Suburban 237 457.2 10.31 API5L X46 33.1 900 Suburban 238 457.2 9.52 API5L X46 33.1 900 Suburban 239 457.2 9.52 API5L X52 33.1 1100 Suburban 240 457.2 9.52 API5L X52 33.1 900 Suburban 241 457.2 9.52 API5L X52 33.1 900 Suburban 242 457.2 7.2 API5L X46 33.1 1100 Rural 244 457.2 9.52 API5L X46 32.6 900 Suburban 246 457.2 10.31 API5L X46 32 1100 Suburban 247 457.2 9.52 API5L X46 32 1100 Suburban 248 457.2 9.52 API5L X52 32 1100 Suburban 249 457.2 9.52 API5L X52 32 1000 Suburban 250 457.2 7.14 API5L X52 32 1100 Suburban 251 457.2 6.35 API5L X52 28 1100 Suburban 252 457.2 9.52 API5L X52 27.6 1100 Suburban 253 457.2 9.52 API5L X52 27.5 1100 Suburban 254 457.2 8.74 API5L X42 27 1100 Suburban 255 457.2 9.52 API5L X52 26.2 1100 Suburban 256 457.2 7.92 API5L X46 26.2 1100 Suburban 258 457.2 8.9 API5L B 24.1 1100 Suburban 259 457.2 7.92 API5L B 24.1 1100 Suburban 260 457.2 9.52 API5L X52 24 1100 Suburban 261 457.2 9.52 API5L X52 24 1000 Suburban 262 457.2 7.92 API5L X42 24 1100 Suburban 263 457.2 9.52 API5L X52 22 1100 Suburban 264 457.2 9.52 API5L X52 20.7 1100 Suburban 265 457.2 9.52 API5L B 19 1000 Suburban 267 457.2 6.35 API5L B 19 1100 Suburban 268 457.2 6.35 API5L B 19 900 Suburban 269 457.2 11.91 API5L X52 18.96 900 Suburban 270 457.2 9.52 API5L X52 18.96 1100 Suburban 274 457.2 10.31 API5L X52 17 1000 Suburban 276 457.2 11.91 API5L X52 15 1100 Suburban 278 457.2 11.91 API5L X52 13.9 860 Suburban 281 406.4 15.88 API5L X52 59 1100 Suburban

26



Material code

Material grade

Pressure (barg)


282 406.4 10.31 API5L X42 38 1100 Suburban 283 406.4 10.31 API5L X46 37.9 900 Suburban 284 406.4 7.92 API5L X46 32.4 1100 Suburban 286 406.4 9.52 API5L B 32 1100 Suburban 287 406.4 9.52 API5L X46 32 1100 Suburban 288 406.4 9.52 API5L X52 32 1100 Suburban 289 406.4 9.52 API5L X56 32 1000 Suburban 290 406.4 7.92 API5L X42 32 1100 Suburban 291 406.4 8.74 API5L B 27 1100 Suburban 292 406.4 12.7 API5L X52 26.2 1100 Suburban 293 406.4 7.92 API5L X42 26.2 1100 Suburban 294 406.4 7.92 API5L X42 26.2 910 Suburban 296 406.4 6.35 API5L X42 26.2 1100 Suburban 297 406.4 9.52 API5L X42 24 1100 Suburban 298 406.4 9.52 API5L X46 24 1100 Suburban 299 406.4 8.18 API5L B 24 1100 Suburban 300 406.4 10.31 API5L X52 19 1100 Suburban 304 406.4 7.92 API5L X42 19 900 Suburban 305 406.4 7.14 API5L X42 19 1100 Suburban 308 355.6 7.92 API5L X46 70 900 Rural 309 355.6 7.92 API5L X46 37 1100 Suburban 310 355.6 7.92 API5L X46 33.1 900 Suburban 311 355.6 7.92 API5L X46 32.6 900 Suburban 312 355.6 8.18 API5L X46 32 1100 Suburban 314 355.6 6.35 API5L B 24.1 1000 Suburban 316 355.6 6.35 API5L X46 10.3 900 Suburban 317 323.8 7.14 API5L X46 75 1100 Rural 318 323.8 7.92 API5L X52 70 1100 Rural 319 323.8 7.14 API5L X42 70 1100 Rural 320 323.8 7.14 API5L X46 70 1100 Rural 321 323.8 7.14 API5L X46 70 1000 Rural 323 323.8 7.14 API5L X46 68.95 1100 Rural 324 323.8 7.14 API5L X46 68.95 900 Rural 325 323.8 7.14 API5L X46 68.9 1100 Rural 326 323.8 7.14 API5L X52 68.9 900 Rural 327 323.8 7.14 API5L X46 48.3 1100 Rural 328 323.8 7.92 API5L X52 46.2 900 Suburban 330 323.8 7.92 API5L X52 43.75 900 Suburban 331 323.8 7.14 API5L X46 43.75 900 Rural

27



Material code

Material grade

Pressure (barg)


332 323.8 7.14 API5L X46 42 1100 Rural 333 323.8 8.18 API5L X52 41.4 1100 Suburban 334 323.8 7.14 API5L X52 41.4 1100 Suburban 335 323.8 12.7 API5L X52 40 1100 Suburban 336 323.8 8.4 API5L X46 40 1100 Suburban 337 323.8 7.14 API5L X46 40 1100 Suburban 338 323.8 9.52 API5L X52 39.3 900 Suburban 339 323.8 7.14 API5L X46 39.3 1100 Suburban 340 323.8 7.14 API5L X52 39.3 900 Suburban 341 323.8 12.7 API5L X52 38.6 700 Suburban 342 323.8 8.74 API5L X46 38.6 1100 Suburban 343 323.8 7.14 API5L X46 38.6 1100 Suburban 344 323.8 12.7 API5L X52 38 1100 Suburban 347 323.8 7.92 API5L X52 38 1000 Suburban 348 323.8 7.14 API5L X46 38 1100 Suburban 349 323.8 7.14 API5L X46 38 1000 Suburban 350 323.8 7.14 API5L X52 38 1000 Suburban 351 323.8 7.14 API5L X52 38 900 Suburban 352 323.8 7.14 API5L X52 38 860 Suburban 354 323.8 8.74 API5L X46 37.2 1100 Suburban 355 323.8 7.14 API5L X46 37.2 1100 Suburban 356 323.8 6.35 API5L X46 37.2 1100 Suburban 357 323.8 6.35 API5L X52 37.2 1100 Suburban 358 323.8 9.52 API5L X46 37 1100 Suburban 359 323.8 7.14 API5L X46 36.4 1100 Suburban 360 323.8 7.14 API5L X52 36.4 1100 Suburban 361 323.8 9.52 API5L X46 36 1100 Suburban 362 323.8 9.52 API5L X52 36 1100 Suburban 363 323.8 7.14 API5L X46 36 1100 Suburban 364 323.8 7.14 API5L X46 34.5 910 Suburban 365 323.8 8.74 API5L X46 33.1 900 Suburban 366 323.8 7.92 API5L X46 33.1 900 Suburban 367 323.8 7.92 API5L X52 33.1 1100 Suburban 368 323.8 7.92 API5L X52 33.1 900 Suburban 370 323.8 7.14 API5L X46 32.6 1100 Suburban 371 323.8 7.14 API5L X46 32.6 900 Suburban 372 323.8 6.35 API5L X46 32.6 900 Suburban 373 323.8 6.35 API5L X46 32.4 1100 Suburban 374 323.8 12.7 API5L X52 32 1100 Suburban

28



Material code

Material grade

Pressure (barg)


375 323.8 9.52 API5L X46 32 1100 Suburban 376 323.8 8.4 API5L X42 32 1100 Suburban 377 323.8 8.4 API5L X46 32 1100 Suburban 379 323.8 8.18 API5L X42 32 1100 Suburban 380 323.8 8.18 API5L X46 32 1100 Suburban 382 323.8 7.92 API5L X52 32 1100 Suburban 383 323.8 7.14 API5L X46 32 1100 Suburban 384 323.8 5.56 API5L X52 32 1000 Suburban 385 323.8 7.14 API5L X52 31 1100 Suburban 386 323.8 7.14 API5L X46 27.6 1100 Suburban 387 323.8 7.14 API5L X46 27.5 1100 Suburban 388 323.8 5.56 API5L B 27.5 1100 Rural 389 323.8 7.14 API5L X52 27 1100 Suburban 390 323.8 7.14 API5L X46 26.4 1100 Suburban 391 323.8 12.7 API5L X52 26.2 1100 Suburban 392 323.8 7.14 API5L X46 26.2 1100 Suburban 393 323.8 6.35 API5L B 26.2 1100 Suburban 394 323.8 5.49 API5L X42 26.2 1100 Suburban 395 323.8 7.14 API5L X46 24.8 1100 Suburban 396 323.8 12.7 API5L X52 24.1 1100 Suburban 397 323.8 7.14 API5L X46 24.1 1100 Suburban 398 323.8 7.14 API5L X46 24.1 900 Suburban 399 323.8 6.35 API5L B 24.1 1100 Suburban 400 323.8 6.35 API5L B 24.1 1000 Suburban 401 323.8 6.35 API5L X46 24.1 900 Suburban 402 323.8 6.35 API5L X52 24.1 1000 Suburban 403 323.8 5.56 API5L B 24.1 1100 Suburban 404 323.8 9.52 API5L X46 24 1000 Suburban 405 323.8 8.4 API5L X52 24 1100 Suburban 406 323.8 7.14 API5L X46 24 1100 Suburban 407 323.8 6.35 API5L X42 24 910 Suburban 408 323.8 5.08 API5L B 24 1000 Rural 410 323.8 7.14 API5L X46 20.9 1100 Suburban 411 323.8 6.35 API5L B 20.7 900 Suburban 412 323.8 6.35 API5L B 20.3 1000 Suburban 416 323.8 9.52 API5L X52 19 1100 Suburban 417 323.8 8.74 API5L X46 19 1100 Suburban 418 323.8 8.4 API5L X42 19 1000 Suburban 419 323.8 8.4 API5L X46 19 1100 Suburban

29



Material code

Material grade

Pressure (barg)


420 323.8 7.92 API5L B 19 1100 Suburban 421 323.8 7.92 API5L X52 19 900 Suburban 422 323.8 7.92 API5L X52 19 1100 Suburban 423 323.8 7.14 API5L B 19 900 Suburban 424 323.8 7.14 API5L B 19 1100 Suburban 425 323.8 7.14 API5L X42 19 1100 Suburban 426 323.8 7.14 API5L X46 19 1000 Suburban 427 323.8 6.35 API5L B 19 900 Suburban 428 323.8 6.35 API5L B 19 1100 Suburban 429 323.8 6.35 API5L B 19 1100 Suburban 430 323.8 6.35 API5L B 19 1000 Suburban 431 323.8 6.35 API5L B 19 900 Suburban 432 323.8 6.35 API5L X46 19 1100 Suburban 433 323.8 6.35 API5L X52 19 1100 Suburban 434 323.8 5.2 API5L B 19 1100 Suburban 436 323.8 7.92 API5L X52 18.96 900 Suburban 437 323.8 6.35 API5L B 18.96 900 Suburban 440 323.8 7.92 API5L X46 17.2 900 Suburban 442 323.8 7.14 API5L X46 17 1100 Suburban 443 323.8 7.14 API5L X46 17 1000 Suburban 444 323.8 7.14 API5L X52 17 1100 Suburban 445 323.8 7.14 API5L X52 17 1000 Suburban 446 323.8 9.52 API5L X46 15.2 1100 Suburban 447 323.8 6.35 API5L X46 15.2 1100 Suburban 448 323.8 8.4 API5L B 15 1100 Suburban 450 323.8 6.35 API5L X42 15 1100 Suburban 453 323.8 6.35 API5L B 14 1000 Suburban 454 323.8 6.35 API5L B 14 750 Suburban 458 323.8 6.35 API5L B 13.7 1000 Suburban 459 323.8 6.35 API5L X46 12.4 1100 Suburban 463 323.8 6.35 API5L X46 9.3 1100 Suburban 464 323.8 6.35 API5L B 8.46 1100 Suburban 465 323.8 6.35 API5L X46 8.3 900 Suburban 466 273 6.35 API5L X46 75 1100 Rural 467 273 6.35 API5L X46 70 1100 Rural 468 273 6.35 API5L X52 70 900 Rural 469 273 6.35 API5L X46 68.95 1100 Rural 470 273 6.35 API5L X46 68.95 1000 Rural 471 273 12.7 API5L X46 67 1100 Suburban

30



Material code

Material grade

Pressure (barg)


472 273 6.35 API5L X52 43.75 1000 Suburban 473 273 12.7 API5L X46 42 1100 Suburban 474 273 12.7 API5L X46 42 1000 Suburban 475 273 6.35 API5L X46 39.3 1100 Suburban 476 273 6.35 API5L X52 39.3 1100 Suburban 477 273 7.92 API5L X46 38.6 900 Suburban 478 273 7.14 API5L X46 38 900 Suburban 479 273 6.35 API5L X46 38 1100 Suburban 480 273 6.35 API5L X46 38 1000 Suburban 481 273 7.92 API5L X46 37.2 1100 Suburban 482 273 6.35 API5L X46 37.2 1100 Suburban 483 273 6.35 API5L X46 36.5 1100 Suburban 485 273 6.35 API5L X46 32.6 900 Suburban 487 273 7.92 API5L X42 32 1100 Suburban 488 273 6.35 API5L B 24.1 1100 Suburban 489 273 7.14 API5L X52 24 1100 Suburban 490 273 7.14 API5L X52 24 1000 Suburban 491 273 6.35 API5L B 24 1000 Suburban 493 273 6.35 API5L B 19 1100 Suburban 494 273 6.35 API5L B 19 1000 Suburban 495 273 6.35 API5L X46 19 900 Suburban 496 273 6.35 API5L X46 19 1100 Suburban 497 273 7.8 API5L X52 18.96 900 Suburban 498 273 7.14 API5L B 18.96 900 Suburban 499 273 6.35 API5L B 18.96 900 Suburban 500 273 6.35 API5L B 18.96 600 Suburban 501 273 6.35 API5L X42 18.96 1100 Suburban 502 273 6.35 API5L X46 18.96 1100 Suburban 503 273 6.35 API5L X46 18.96 1100 Suburban 504 273 6.35 API5L X46 18.96 1000 Suburban 505 273 6.35 API5L X46 18.96 900 Suburban 506 273 6.35 API5L X52 18.96 900 Suburban 508 273 6.35 API5L X46 17.2 1100 Suburban 510 273 7.14 API5L X46 17 1000 Suburban 513 273 6.35 API5L X46 15 1100 Suburban 515 273 5.56 API5L B 13.7 1100 Suburban 517 219.1 6.35 API5L X42 75 1100 Rural 518 219.1 6.35 API5L X42 70 1100 Rural 519 219.1 6.35 API5L X46 70 1100 Rural

31



Material code

Material grade

Pressure (barg)


520 219.1 5.56 API5L X52 70 1100 Rural 522 219.1 8.18 API5L X42 68.95 1100 Rural 523 219.1 6.35 API5L X42 68.95 1100 Rural 524 219.1 6.35 API5L X46 68.95 900 Rural 525 219.1 12.7 API5L X42 67 1100 Suburban 526 219.1 6.35 API5L X42 67 1100 Rural 527 219.1 6.35 API5L X42 49.7 1100 Suburban 528 219.1 6.35 API5L X42 49.6 1100 Suburban 529 219.1 6.35 API5L X42 48.3 1100 Suburban 530 219.1 6.35 API5L X52 46.2 900 Suburban 531 219.1 6.35 API5L X46 43.75 900 Suburban 532 219.1 6.35 API5L X52 43.75 900 Suburban 533 219.1 6.35 API5L X42 42 1100 Suburban 534 219.1 6.35 API5L X42 41.4 1100 Suburban 535 219.1 6.35 API5L X42 39.3 1100 Suburban 536 219.1 6.35 API5L X42 39.3 900 Suburban 537 219.1 6.35 API5L X52 39.3 900 Suburban 538 219.1 6.35 API5L X42 38.6 1100 Suburban 541 219.1 7.14 API5L X42 38 900 Suburban 542 219.1 6.35 API5L X42 38 1100 Suburban 544 219.1 7.14 API5L X46 37.2 1100 Suburban 545 219.1 6.35 API5L X42 37.2 1100 Suburban 546 219.1 6.35 API5L X46 37.2 1100 Suburban 547 219.1 6.35 API5L X46 36.4 900 Suburban 548 219.1 7.14 API5L B 36 1100 Suburban 549 219.1 6.35 API5L X42 36 1100 Suburban 550 219.1 6.35 API5L X46 35.9 900 Suburban 551 219.1 8.18 API5L X42 34.5 1100 Suburban 552 219.1 6.35 API5L X46 34.4 1100 Suburban 553 219.1 6.3 API5L B 33.7 1100 Suburban 556 219.1 7.14 API5L X42 33.1 900 Suburban 557 219.1 6.35 API5L X42 33.1 900 Suburban 559 219.1 6.35 API5L X42 32.4 1100 Suburban 562 219.1 8.18 API5L X42 32 1100 Suburban 563 219.1 7.14 API5L X52 32 1100 Suburban 564 219.1 6.35 API5L X42 32 1100 Suburban 565 219.1 6.35 API5L X46 32 1100 Suburban 566 219.1 5.49 API5L X52 32 1100 Suburban 568 219.1 8.18 API5L X42 27.5 1100 Suburban

32



Material code

Material grade

Pressure (barg)


569 219.1 6.35 API5L X42 27.5 1100 Suburban 572 219.1 6.35 API5L B 26.2 910 Suburban 573 219.1 6.35 API5L X42 26.2 1100 Suburban 574 219.1 6.35 API5L X42 26 1100 Suburban 576 219.1 6.35 API5L X42 24.8 1100 Suburban 577 219.1 4.4 API5L B 24.8 1100 Suburban 579 219.1 6.35 API5L X42 24.1 1100 Suburban 580 219.1 6.35 API5L X42 24.1 600 Suburban 581 219.1 5.49 API5L X42 24.1 1100 Suburban 582 219.1 4.78 API5L B 24.1 1100 Suburban 583 219.1 4.78 API5L X52 24.1 1100 Suburban 584 219.1 4.78 API5L X52 24.1 900 Suburban 587 219.1 7.92 API5L X42 24 900 Suburban 588 219.1 7.14 API5L X42 24 1000 Suburban 589 219.1 7.14 API5L X52 24 1100 Suburban 590 219.1 6.35 API5L X42 24 1100 Suburban 591 219.1 6.35 API5L X42 24 910 Suburban 592 219.1 6.35 API5L X46 24 1000 Suburban 593 219.1 5.49 API5L X52 24 1100 Suburban 594 219.1 5.08 API5L B 24 1000 Suburban 596 219.1 6.35 API5L X42 22 1100 Suburban 597 219.1 4.78 API5L B 21.4 1100 Suburban 598 219.1 6.35 API5L X42 20.9 1100 Suburban 599 219.1 6.35 API5L X42 20 1100 Suburban 608 219.1 6.35 API5L B 19 900 Suburban 609 219.1 6.35 API5L B 19 1100 Suburban 610 219.1 6.35 API5L B 19 1000 Suburban 611 219.1 6.35 API5L B 19 900 Suburban 612 219.1 6.35 API5L X42 19 1100 Suburban 613 219.1 6.35 API5L X46 19 1100 Suburban 614 219.1 6.35 API5L X46 19 1000 Suburban 615 219.1 5.49 API5L B 19 1000 Suburban 616 219.1 5.49 API5L X42 19 1100 Suburban 618 219.1 4.78 API5L B 19 1100 Suburban 619 219.1 4.78 API5L B 19 1000 Suburban 621 219.1 6.35 API5L X46 18.96 900 Suburban 622 219.1 6.35 API5L X52 18.96 900 Suburban 623 219.1 4.78 API5L B 18.96 900 Suburban 625 219.1 6.35 API5L X42 17.2 1100 Suburban

33



Material code

Material grade

Pressure (barg)


626 219.1 6.35 API5L X46 17.2 1100 Suburban 627 219.1 4.78 API5L B 17.2 1100 Suburban 630 219.1 6.35 API5L X46 17 1000 Suburban 633 219.1 5.2 API5L X42 15 1100 Suburban 634 219.1 4.78 API5L B 15 1100 Suburban 636 219.1 6.35 API5L X42 14 1000 Suburban 638 219.1 6.35 API5L X42 13.8 1100 Suburban 640 219.1 6.35 API5L B 13.7 1100 Suburban 646 168.3 7.14 API5L X42 70 1100 Suburban 647 168.3 6.35 API5L X42 70 1100 Rural 648 168.3 5.56 API5L X42 70 1100 Rural 649 168.3 4.4 API5L X52 70 1100 Rural 650 168.3 7.14 API5L X42 68.95 1100 Suburban 651 168.3 7.14 API5L X42 68.95 900 Suburban 652 168.3 6.35 API5L X52 68.95 900 Suburban 653 168.3 5.56 API5L X42 68.95 1100 Rural 654 168.3 5.49 API5L X42 68.95 1100 Rural 655 168.3 7.14 API5L X46 68.9 1100 Suburban 656 168.3 5.56 API5L X42 68.9 1100 Rural 657 168.3 5.56 API5L X46 68.9 1100 Rural 658 168.3 4.78 API5L X46 68.9 1100 Rural 659 168.3 5.56 API5L X42 49.6 1100 Suburban 660 168.3 5.56 API5L X42 48.3 1100 Suburban 661 168.3 7.14 API5L X42 46.2 900 Suburban 662 168.3 5.56 API5L X46 46.2 900 Suburban 663 168.3 7.14 API5L X42 43.75 1100 Suburban 664 168.3 6.35 API5L X52 43.75 1000 Suburban 665 168.3 5.49 API5L X42 43.75 1100 Suburban 666 168.3 5.08 API5L B 42 1100 Suburban 667 168.3 5.56 API5L X42 41.4 1100 Suburban 669 168.3 7.14 API5L X46 39.3 700 Suburban 672 168.3 7.14 API5L X42 38 1100 Suburban 673 168.3 7.14 API5L X42 38 1000 Suburban 674 168.3 7.14 API5L X42 38 900 Suburban 675 168.3 6.35 API5L X52 38 1000 Suburban 676 168.3 5.56 API5L X42 38 1000 Suburban 677 168.3 5.56 API5L X52 38 900 Suburban 678 168.3 4.78 API5L X52 38 900 Suburban 680 168.3 7.14 API5L X42 37.2 1100 Suburban

34



Material code

Material grade

Pressure (barg)


682 168.3 5.56 API5L X42 37.2 1100 Suburban 683 168.3 5.56 API5L X42 37 1000 Suburban 684 168.3 6.35 API5L X46 36.4 900 Suburban 685 168.3 5.56 API5L X42 36 1100 Suburban 688 168.3 6.35 API5L X46 33.1 900 Suburban 696 168.3 6.35 API5L X46 32 1000 Suburban 697 168.3 6.35 API5L X52 32 1100 Suburban 698 168.3 5.56 API5L X42 32 1100 Suburban 700 168.3 4.55 API5L X52 32 1100 Suburban 701 168.3 7.14 API5L X42 27.6 1100 Suburban 704 168.3 5.2 API5L X52 27.59 900 Suburban 705 168.3 7.14 API5L X42 27.5 1100 Suburban 706 168.3 5.56 API5L X42 27.5 1100 Suburban 710 168.3 5.56 API5L X42 24.1 1100 Suburban 711 168.3 5.56 API5L X46 24.1 1100 Suburban 714 168.3 4.78 API5L B 24.1 1100 Suburban 715 168.3 4.55 API5L B 24.1 1100 Suburban 716 168.3 4.55 API5L B 24.1 1000 Suburban 717 168.3 4.4 API5L B 24.1 1100 Suburban 718 168.3 4.4 API5L B 24.1 800 Suburban 720 168.3 5.56 API5L X42 24 1100 Suburban 721 168.3 5.08 API5L B 24 1000 Suburban 730 168.3 6.35 API5L X42 19 1100 Suburban 732 168.3 5.56 API5L B 19 1100 Suburban 733 168.3 5.56 API5L B 19 1000 Suburban 734 168.3 5.56 API5L B 19 900 Suburban 746 168.3 4.78 API5L B 18.96 900 Suburban 747 168.3 6.35 API5L B 17.2 1100 Suburban 778 168.3 6.35 API5L X46 9.3 1100 Suburban 779 114.3 6.35 API5L B 70 1100 Suburban 780 114.3 4.78 API5L B 70 1100 Rural 782 114.3 6.02 API5L B 68.95 1100 Suburban 783 114.3 4.78 API5L B 68.95 1100 Rural 784 114.3 4.78 API5L B 49.6 1100 Suburban 785 114.3 6.02 API5L B 43.75 900 Suburban 802 114.3 7.14 API5L B 32 1100 Suburban 834 114.3 6.02 API5L X42 19 900 Suburban 835 114.3 6.02 API5L X42 19 1100 Suburban 836 114.3 6.02 API5L X42 19 1000 Suburban

35



Material code

Material grade

Pressure (barg)


857 114.3 6.02 API5L X46 17 1000 Suburban 875 88.9 5.49 API5L B 34.48 600 Suburban

36

5.2 APPENDIX B – THE CHARPY V-NOTCH IMPACT ENERGY VALUES

69. Table 14 illustrates the Charpy V-notch impact energy values by material type for use in the Charpy energy-fracture toughness correlation from BS7910 [6]. This correlation is detailed in section 2.4.

Table 14 Charpy V-notch impact energy values

Material Code Material Grade Charpy V-notch energy (J) API5L B 22 API5L X42 24 API5L X461 27 API5L X52 30 API5L X561 33 API5L X60 35 API5L X65 38 API5L X70 40 API5L X80 45 ENISO L245 22 ENISO L290 24 ENISO L360 30 ENISO L415 35 ENISO L450 38 ENISO L485 40 ENISO L555 45

1 These values have had to be estimated as they are not published within the relevant standards.

37

5.3 APPENDIX C – THE BRITISH GAS TEST DATA USED TO DERIVE THE MICRO-CRACK CORRELATION

70. Table 15 details the British Gas data as originally reported. As can be seen, the data is reported in Imperial measurements and the pressure has to be inferred from the failure stress. Table 16 shows the values converted to the units required by PIPIN and also shows the actual variables required by the cut-down version of PIPIN that was used to generate the micro-crack correlation.

38

Table 15 British Gas data

Case number

Diameter (in)

Dent depth (in)

Dent depth/diameter

Defect depth (in)

Defect depth/wall thickness

Pipe grade

2/3 Cv (ft lbf-1)

Failure stress (lbf in-2)

1 30 0.917 91.70% 0.109 22.0% X60 15.5 14,100 2 30 1.407 70.35% 0.109 22.0% X60 15.5 5,500 3 30 0.355 11.83% 0.109 22.0% X60 15.5 26,100 4 30 0.525 13.13% 0.109 22.0% X60 15.5 18,500 5 30 1.093 21.86% 0.135 27.0% X60 17 10,200 6 30 1.805 30.08% 0.135 27.0% X60 17 4,700 7 30 0.975 13.93% 0.135 27.0% X60 17 11,500 8 30 0.62 7.75% 0.135 27.0% X60 17 23,400 9 30 2.093 23.26% 0.135 27.0% X60 17 4,250 10 30 0.375 3.75% 0.135 27.0% X60 17 22,200 11 30 0.505 4.59% 0.116 23.0% X60 15 17,000 12 30 0.538 4.48% 0.116 23.0% X60 15 14,700 13 30 0.488 3.75% 0.116 23.0% X60 15 18,300 14 30 0.444 3.17% 0.116 23.0% X60 15 18,800 15 30 0.561 3.74% 0.116 23.0% X60 15 11,400 16 30 0.383 2.39% 0.175 35.0% X60 40 34,200 17 30 0.488 2.87% 0.175 35.0% X60 40 31,000 18 30 0.57 3.17% 0.175 35.0% X60 40 27,900 19 30 0.42 2.21% 0.175 35.0% X60 40 35,600 20 30 0.481 2.41% 0.175 35.0% X60 40 32,200 21 30 0.73 3.48% 0.026 5.0% X60 23 86,950 22 30 0.61 2.77% 0.04 8.0% X60 23 67,200 23 30 0.715 3.11% 0.045 9.0% X60 23 61,200 24 30 0.75 3.13% 0.041 8.0% X60 23 59,800 25 30 0.725 2.90% 0.045 9.0% X60 23 58,100 26 30 0.725 2.79% 0.052 10.4% X60 23 56,700 27 30 0.755 2.80% 0.049 9.8% X60 23 56,400 28 30 0.812 2.90% 0.1 20.0% X60 23 20,200 29 30 2.07 7.14% 0.045 9.0% X60 23 15,100 30 30 2.09 6.97% 0.02 4.0% X60 23 28,500 31 30 2.09 6.74% 0.094 18.8% X60 23 10,600 32 30 2.285 7.14% 0.01 2.0% X60 23 39,800 33 30 2.3 6.97% 0.011 2.2% X60 23 42,350 34 30 2.645 7.78% 0.007 1.4% X60 23 36,600 35 30 0.44 1.26% 0.117 24.0% X52 47 57,400 36 30 0.61 1.69% 0.117 26.0% X52 47 55,600 37 30 0.72 1.95% 0.117 26.0% X52 47 53,300

39

Case number

Diameter (in)

Dent depth (in)

Dent depth/diameter

Defect depth (in)


Pipe grade

2/3 Cv (ft lbf-1)


38 30 0.77 2.03% 0.117 26.0% X52 47 52,700 39 30 0.98 2.51% 0.117 24.0% X52 47 44,000 40 30 0.5 1.25% 0.117 22.0% X52 52 54,100 41 30 0.81 1.98% 0.117 24.0% X52 52 45,400 42 30 0.66 1.57% 0.117 25.0% X52 52 43,700 43 30 0.72 1.67% 0.117 24.0% X52 52 42,700 44 30 0.78 1.77% 0.117 23.0% X52 52 41,600 45 30 0.93 2.07% 0.117 25.0% X52 52 40,500 46 30 1.06 2.30% 0.117 25.0% X52 52 38,300 47 30 0.46 0.98% 0.117 24.0% X52 23 42,800 48 30 0.61 1.27% 0.117 23.0% X52 23 39,000 49 30 0.43 0.88% 0.117 24.0% X52 23 38,400 50 30 0.58 1.16% 0.117 24.0% X52 23 37,800 51 30 0.74 1.45% 0.117 23.0% X52 23 33,100 52 30 0.85 1.63% 0.117 24.0% X52 23 34,000 53 30 1.22 2.30% 0.117 24.0% X52 23 28,900 54 30 0.99 1.83% 0.117 23.0% X52 23 27,200 55 30 1.19 2.16% 0.117 23.0% X52 23 26,700 56 30 1.34 2.39% 0.117 25.0% X52 23 21,700 57 30 1.61 2.82% 0.117 23.0% X52 23 18,400 58 30 0.404 0.70% 0.122 25.0% X52 47 64,500 59 30 0.388 0.66% 0.122 25.0% X52 47 66,350 60 30 0.398 0.66% 0.125 25.0% X52 47 65,800 61 30 0.758 1.24% 0.129 25.0% X52 52 53,400 62 30 0.783 1.26% 0.129 25.0% X52 52 50,000 63 30 0.827 1.31% 0.123 25.0% X52 47 47,300 64 30 0.894 1.40% 0.13 25.0% X52 52 44,800 65 30 1.09 1.68% 0.128 25.0% X52 52 34,300 66 30 1.197 1.81% 0.123 25.0% X52 23 20,900 67 30 0.57 0.85% 0.117 25.0% X52 47 56,800 68 30 0.64 0.94% 0.117 25.0% X52 47 51,500 69 30 0.97 1.41% 0.117 25.0% X52 52 45,000 70 30 0.41 0.59% 0.117 25.0% X52 23 40,100 71 30 0.78 1.10% 0.117 25.0% X52 23 20,900 72 30 0.3 0.42% 0.117 25.0% X52 26 55,200 73 30 0.67 0.92% 0.117 25.0% X52 26 35,900 74 30 0.52 0.70% 0.117 25.0% X52 26 32,000 75 30 0.74 0.99% 0.117 25.0% X52 26 30,200

40

Case number

Diameter (in)

Dent depth (in)

Dent depth/diameter

Defect depth (in)


Pipe grade

2/3 Cv (ft lbf-1)


76 30 1.08 1.42% 0.117 25.0% X52 26 22,300 77 12.75 0.25 0.3% 0.026 10.0% 11 59,200 78 12.75 0.28 0.4% 0.03 11.0% 11 56,100 79 12.75 0.23 0.3% 0.038 13.0% 11 58,000 80 12.75 0.62 0.8% 0.059 20.0% 11 14,300 81 12.75 0.63 0.8% 0.059 20.0% 11 11,800 82 12.75 0.69 0.8% 0.049 16.0% 11 16,700 83 12.75 0.98 1.2% 0.091 30.0% 11 6,400 84 12.75 1.02 1.2% 0.084 28.0% 11 7,000 85 12.75 0.93 1.1% 0.089 33.0% 11 9,000 86 18 0.17 0.2% 0.11 34.0% 20 35,900 87 18 0.075 0.1% 0.11 35.0% 20 39,000 88 18 0.085 0.1% 0.11 34.0% 20 44,400 89 18 0.37 0.4% 0.051 16.0% 20 54,500 90 18 0.36 0.4% 0.047 15.0% 20 53,700 91 18 0.37 0.4% 0.047 15.0% 20 53,400 92 18 1.08 1.2% 0.035 11.0% 20 45,000 93 18 1.08 1.2% 0.03 10.0% 20 51,500 94 18 1.08 1.1% 0.028 9.0% 20 53,900 95 24 1.68 1.8% 0.072 15.0% X46 15 6,400 96 24 1.75 1.8% 0.062 13.0% X46 15 7,700 97 24 1.58 1.6% 0.068 14.0% X46 15 9,500 98 24 1.58 1.6% 0.074 16.0% X46 15 15,900 99 24 1.78 1.8% 0.103 21.0% X46 15 8,300 100 24 1.1 1.1% 0.051 11.0% X46 15 26,400 101 24 1.13 1.1% 0.06 13.0% X46 15 13,000 102 24 1.1 1.1% 0.052 11.0% X46 15 16,000 103 24 1.2 1.2% 0.046 10.0% X46 15 32,800 104 24 1.22 1.2% 0.049 10.0% X46 15 33,000 105 24 0.6 0.6% 0.133 28.0% X46 15 26,600 106 24 0.67 0.6% 0.176 37.0% X46 15 17,800 107 24 0.62 0.6% 0.135 28.0% X46 15 15,900 108 24 0.55 0.5% 0.125 26.0% X46 15 28,700 109 30 1.11 1.0% 0.084 18.0% X52 20 17,000 110 30 1.05 1.0% 0.089 19.0% X52 20 16,500 111 30 1.02 0.9% 0.098 21.0% X52 20 23,500 112 30 0.93 0.8% 0.098 21.0% X52 20 24,700 113 42 2.01 1.8% 0.12 21.0% X60 34 23,500

41

Case number

Diameter (in)

Dent depth (in)

Dent depth/diameter

Defect depth (in)


Pipe grade

2/3 Cv (ft lbf-1)


114 42 1.87 1.6% 0.13 22.0% X60 34 24,300 115 42 1.96 1.7% 0.116 20.0% X60 34 24,700 116 42 3.02 2.6% 0.065 11.0% X60 34 19,400 117 42 3.06 2.6% 0.063 11.0% X60 34 24,700 118 30 0.43 0.4% 0.14 28.0% X60 12 16,200 119 30 0.66 0.6% 0.14 28.0% X60 12 19,300 120 30 0 0.0% 0.14 28.0% X60 12 54,400 121 30 0.46 0.4% 0.24 48.0% X60 15 20,000 122 30 0.976 0.8% 0.138 28.0% X60 15 11,700 123 30 1.054 0.9% 0.103 21.0% X60 15 23,400 124 30 1.058 0.9% 0.095 19.0% X60 15 20,700

Table 16 Converted British Gas data for use in the cut-down version of PIPIN

Case number

Diameter (mm)

Wall thickness (mm)

Pressure (bar)

2/3 Charpy (J)

Yield stress (MPa)

Tensile stress (MPa)

Dent depth (mm)

Gouge depth (mm)

1 762 12.58 32.11076374 21.0151782 413 517 23.2918 2.76862 762 12.58 12.52547522 21.0151782 413 517 35.7378 2.76863 762 12.58 59.4390733 21.0151782 413 517 9.017 2.76864 762 12.58 42.13114391 21.0151782 413 517 13.335 2.76865 762 12.70 23.4421738 23.04890512 413 517 27.7622 3.429 6 762 12.70 10.80178597 23.04890512 413 517 45.847 3.429 7 762 12.70 26.42990183 23.04890512 413 517 24.765 3.429 8 762 12.70 53.7791046 23.04890512 413 517 15.748 3.429 9 762 12.70 9.767572417 23.04890512 413 517 53.1622 3.429 10 762 12.70 51.0212018 23.04890512 413 517 9.525 3.429 11 762 12.81 39.41003132 20.33726922 413 517 12.827 2.946412 762 12.81 34.0780859 20.33726922 413 517 13.6652 2.946413 762 12.81 42.42373959 20.33726922 413 517 12.3952 2.946414 762 12.81 43.58285816 20.33726922 413 517 11.2776 2.946415 762 12.81 26.42790335 20.33726922 413 517 14.2494 2.946416 762 12.70 78.6002298 54.23271793 413 517 9.7282 4.445 17 762 12.70 71.24582233 54.23271793 413 517 12.3952 4.445 18 762 12.70 64.1212401 54.23271793 413 517 14.478 4.445 19 762 12.70 81.81778307 54.23271793 413 517 10.668 4.445 20 762 12.70 74.00372513 54.23271793 413 517 12.2174 4.445 21 762 13.21 207.826362 31.18381281 413 517 18.542 0.660422 762 12.70 154.4425568 31.18381281 413 517 15.494 1.016 23 762 12.70 140.6530428 31.18381281 413 517 18.161 1.143

42

Case number

Diameter (mm)

Wall thickness (mm)

Pressure (bar)

2/3 Charpy (J)

Yield stress (MPa)


Dent depth (mm)

Gouge depth (mm)

24 762 13.02 140.8713768 31.18381281 413 517 19.05 1.041425 762 12.70 133.5284606 31.18381281 413 517 18.415 1.143 26 762 12.70 130.3109073 31.18381281 413 517 18.415 1.320827 762 12.70 129.6214316 31.18381281 413 517 19.177 1.244628 762 12.70 46.42469713 31.18381281 413 517 20.6248 2.54 29 762 12.70 34.70361023 31.18381281 413 517 52.578 1.143 30 762 12.70 65.5001915 31.18381281 413 517 53.086 0.508 31 762 12.70 24.36147473 31.18381281 413 517 53.086 2.387632 762 12.70 91.47044287 31.18381281 413 517 58.039 0.254 33 762 12.70 97.33098632 31.18381281 413 517 58.42 0.279434 762 12.70 84.1160354 31.18381281 413 517 67.183 0.177835 762 12.38 128.6216918 63.72344357 358 455 11.176 2.971836 762 11.43 115.0045468 63.72344357 358 455 15.494 2.971837 762 11.43 110.2471644 63.72344357 358 455 18.288 2.971838 762 11.43 109.0061082 63.72344357 358 455 19.558 2.971839 762 12.38 98.5950251 63.72344357 358 455 24.892 2.971840 762 13.51 132.2477072 70.50253331 358 455 12.7 2.971841 762 12.38 101.7321395 70.50253331 358 455 20.574 2.971842 762 11.89 94.00587484 70.50253331 358 455 16.764 2.971843 762 12.38 95.68199027 70.50253331 358 455 18.288 2.971844 762 12.92 97.27003267 70.50253331 358 455 19.812 2.971845 762 11.89 87.12214945 70.50253331 358 455 23.622 2.971846 762 11.89 82.38958825 70.50253331 358 455 26.924 2.971847 762 12.38 95.90606987 31.18381281 358 455 11.684 2.971848 762 12.92 91.19065563 31.18381281 358 455 15.494 2.971849 762 12.38 86.04656736 31.18381281 358 455 10.922 2.971850 762 12.38 84.70208975 31.18381281 358 455 14.732 2.971851 762 12.92 77.39514619 31.18381281 358 455 18.796 2.971852 762 12.38 76.18706485 31.18381281 358 455 21.59 2.971853 762 12.38 64.75900512 31.18381281 358 455 30.988 2.971854 762 12.92 63.59963674 31.18381281 358 455 25.146 2.971855 762 12.92 62.43052577 31.18381281 358 455 30.226 2.971856 762 11.89 46.68026279 31.18381281 358 455 34.036 2.971857 762 12.92 43.02328368 31.18381281 358 455 40.894 2.971858 762 12.40 144.6795809 63.72344357 358 455 10.2616 3.098859 762 12.40 148.8293053 63.72344357 358 455 9.8552 3.098860 762 12.70 151.2250035 63.72344357 358 455 10.1092 3.175 61 762 13.11 126.6539282 70.50253331 358 455 19.2532 3.276662 762 13.11 118.5898204 70.50253331 358 455 19.8882 3.2766

43

Case number

Diameter (mm)

Wall thickness (mm)

Pressure (bar)

2/3 Charpy (J)

Yield stress (MPa)


Dent depth (mm)

Gouge depth (mm)

63 762 12.50 106.968018 63.72344357 358 455 21.0058 3.124264 762 13.21 107.0801727 70.50253331 358 455 22.7076 3.302 65 762 13.00 80.72197635 70.50253331 358 455 27.686 3.251266 762 12.50 47.26493819 31.18381281 358 455 30.4038 3.124267 762 11.89 122.1861257 63.72344357 358 455 14.478 2.971868 762 11.89 110.7849555 63.72344357 358 455 16.256 2.971869 762 11.89 96.80238828 70.50253331 358 455 24.638 2.971870 762 11.89 86.26168378 31.18381281 358 455 10.414 2.971871 762 11.89 44.95933145 31.18381281 358 455 19.812 2.971872 762 11.89 118.744263 35.25126666 358 455 7.62 2.971873 762 11.89 77.22679421 35.25126666 358 455 17.018 2.971874 762 11.89 68.83725389 35.25126666 358 455 13.208 2.971875 762 11.89 64.96515836 35.25126666 358 455 18.796 2.971876 762 11.89 47.9709613 35.25126666 358 455 27.432 2.971877 323.85 6.60 166.4691761 14.91399743 393 0 6.35 0.660478 323.85 6.93 165.474168 14.91399743 393 0 7.112 0.762 79 323.85 7.42 183.3610187 14.91399743 393 0 5.842 0.965280 323.85 7.49 45.62436456 14.91399743 393 0 15.748 1.498681 323.85 7.49 37.64807705 14.91399743 393 0 16.002 1.498682 323.85 7.78 55.31352601 14.91399743 393 0 17.526 1.244683 323.85 7.70 20.9961123 14.91399743 393 0 24.892 2.311484 323.85 7.62 22.71214071 14.91399743 393 0 25.908 2.133685 323.85 6.85 26.2516951 14.91399743 393 0 23.622 2.260686 457.2 8.22 88.97841632 27.11635897 348 0 4.318 2.794 87 457.2 7.98 93.9000239 27.11635897 348 0 1.905 2.794 88 457.2 8.22 110.0457294 27.11635897 348 0 2.159 2.794 89 457.2 8.10 133.0831742 27.11635897 348 0 9.398 1.295490 457.2 7.96 128.9013125 27.11635897 348 0 9.144 1.193891 457.2 7.96 128.1811935 27.11635897 348 0 9.398 1.193892 457.2 8.08 109.6893159 27.11635897 348 0 27.432 0.889 93 457.2 7.62 118.3599952 27.11635897 348 0 27.432 0.762 94 457.2 7.90 128.4637934 27.11635897 348 0 27.432 0.711295 609.6 12.19 17.65057792 20.33726922 317 434 42.672 1.828896 609.6 12.11 21.09972431 20.33726922 317 434 44.45 1.574897 609.6 12.34 26.51198227 20.33726922 317 434 40.132 1.727298 609.6 11.75 42.25193274 20.33726922 317 434 40.132 1.879699 609.6 12.46 23.39018952 20.33726922 317 434 45.212 2.6162100 609.6 11.78 70.3265214 20.33726922 317 434 27.94 1.2954101 609.6 11.72 34.473785 20.33726922 317 434 28.702 1.524

44

Case number

Diameter (mm)

Wall thickness (mm)

Pressure (bar)

2/3 Charpy (J)

Yield stress (MPa)


Dent depth (mm)

Gouge depth (mm)

102 609.6 12.01 43.4578623 20.33726922 317 434 27.94 1.3208103 609.6 11.68 86.69007801 20.33726922 317 434 30.48 1.1684104 609.6 12.45 92.90685058 20.33726922 317 434 30.988 1.2446105 609.6 12.07 72.59604558 20.33726922 317 434 15.24 3.3782106 609.6 12.08 48.64841155 20.33726922 317 434 17.018 4.4704107 609.6 12.24642857 44.04641637 20.33726922 317 434 15.748 3.429 108 609.6 12.21153846 79.27865621 20.33726922 317 434 13.97 3.175 109 762 11.85333333 36.46560369 27.11635897 358 455 28.194 2.1336110 762 11.89789474 35.52614265 27.11635897 358 455 26.67 2.2606111 762 11.85333333 50.40833451 27.11635897 358 455 25.908 2.4892112 762 11.85333333 52.98237712 27.11635897 358 455 23.622 2.4892113 1066.8 14.51428571 44.08892231 46.09781024 413 517 51.054 3.048 114 1066.8 15.00909091 47.14402027 46.09781024 413 517 47.498 3.302 115 1066.8 14.732 47.03537561 46.09781024 413 517 49.784 2.9464116 1066.8 15.00909091 37.63761289 46.09781024 413 517 76.708 1.651 117 1066.8 14.54727273 46.44559034 46.09781024 413 517 77.724 1.6002118 762 12.7 37.2316878 16.26981538 414 560 10.922 3.556 119 762 12.7 44.35627003 16.26981538 414 560 16.764 3.556 120 762 12.7 125.0249269 16.26981538 414 560 0 3.556 121 762 12.7 45.96504667 20.33726922 430 578 11.684 6.096 122 762 12.51857143 26.50541584 20.33726922 430 578 24.7904 3.5052123 762 12.45809524 52.7547407 20.33726922 430 578 26.7716 2.6162124 762 12.7 47.5738233 20.33726922 430 578 26.8732 2.413

45

5.4 APPENDIX D – THE GRAPHS OF MICRO-CRACK DEPTH AGAINST VARIOUS PARAMETERS USED TO DERIVE THE MICRO-CRACK CORRELATION IN SECTION 2.6

71. Figure 5 illustrates the graphs that were generated when deriving the revised micro-crack correlation. They show the micro-crack depth plotted against various parameters. Regression lines were then applied to the plotted points, with the equation of the lines given and the R2 value given as a measure of the goodness of fit.

46

Crack depth vs pd/2t

y = 3.5256e-0.0011x

R2 = 0.7122

00.5

11.5

22.5

33.5

44.5

5

0 2000 4000 6000 8000

pd/2t

crac

k de

pth crack depth vs pd/2t

Expon. (crack depthvs pd/2t)

crack depth vs dent depth * pd/2t

y = 53888e-0.4939x

R2 = 0.4925

020000400006000080000

100000120000140000160000180000

0 1 2 3 4 5

crack depth

dent

dep

th *

pd/2

t

crack depth vs dentdepth * pd/2t

Expon. (crack depthvs dent depth * pd/2t)

a b

crack depth vs dent depth

0102030405060708090

0 1 2 3 4 5

crack depth

dent

dep

th

crack depth vs dent depth

crack depth vs dent depth * pd/2t / cvn

y = 1476.2e-0.2893x

R2 = 0.2375

0

1000

2000

3000

4000

5000

6000

0 1 2 3 4 5

crack depth

dent

dep

th *

pd/2

t / c

vn

crack depth vs dentdepth * pd/2t/ cvn

Expon. (crack depthvs dent depth * pd/2t/cvn)

c d

crack depth vs dent depth * pd/2t / sigma_y

y = 147.88e-0.5273x

R2 = 0.5431

050

100150200250300350400450

0 1 2 3 4 5

crack depth

dent

dep

th *

pd/2

t / s

igm

a_y

crack depth vs dentdepth * pd/2t /sigma_y

Expon. (crack depthvs dent depth * pd/2t /sigma_y)

crack depth vs dent depth * pd/2t / sigma_y * cvn

y = 5418.9e-0.7329x

R2 = 0.478

0

2000

400060008000

10000

12000

14000

16000

0 1 2 3 4 5

crack depth

dent

dep

th *

pd/2

t / s

igm

a_y

* cvn

crack depth vs dentdepth * pd/2t /sigma_y * cvn

Expon. (crack depthvs dent depth * pd/2t /sigma_y * cvn)

e f

crack depth vs dent depth * pd/2t / sigma_y / cvn

y = 4.0355e-0.3217x

R2 = 0.287

0

2

4

6

8

10

12

14

0 1 2 3 4 5

crack depth

dent

dep

th *

pd/2

t / s

igm

a_y

/ cvn

crack depth vs dentdepth * pd/2t /sigma_y / cvn

Expon. (crack depthvs dent depth * pd/2t /sigma_y / cvn)

crack depth vs dent depth * p/2t

y = 1.8893e-0.0286x

R2 = 0.62060

0.51

1.52

2.53

3.54

4.55

0 50 100 150 200 250

dent depth * p / 2t

crac

k de

pth crack depth vs dent

depth * p/2t

Expon. (crack depthvs dent depth * p/2t )

g h

47

crack depth vs (PD+dd)/2t

y = 3.5331e-0.0011x

R2 = 0.7125

00.5

11.5

22.5

33.5

44.5

5

0 2000 4000 6000 8000

(PD+dd)/2t

crac

k de

pth crack depth vs

(PD+dd)/2t

Expon. (crack depthvs (PD+dd)/2t)

crack depth vs PD/2t + dd

y = 3.7092e-0.0011x

R2 = 0.7194

00.5

11.5

22.5

33.5

44.5

5

0 2000 4000 6000 8000

PD/2t + dd

crac

k de

pth crack depth vs PD/2t

+ dd

Expon. (crack depthvs PD/2t + dd)

i j

crack depth vs P(D+dd)/2(t+gd)

y = 3.5518e-0.0013x

R2 = 0.7826

00.5

11.5

22.5

33.5

44.5

5

0 2000 4000 6000 8000

P(D+dd)/2(t+gd)

crac

k de

pth crack depth vs

P(D+dd)/2(t+gd)

Expon. (crack depthvs P(D+dd)/2(t+gd))

k

Figure 5 Graphs of micro-crack depth against various parameters

72. Figure 5 graph k produced the best fit and was therefore chosen for the revised micro-crack correlation.

73. A further graph was produced to check that the exponential curve, automatically generated by Excel in Figure 5 graph k, could be reproduced when the real data was fed into the equation. This is shown in Figure 6, where the squares are the actual data points and the diamonds are the values of the micro-crack depth calculated by feeding the data into the derived equation.

48

A check that the derived exponential correlation resembles the data

00.5

11.5

22.5

33.5

44.5

5

0 2000 4000 6000 8000

P(D+dd)/2(t + gd)

crac

k de

pth

Datay = 3.5518*e(-0.0013x)

Figure 6 Graph to reproduce the exponential curve derived by Excel that best fits the observed data

74. A comparison with Figure 5 graph k shows that the curve can be reproduced when the data points are fed into the exponential equation.

49

5.5 APPENDIX E – ADDITIONAL INFORMATION OBTAINED ON THE MICRO-CRACK CORRELATION

75. After the analysis of the data to derive the micro-crack correlation was performed, detailed in Section 2.6, a report was passed to the author that contained additional information [14]. This detailed all 132 test cases as reported by Jones [11] in a form that could be used within PIPIN. In other words, eight more test cases were available for analysis. As this only represents an increase of approximately 6% in the number of test cases available, it was considered that it was unlikely the extra eight tests would significantly affect the results already obtained. To confirm this, however, the process described in Section 2.6 was repeated for the full dataset of 132 test cases.

76. It was found that the form of the equation remained the same as that derived in Section 2.6 (Equation 13) with just minor changes to the constants. When the revised correlation was then used for the 584 pipelines, the average difference between the results from the two correlations was found to be approximately 2%. Much of this variation may be due to the nature of the Monte Carlo solution method, which does not fully reproduce results each time it is run, rather than the change in the micro-crack correlation. Even if this is not the case, then the variation seen is small, when compared to the general uncertainties within the model. It was therefore felt that it was not worth pursuing this any further and that the correlation derived using the 124 test cases was fit for purpose.

77. In addition to the extra data, the report by Francis et al [15] also postulated a form for the micro-crack correlation that is very different from that proposed in this report. In particular, the authors stated that “Intuitively the depth of the micro-crack is likely to be dependent on the size of the remaining ligament and on the amount of plastic straining. Assuming that D/2R is a simple measure of plastic strain, a relationship of the form,

βγ

δ ⎟⎠⎞

⎜⎝⎛

⎟⎠⎞

⎜⎝⎛=

R2D

waC (14)

is postulated, where C, γ and β are constants.” In this equation, D is the dent depth (m), R is the radius (m), a is the gouge depth (m) and w is the wall thickness (m).

78. Ultimately, the authors determined that C = 0.023, β = 0.5 and γ = 1.5. They also stated that the ‘goodness of fit’ of the data to the failure assessment line was visually observed and a check was made on the parameter:

( )( )∑ −i

2riri LFK (15)

where i refers to each of the test cases and Kr, Lr and F are calculated within the model. The aim is to minimise the value calculated by Equation 15. At no point, however, are any numerical values given for how well the correlation fit the data.

79. As part of the analysis of the full 132 case dataset, the correlation detailed in Equation 14 was considered. Using the R2 parameter, which is the coefficient of determination and a measure of the goodness-of-fit, it was found that Equation 14 gave an R2 value of 0.1128 as opposed to an R2 value of 0.7655 for the revised version of Equation 13 (the R2 value for Equation 13 was 0.7826). This indicates that the equation put forward by Francis et al does not produce a good fit to the data. In addition, the calculation given by Equation 15 was also considered. The value for the correlation by Francis et al was 19.23, whilst that for the revised version of Equation 13 was

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9.07 (the value for Equation 13 was 7.23). In other words, the correlation by Francis et al does not fit the data as well as that proposed in this report.

80. In conclusion, the additional data available in the Advantica report by Francis et al [15] does not significantly effect the correlation derived within this report and hence is not considered further. Also, the form of the correlation derived by Francis et al does not appear to provide a good fit to the data and provides a considerably worse fit to the data than the correlation used within this report. The Advantica report has therefore not led to any change in the conclusions derived within this report.

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

4. HSE (2002). Report on a second study of pipeline accidents using the Health and Safety Executive’s risk assessment programs MISHAP and PIPERS. HSE Research Report 036.

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

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

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

8. 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.

9. 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.

10. CEGB (1996). Assessment of the integrity of structures containing defects. Central Electricity Generating Board report R/H/R6.

11. Jones DG (1982). The significance of mechanical damage in pipelines. International, Rohre, Rohreitungsbau, 1982, July, Vol. 21, pt. 7, pp 347-354.

12. Kiefner JF, Maxey WA, Eiber RJ and Duffy AR (1973). Failure stress levels of flaws in pressurised cylinders. ASTM special technical publication. No. 536, pp. 461-481.

13. Cosham A and Hopkins P (2002). The pipeline defect assessment manual. Proceedings of IPC 2002: International Pipeline Conference, October 2002; Calgary, Alberta, Canada. IPC02-27067.

14. Francis A, Miles T and Chauhan V (2004). A new limit state function for the instantaneous failure of a dent containing a gouge in a pressurised pipeline. Advantica report no: UKOPA/PO/4/10.

Published by the Health and Safety Executive 12/15



RR1037

www.hse.gov.uk

The Health and Safety Executive (HSE) use the PIPIN (PIPeline INtegrity) model to determine failure frequencies of major hazard 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); and a predictive model that uses structural reliability techniques to predict the failure frequency due to third party activity (TPA) only. The science underlying the TPA model has undergone a peer review with a number of recommendations made for improvements. HSE asked the Health and Safety Laboratory (HSL) to investigate the recommendations of the peer review and ascertain which recommendations improved the scientific basis of the model. HSL considered each of the recommendations in turn and the impact on the failure rates calculated for a set of 584 pipelines. Following discussions between HSL and other experts, one of the recommendations was rejected. The effect of implementing the remaining recommendations is to increase the failure rates, on average, although some pipelines see a decrease in the failure rate calculated.

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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