Z.mustaffa Dissertation

8/12/2019 Z.mustaffa Dissertation

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Chapter 6

SYSTEM R ELIABILITY FOR C ORRODED P IPELINES

6.1 INTRODUCTION This chapter intends to demonstrate the application of the reliability model of corrodedpipelines as proposed in Chapter 5. Pipelines are structures operating in series and thisprovides great advantages to the model. It will be shown later that the model has broadapplications, through which three of its applications will be highlighted in this chapter,namely (i) reliability per pipeline section, and reliability for total pipeline system (ii)

with, and (iii) without the inclusion of length effects.

6.2 R ELIABILITY P ER P IPELINE SECTION In the previous examples in Chapter 5, the pipeline was treated as a single structure butin reality it comprises many subsections, as casted and sized according to manufacturersscope of designs. Most of the time pipeline operators are more concerned about certainpipeline sections which are exposed to bigger threats, as compared to the whole structurealone. Dealing with certain pipeline section of interests seemed to be more economicaltoo.

In conjunction to the development of the reliability model of corroded pipelines i.e. di-mensionless limit state function (LSF) model in Chapter 5 earlier, it is the interest of thepresent section to further expand its capability and potential, especially when dealingwith sectional analysis. Recall that the model as given by equation (5.10) is,

0.8442 0.0545 0.0104o P t d l

Z D t w SM

= TS

LoadResistance


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6 System Reliability for Corroded Pipelines

This model suits the reliability computation for sectional pipelines very well. Reason be-ing, the model can easily takes into account defects distributions (statistical properties ofparameter d , l and w ) at separate pipeline sections. This can be done by first assuming a

pipeline of length L is schematised into n sections by,

L

x = L/ n

Figure 6.1 A pipeline with length L divided into n sections (not to scale)

If the same pipeline API 5LX-65 (as in Chapter 5) were to be tested, and that the pipe-line was divided into four sections, the corresponding corrosion defects statistical prop-erties for each section could be determined, as displayed in Appendix II. Now, to con-tinue with the calculation, re-apply other random variables as displayed in Table 5-2, butthis time fix the operating pressure to be 18 MPa (for example).

The calculations resulted in probability of failure ( P fi ) for each section i of the wholepipeline length as portrayed in Figure 6.2. For comparison, the probability of failure forthe whole pipeline length with corrosion defect properties as given in Table 5-1 was alsoincluded in the figure. It is interesting to notice that different failure values were ob-served at each section. In general the sectional pipelines would fail in the order of 10-3 which corresponds to probability of 1/1000 per year per pipe. Section 1 with the highestmagnitude of failure seemed to be the earliest to fail, followed by sections 4, 3 and finally2. When comparing these outcomes with failure computed from the whole pipelinelength, it was obvious that the latter produced the fastest to fail with P f of 10-2 (1/100).This is true as the higher the defects considered in a certain reliability calculation, thehigher the corresponding probability of failure too. The single pipeline length has takeninto account all defects as a lump sum value whereas dividing into sections enabled thedefects to be fairly distributed. The failure probabilities were distributed throughout the

pipeline section in the same manner as well.

Despite sectional pipeline with similar lengths, the reliability model of corroded pipelines could also be applied to any pipeline distances; so long the defect characteristics could beinterpreted statistically. The same pipeline candidate as shown in Figure 6.3 for exam-ple, portrays clusters or groups of defects concentrated at several different locationsalong the pipeline length. Once their defects statistical properties were known, the P f could be computed following the same steps as described earlier.

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6.2. Reliability Per Pipeline Section

010

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Longitudinal distance (km)

D e f e c t

d e p t h ,

d ( % )

Section 1 Section 2 Section 3

P f1 = 7.8 x 10-3 P f3 = 5.6 x 10

-3P f2 = 4.4 x 10-3 P f 4 = 6.7 x 10 -3

Section 4

P f = 1.3 x 10-2

Figure 6.2 Comparison in probability of failure between sectional and individual pipelineof pipeline API 5LX-65

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Longitudinal distance (km)

D e f e c t

d e p t h ,

d ( % )

P f = 5.6 x 10-2

P f = 6.7 x 10-2

Figure 6.3 Probability of failure computed at sections of interests of pipeline API 5LX-65

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6 System Reliability for Corroded Pipelines

6.3 L ENGTH E FFECTS ON SYSTEM R ELIABILITY OF P IPELINES Recall that a system as cited by Vrijling et al. (2006) in Chapter 2 is defined as a groupof elements or processes with a common objective. When speaking about system reli-ability of pipelines, one is referring to the whole structure for which elements and proc-esses have relations amongst each other. When a complete pipeline is installed, the sub-sections are interconnected to each other which resemble to an operation of system in se-ries , as described in Section 2.4.4.

A pipeline system may be exposed to more than one types of failure as well, but in thiscontext the system reliability is only concerned with failures subjected to corrosionthreat. Even though corrosions in pipelines have been widely studied using probabilisticapproaches, the potential effect of spatial correlation of corrosion defects (in sections of apipeline) on its failure probability has not received much attention. De Leon and Macas(2005) may be one of the first to look at this aspect but their work simply assumed sev-eral degrees of spatial correlation coefficient (of 0, 0.2, 0.4, 0.6, 0.8, and 1.0) for the cor-rosion in determined sections of a pipeline. In reality, the correlation should not besimply assumed as other factors, one of which will be elaborated in the next paragraph,may contribute to the degree of correlation for pipelines aligned in series.

A structure like pipeline which is arrayed in series may promote sectional length effects .Studies on the effect of sectional length for structures operating in series have been pre-

viously carried out, for instance in flood sea defence structures (Van Gelder et al., 2008;Mai Van, 2010). It is then interesting to investigate the length effects to another struc-ture like pipelines and its consequences towards the overall probability of failure of thestructure.

Herein, the procedures to carry out the length effects analysis to pipeline systems wereadapted from reliability analysis applied to flood sea defence structures and systems asreported in Van Gelder et al. (2008) and Van Gelder and Vrijling (2006), with the follow-ing assumptions:

1.

The pipeline system with total length, L can be divided into n number of sectionswith n dependant on the correlation distance .2. The influence of failure modei.e. corrosions equally contribute to the total prob-

ability of failure of a pipeline section.3. Pipeline system has uniformly cross section throughout its length, L.

With reference to Figure 6.1 once again, the pipeline system that has uniform cross sec-tion comprises n sections of certain length. The strength, R at every pipeline section ofa system in series can be described as random variables and the strengths at two adja-cent sections are assumed to be correlated. The degree of correlation depends, amongstother factors, on the distance x between the two points considered.

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6.3. Length Effects on System Reliability of Pipelines

In statistics, the relation between the correlation and the distance can be described by acorrelation function. A common form ofautocorrelation function , describing strengthsat location x and x + x is described by,

( ) ( )2

, corr x

d R x R x x er D - +D = (6.1)

with x as a characteristics under consideration, x as distance between two points (intime) which is known as the distance lag and d corr as correlation distance or sometimes re-ferred to as fluctuation scale . Within a statistically homogeneous length of a pipeline,the number of pipeline sections is identified with lengths equal to the d corr . The d corr isdefined as the distance over which the statistical properties of the reliability function areassumed totally correlated.

In this context, the parameter x is described by corrosion depth (d ) measured in millime-tre (mm) or percentage (%). Corrosion development in a particular pipeline section isassumed to be proportionally related to the corresponding pipeline strength.

To continue the analysis, the reliability index for the i -th section is beta, (for i =1,2,n ),

( ) (i P F b = F - ) (6.2)

and that is given by,

2 2R S

Z R S

m m mb

ss s

-=+

Z = (6.3)

Then the overall failure probability is given by,

( ) ( ) ( ) ( ) ( ) 211 21i

P F n r

b b b b r

- = F - + - F - - F - F - -

(6.4)

Since ( ) ( )2 2

1max and and and 1corr x

d i i j i j i

corr

x P F F P F F e d

r D -

-20%) were found at 11 and 12,while moderate concentration (>7%) were at 1, 6, 9 and 10 and oclock positions.Higher percentage of occurrence at certain pipeline oclock position indirectly tells thehigher tendency for that location towards failure.

Table 8.1 Number of defects at each oclock position in the pipeline

Oclock position 1 2 3 4 5 6 7 8 9 10 11 12Number of defects 26 17 5 16 14 22 16 14 22 25 63 66

1 o'clock26

8%

2 o'clock17

5%

3 o'clock5

2%

4 o'clock16

5%5 o'clock

14

5%6 o'clock

22

7%

7 o'clock16

5%8 o'clock

14

5%

9 o'clock22

7%

10 o'clock

258%

11 o'clock63

21%

12 o'clock66

22%

Figure 8.12 Pie chart on defect distributions at individual oclock positions

Preliminary results presented here are valuable as they enable one to conduct proper en-gineering precautions or maintenances to those locations only. Tackling the problembased on individual oclock position of the pipeline, however, does not seem to be veryeconomical and practical way to do. For instance, the usual form of external corrosion

protection for offshore pipelines is by cathodic protection using sacrificial anodes. Itnormally consists of a zinc or aluminium bar cast about a steel tube and welded on tothe pipeline. In the case of spatial corrosion prediction based on individual oclock

7

1

4

5

11

109 3

6

12

2

Waves

Pipeline

Sea bed

Currents

8

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8 Spatial Corrosion Prediction

position, it does not seem practical to weld every single point of concerns. It may bewise then to further expand the individual oclock position as a group or region in orderto investigate spatial corrosion prediction.

The spatial corrosion prediction defined the so called region when representing the in-teractions between each oclock position in space. The attempt was to group individualoclock position based on regions. It is important to highlight here, however, that thereare various ways to define the regions and this is subjected to many arguments. In thispresent work, the regions were proposed by incorporating the ideologies obtained fromstudies presented in Section 8.2 earlier.

The pipeline was divided into three unsymmetrical regions, as shown in Figure 8.13. Re-gion I which was bounded from the 10 to


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8.4. Discussions

Table 8.2 Summary of defects taken place at each region

Region Oclock positions Total defectsI 10, 11, 12, 1, 2 and


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Table 8.3 Number defects based on oclock position in Region I

0 x 30 km 30 < x 60 km 60 < x 90 km 90 < x 128 km10 oclock 8 4 6 7

11 oclock 34 12 11 612 oclock 47 4 7 71 oclock 9 4 4 92 oclock 10 4 2 1

Note: x is a point at any locations along the longitudinal distance of the pipeline

Figure 8.9, Figure 8.10, and Table 8.3 earlier revealed that the first 30 km (as measuredfrom the pig launcher location) seemed to experience more corrosions compared to otherremaining lengths of the pipeline. Due to the limitation of the database, it was unlikelyto verify the actual underwater condition within the vicinity of the pipeline. Neverthe-less, statistics allow to check for dependency between the oclock position and longitudi-nal pipeline distance. The dependency check for Region I at the first 30 km was carriedout using the chi-square ( 2 ) test of independence . It was used to determine the presenceof any significant association between the variables oclock positions and longitudinalpipeline distance. In other words, the method investigates whether corrosion develop-ment was associated with its location along the pipeline.

The procedures to carry out the chi-square ( 2 ) test could be simplified into four steps,namely,

1. state the hypothesis,2. set the rejection criteria,3. compute the test statistic, and4. interpret results of null hypothesis.

For the first step, two hypotheses were prepared, namely a null hypothesis (H o) and al-ternative hypothesis (H a). The former assumes that there is no association between thetwo variables while the latter speculates that there is an association between the two

variables. Herein the hypotheses were addressed as,

Ho: Oclock position and longitudinal distance are independent

Ha: Oclock position and longitudinal distance are dependant

For the second step, the rejection criteria requires two important parameters namely, de-gree of freedom (DF ) and predetermined level of significance (confidence level). The pre-determined level of significance was assumed to be 0.05 (95% confidence level) whileDF can be determined using below equation,

( 1) * ( 1DF r c = - - ) (8.1)

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8.4. Discussions

where r is the number of levels of oclock positions andc is the number of levels of longi-tudinal distance. Using the information presented in Table 8.4, r was counted to be 5whilec was 3, thus the resulting DF based on equation (8.1) was computed as 8. Having

DF as 8 and predetermined level of significance as 0.05, the critical value ( 2

*,0.05 ) basedon the chi-square distribution table was set to be 15.51.

To continue with the third step, it was also required to compute the expected frequencycount when oclock position isr and longitudinal distance is c (E r,c ) and the chi-squaretest ( 2 ) statistic. The corresponding equations for these parameters are given below,

, ( * )/r c r c E n n = n 2

,

(8.2)2

, ,[( ) / ]r c r c r c O E E c = S - (8.3)

where n r is the number of observations from level r of oclock positions,n c is the numberof observations from levelc of longitudinal distance, n is the number of observations inthe sample and O r,c is the observed frequency count when oclock position isr and longi-tudinal distance is c .

Table 8.4 Data sets for chi-square test for independence for Region Iat the first 30 km of pipeline length

0 x 10 km 10 < x 20 km 20 < x 30 km Row total10 oclock 3 1 4 811 oclock 17 11 6 3412 oclock 34 11 2 471 oclock 5 3 1 92 oclock 9 0 1 10Column total 68 26 14 108Note: x is a point at any locations along the longitudinal distance of the pipeline

The E r,c could be computed as,E1,1= (8*68)/ 108 = 3.02

E1,2= (8*26)/ 108 = 1.16

E1,3= (8*14)/ 108 = 0.62

E2,1= (34*68)/ 108 = 21.41

E2,2= (34*26)/ 108 = 8.19

E2,3= (34*14)/ 108 = 4.41

E3,1= (47*68)/ 108 = 29.59

E3,2= (47*26)/ 108 = 11.31

E3,3= (47*14)/ 108 = 6.09

E4,1= (9*68)/ 108 = 5.67

E4,2= (9*26)/ 108 = 0.75

E4,3= (9*14)/ 108 = 1.17

E5,1= (10*68)/ 108 = 6.30

E5,2= (10*26)/ 108 = 2.41

E5,3= (10*14)/ 108 = 1.30

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Finally, the chi-square test ( 2 ) statistic could be obtained as, 2 = (3-3.02)2/3.02 + (1-1.16) 2/1.16 + (4-0.62) 2/0.62 +

(17-21.41)2/21.41 + (11-8.19) 2/8.19 + (6-4.41) 2/4.41 +

(34-29.59)2/29.59 + (11-11.31) 2/11.31 + (2-6.09) 2/6.09 +

(5-5.67)2/5.67 + (3-0.75) 2/0.75 + (1-1.17) 2/1.17 +

(9-6.30)2/6.30 + (0-2.41) 2/2.41 + (1-1.30) 2/1.30

= 34.79

Since the chi-square test statistic ( 2 ) 34.79 exceeds the critical value ( 2 *,0.05 ) of 15.51,the null hypothesis should be rejected, thus there is a statistically significant associationbetween oclock position and longitudinal distance at the first 30 km distance of thepipeline.

It is important to highlight here, however, that the above conclusion was entirely basedon statistics computation in the absence of underwater inspection reports. It was alsorecommended to conduct site investigations and later to make comparison with theabove findings.

Region II

Region II was proposed to allow for hydrodynamics exerted by currents originated fromthe upstream section. Early studies speculated that the downstream section of the pipe-line would experience high vortex activities (Qi et al., 2006). Field data seemed to agreewell to this hypothesis when representing this scenario onto external corrosion impacts.About 24% of the corrosions were obtained from the analysis. Being the second largestregion to be affected with corrosions, this outcome could be reasonably well accepted asthe pipeline was placed in a shallow water condition which allowed waves action to be-come the dominant environmental factor.

Region III

Region III which was assumed to be governed by the sea bed/soil-structure interactionsproduced the least threat to external corrosions with only 11%. Apparently vortex for-mation at the upstream section of the pipeline caused by the wave trough effect resultedin mild effect towards corrosion too. It was not the interest of the present work to de-bate much neither on soil characteristics nor soil-structure interactions because their con-tributions to the outcomes of this analysis were considered to be minor.

Region Boundary Identification

It may be of concern to understand how the boundaries of each region were identified.This was actually based on qualitative judgement but still subjected to theories of fluids-

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8.5. Conclusions

structure interactions presented at the beginning of this chapter. The work involved ex-panding the coverage of pipelinecircumferential width to certain extent until the theoriesrelated to its coverage were reasonably complied. For instance, to decide whether the 10

oclock position (i.e. downstream section of pipeline) was the best boundary for Region Iand II was entirely based on how the flows move in that section. Knowing (from theo-ries) that the downstream section of the pipeline should have mutual impacts from thewaves and currents, then the 10 oclock position was chosen simply to allow more effectsfrom the currents because waves actions have been originated from the top part of thepipeline as well. For the sake of some simple quantitative computations, interested read-ers are advised to refer to Mustaffa and van Gelder (2010).

8.5 C ONCLUSIONS This chapter utilized actual field data to validate earlier theoretical (experimental andnumerical) works on fluid-structure interactions between external flows (waves and/orcurrents) and circular cylinders (pipelines). The hydrodynamics of vortex flows pro-duced in the fluid-structure interactions were assumed to result in external corrosions onthe pipeline walls. This work critically analysed the spatial consequences of corrosionsby considering the defect orientations measured from the cross section of the pipeline. Itwas proposed to describe the corrosions distributions by regions, instead of analyzing itindividually. Using expert judgements based on principles of the theoretical works, theregion was defined by expanding the coverage of pipeline circumferential width to certainpoint.

Results from this analysis conformed well to both theories on waves and currents but theformer was found to give higher impact to the pipeline probably because the structurewas placed in a shallow water condition which was mostly governed by waves. Certainsection of the pipeline experienced higher corrosion concentrations. It was unlikely toconduct thorough investigation on this aspect due to limitations in the field data set.This then restricted the work to be carried out based on statistics only, thus it was thensubjected for improvements especially when site investigations are possible to carry out.

Two new values were added to the fluid-structure interactions between waves and/orcurrents and pipelines in the proposed region. It was found that (i) each oclock position(as measured with respect to pipeline cross section) would have consistent and uniformcorrosions development throughout the whole pipeline length, but (ii) more corrosionsshould be expected for areas governed by waves, which was mainly dominated by the 11and 12 oclock positions.

The analyses have proven that the idea of interpreting vortex characteristics using exter-nal corrosions on pipelines could be well accepted. A more complicated probabilistic ap-proach, however, may be required for other aspects of fluid-structure interactions as

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170

briefly highlighted in Mustaffa et al. (2009). The updated knowledge from this fluid-structure interaction is hoped to benefit the industry and constructively incorporatedinto the current subsea pipeline designs.


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Chapter 9

C ONCLUSIONS AND R ECOMMENDATIONS

The title of this thesis, System Reliability Assessment of Offshore Pipelines , portrays theapplication of probabilistic methods in assessing the reliability of these structures. Themain intention of this thesis is to identify, apply and judge the suitability of the prob-abilistic methods in evaluating the system reliability of offshore pipelines subjected tocorrosion. The analysis was first emphasized on interpreting corrosion data as randomvariables and probabilistic functions , through which uncertainties of the corrosioninspection tool could be taken into account. The reliability of the pipeline was initiallystudied by treating the structure as an independent unit. The analysis was further

elaborated for pipelines arrayed as a series system of units , with the consideration oflength effects . A framework for the reliability-based maintenance model was alsodeveloped in this thesis, aiming at optimizing the pipeline system operations. Herein,the analysis was mainly focused on improving the practice of releasing corrosioninhibitors into the pipeline. The use of inhibitors is considered to be the most appliedmaintenance practice among pipeline industries because of its simple mechanism to fightagainst corrosions. Last but not least, the thesis also looked into interpreting corrosionsin space using theories on hydrodynamics.

Chapter 2 and 3 have fairly introduced readers to some basic theories pertaining to themain theme of this thesis. While the former describes the methodology that will be util-ized throughout the thesis, the latter acquaints some basic knowledge on corrosions inpipelines. Without doubts the two themes are too broad to be discussed. Thus, descrip-tions presented were rather simplified and straight forward, intentionally prepared to suitthe content of this thesis.

When speaking about maintaining structural reliability, quite often people tend to thinkof sophisticated ways and apply the most updated technologies to achieve it. It has notbeen much attention, however, to look into the primary source of the measured data setfor which the reliability computation relied on. Inspection tools can be considered as aprimary mean that provides direct information to the end users on defects encountered


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9 Conclusions and Recommendations

by any civil engineering structures. The tools are designed to allow tolerances, in whichthese could be a source of uncertainties . Tolerances given by the tools have been quali-tatively addressed as design standards and not quantitatively accounted for when pre-

senting the end results of the measured data set. This scenario can be seen in an intelli-gent pigging (IP) tool, a tool that records internal and external corrosion defects devel-oped in a pipeline. The present work is aiming at illustrating some possible implicationsof ignoring the tolerances of an IP. Herein, the tolerances or noise are described bynormally distributed random variables. Using simple mathematics, data of the noisecould be incorporated into the measured data sets, allowing new data sets to be prob-abilistically simulated. Comparisons have been made between the measured and simu-lated data sets and descriptive statistics of the two have implicitly highlighted the influ-ence of the IP tool tolerance. The proposed framework is simple and straight forwardbut its implications towards sustainability and reliability should not be taken forgranted. Synchronizing the IP data sets should be the first step to consider so that bet-ter estimates on historical corrosion development of a particular pipeline can beachieved. These were the topics of Chapter 4.

Chapter 5 exhibits the possibilities of incorporating a more detailed description of corro-sion shape into a single equation/model. The so called reliability model for corrodedpipelines was simply developed using a dimensionless limit state function (LSF) model.The intention to promote the Buckingham- method as the most suitable method tocarry out the analysis has been acknowledged when results from the proposed framework(model) have been fairly justified with the design codes and past literatures. In terms ofreliability performance, the proposed model was bounded by the two most referred Modi-fied ASME B31G and DNV models. This indirectly describes the present model havingsimilar characteristics to the two, which is indeed favourable. Implicitly, results fromthis chapter supports the idea of not to ignore any less important defect parameters(particularly defect circumferential width, w ) because it has been proven that the pa-rameters (including defect depth, d and longitudinal length, l ) do correlate with eachother statistically. As one component expands in one direction, the other two will alsobe affected accordingly. Relationships do presence in these interactions. It is thenwrong to assume that probabilistic approaches have no value at all, especially in the reli-ability assessment of corroded pipelines.

Since probabilistic modelling deals with random variables, so the goodness of thereliabil-ity model for corroded pipelines of Chapter 5 is subject to the goodness of the field dataset. As much as possible, the analysis tried not to rely on other out-source data sets butonly to utilize field data. Nevertheless, the fact that burst pressure ( P b) data used inthat model cannot be directly obtained from the field, the analysis was then relied onburst data sets reproduced either experimentally or numerically. This model uncertainty may affect the performance of the proposed model to certain extend unless the simulatedP b data sets conformed well to the present corrosion characteristics reported from thefield.

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173

The applications of the reliability model for corroded pipelines are highlighted in Chapter6. The model acts as a solver to pipeline operators when different corrosion scenariosneeded to be tackled. From multiple pipeline sections to a single (whole) length; or even

from one defect to clusters of defects; the reliability of the structure can be computedeasily so long the corrosion characteristics can be statistically determined. Resultsshowed that the probability of failures ( P f ) for a pipeline cuts into several sections wouldbe smaller compared to one section covering the whole pipeline length. In addition, acluster of defects interacting together might provide more threats to the pipeline. Themodel also speculated reliability estimates when the pipeline length effect was consideredin the analysis. The P f for pipeline with length effects was expected to be higher thanthe one without. Probabilistic methods have proven that correlations do exist amongthese corroded pipeline sections. Apparently when acting as one system is series, thestructure has the tendency to promote more danger to the environment.

Chapter 7 was designed using three important principles. There have been some con-cerns among pipeline operators, especially in a developing country like Malaysia aboutthe goodness or suitability of the adapted design standards or codes to the present envi-ronment and operating conditions. There is a need in conducting a compatibility checkbetween those proposed in the design standards and the actual situations. The absenceof available resources and expertise has always been blamed for not being able to carryout the work. This should not be the case in the present time anymore because theknowledge from forensic evidence has allowed the problem to be appreciated in anotheraspect, provided good and reliable measured data sets are available. Valuable informa-tion can be digged up, extracted, investigated and become answers to the problems, simi-larly to what is known as causes and effects. In the context of pipeline systems experi-encing corrosions, ideologies of forensic evidence can be used to provide better under-standing on the development of corrosions as well as mechanisms to fight against them.Obeying to the fact that water can never be avoided from entering the pipeline, whichresulted in corrosion formation, one of the common ways applied to fight corrosion isthrough the use of corrosion inhibitor. Care should be taken when applying this, know-ing the effectiveness of corrosion inhibitor in the real world application is still not certainand remain as a big issue. Consequently, the only thing left to do is to look at the main-tenance practice to release the inhibitor into the pipeline.

Chapter 7 has identified several governing factors for the development of the reliability-based maintenance model , amongst which are the transported hydrocarbon and water it-self. This could be achieved using the input-output model. Through benchmarking , cor-rosions were simulated from the model using the Monte Carlo simulation method. It isimportant to highlight here that the proposed model can be used as an aid to monitorthe effectiveness of the present corrosion inhibitor practice. When applied to presentfield data set from Malaysia pipeline operations, the outcomes revealed that the practiceof releasing inhibitor in the pipelines did not seem to follow specific trends, but to simplyfulfil the total targeted monthly amount. Indeed this result has welcomed the idea tofurther exploit other approaches of inhibitor practice, aiming at optimizing the systemoperation and at the same time minimizing corrosion development. This thesis proposes

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9 Conclusions and Recommendations

174

the release of inhibitor to be conducted according to physics of corrosions itself. This isbecause theories on corrosion physics showed that corrosions evolve every day even withmicro meter increments! If the metaphor of the action-reaction law of Newtons law of

motion were to be applied in this context, factors expediting corrosion process shouldbe counter parted by mechanisms fighting against it too. This hypothesis was thentranslated into time domain of corrosion growth which eventually triggered the idea ofsimulating corrosion based on uniformly and periodically inhibitor released practices.Results showed an improvement in corrosion magnitude (as measured in corrosion depth,d mm or %) if either one of the two practices were to be replaced with the currentpractice in the field.

Cost implication towards the above proposed optimization techniques could not be criti-cally illustrated due to the limitation of the research database. Nevertheless, the advan-tage of simulating smaller percentages of corrosion magnitude has fairly answered its re-sponse towards the repair or maintenance costs associated to corrosion failures. It is rec-ommended that matters associated with costs to be further supported by means of costbenefit analysis (CBA), or other suitable analysis. In addition to that, if the total lifecycle cost (TLCC) were to be carried out for pipeline systems, the analysis will not bestraight forward. This is due to the varying reservoir production profiles with timewhich are proportionally related to pipeline operation systems. The TLCC can only becarried out with complete past information and reliable future predictions data.

Spatial corrosion prediction was the topic of interest of Chapter 8. It is interesting to seehow simple statistic approaches could be applied to speculate corrosion formation inspace. Theories on hydrodynamics of waves/currents near circular cylinder were appliedto support the analysis of pipelines placed close to the sea bed. Vortex activities at thevicinity of the cylinder were assumed to imitate activities surrounding a pipeline whichresult in the formation of external corrosions. Even though the analysis presentedinvolved simple statistics, the hydrodynamics theories on vortices conformed well to fielddata on pipelines experiencing external corrosions placed closed to the shore. It hashelped to provide some preliminary insights about corrosion prediction in space. Thisfield should be further explored probabilistically especially to cater the complicated fluid-

structure interactions. Better descriptions on this aspect will lead to proper reliabilityestimate on pipelines subjected to external corrosions, which is also a continuation of themodel proposed in Chapter 5.

The proposed frameworks in this thesis are simple and straight forward but their im-plications towards sustainability and reliability of pipeline system operations arehighly acknowledged. The frameworks have proven to be able to provide better esti-mate for a time-variant process like corrosion.


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APPENDIX I

Burst Test Data Set(Extracted from DnV Technical Report, 1995)

D t SMTS d l w P b

No. (mm) (mm) (MPa) (mm) (mm) (mm) (MPa)

1 508 6.4 517 3.01 103 102 12.5

2 508 6.4 517 2.94 205 204 9.8

3 508 6.4 517 3.37 205 394 8.45

4 610 12.34 471 4.94 152 574 18.45

5 324 5.93 432 4.68 47 43 13.49

6 324 6.07 432 4.01 59 53 14.29

7 324 5.84 432 3.91 33 21 16.29

8 324 5.99 432 4.67 26 20 15.369 324 6 432 4.38 29 30 16.09

10 324 6.07 432 2.91 41 34 16.95

11 324 5.58 432 4.41 35 31 13

12 324 6.14 432 2.39 29 24 15.78

13 324 6.16 432 4.5 37 30 14.29

14 324 5.95 432 4.17 39 27 15.57

15 324 6.02 432 1.99 50 24 16.12

16 324 6.4 432 3.23 20 19 16.64

17 324 6.01 432 3.6 19 19 16.2218 324 6.3 432 3.57 20 19 15.95

19 324 6.31 432 3.73 20 20 14.16

20 324 6.16 432 3.73 20 20 18.85

21 324 6.27 432 3.76 20 20 19.13

22 324 6.25 432 3.79 20 20 19.27

23 324 6.18 432 3.75 20 20 19.44

24 324 6.45 432 3.05 21 22 15.81

25 324 6.4 432 3.72 39 20 13.87

26 324 6.45 432 3.79 20 21 14.8427 324 6.35 432 3.72 20 21 15.53

28 324 6.27 432 3.77 20 21 17.61


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29 324 6.29 432 3.79 72 21 15.11

30 324 6.24 432 3.79 72 21 15.67

31 324 6.16 432 3.7 20 20 15.25

176


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APPENDIX II

Descriptive Statistics of Corrosion Defects(Corrosion defects for pipeline sections withn =4)

Pipelinelength (km) 0 to 30 30 to 60Defect

parameters Section 1 2

PDF Weibull (2.02, 1.58) Lognorm (1.17, 1.03)

Mean 1.37 1.02

d

Std. dev. 0.70 0.74

PDF Lognorm (33.91, 15.74) Expon (39.99)

Mean 33.54 39.99

l

Std. dev. 13.40 33.70

PDF Weibull (0.62, 27.31) Lognorm (42.83, 32.66)

Mean 32.88 43.03

w

Std. dev. 31.69 33.25

177


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178

Continue

Pipelinelength (km) 60 to 90 90 to 128Defect

parameters Section 3 4

PDF Weibull (1.55, 1.51) Weibull (1.76, 1.14)

Mean 1.36 1.12

d

Std. dev. 0.90 0.65

PDF Lognorm (28.77, 16.96) Expon (32.16)

Mean 28.71 32.16

l

Std. dev. 17.03 25.83

PDF Weibull (0.84, 34.24) Weibull (1.07, 37.14)

Mean 36.18 36.57

w

Std. dev. 38.93 28.37


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Tung, Y.K., Yen, B.C. and Melching, C.S. (2006) Hydrosystems Engineering Reliability Assess-ment and Risk Analysis, New York, McGraw-Hill Co.

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Valor, A., Caleyo, F., Rivas, D. and Hallen, J.M. (2010) Stochastic Approach to Pitting-Corrosion-Extreme Modelling in Low-Carbon Steel,Corrosion Science, 52, pp. 910-915.

Van Gelder, P.H.A.J.M. and Mai Van, C. (2008a) Distribution Functions of Sea Waves and River

Discharges in Flood Risk, Journal of Hydraulic Research , Vol. 46, Issue 2, pp. 280-291.Van Gelder, P.H.A.J.M., Mai Van, C., Wang, W., Shams, G., Rajabalinejad, M., and Burgmeijer,

M.A. (2008b) Data Management of Extreme Marine and Coastal Hydro-Meteorological events,Journal of Hydraulic Research , Vol. 46, Issue 2, pp. 191-210.

Van Gelder, P.H.A.J.M., Buijs, F., Mai Van, C., ter Horst, W., Kanning, W., Nejad, M., Gupta,S., Shams, R., Van Erp, N., Gouldby, B., Kingston, G., Sayers, P., Wills, M., Kortenhaus, A.and Lambrecht, H.J. (2008) Reliability Analysis of Flood Sea Defence Structures and Systems,Brussels.

Van Gelder, P.H.A.J.M. and Vrijling, J.K., (2006) Probabilistic Design in Hydraulic Engineering,Delft, Delft University of Technology Publications.

Van Gelder, P.H.A.J.M. and Vrijling, J.K., (2006) Time Effects and Length Effects , MSc. Prob-abilistic Design in Hydraulic Engineering, Delft University of Technology, 11th November 2010[Lecture notes taken by Z. Mustaffa]

Van Gelder, P.H.A.J.M. (2000) Statistical Methods for the Risk-Based Design of Civil Structures, Ph.D, Delft University of Technology.

Vrijling, J.K. et al, (2006) Probability in Civil Engineering, Delft, Delft University of TechnologyPublications.

Vrouwenvelder, A.C.W.M. and Vrijling, J.K. (1987) Probability in Civil Engineering, Delft, DelftUniversity of Technology Publications.

Wireman, T. (2010) Benchmarking Best Practices in Maintenance Management, 2nd Edition,New York, Industrial Press (IP).

World Corrosion Organisation (2010) WCO Celebrates Corrosion Awareness Day 2010 on April24, 2010 [online article].

Yam, R.C.M., Tse, P., Ling, Li. and Fung, F. (2000) Enhancement of Maintenance Managementthrough Benchmarking, Journal of Quality in Maintenance Engineering , Vol 6, No. 4, pp. 224-240.

Yen, B. C. and Tung, Y.K. (1993) Some Recent Progress in Reliability Analysis for HydraulicDesign, in.: Yen, B.C. and Tung, Y.K. (eds.), Reliability and Uncertainty Analysis in Hydrau-lic Design , New York, ASCE.

Zhao, M., Cheng, L. and Ah, H. (2006) A Finite Element Solution of Wave Forces on a Horizon-tal Circular Cylinder Close to the Sea-Bed, Journal of Hydrodynamics, Ser. B, Vol 18, Issue 3,Supplement 1, pp. 139-145.

Zhou, X. (1993)Experimental Study of Corrosion Rate and Slug Flow Characteristics in Horizon-tal Multiphase Pipeline , Master of Science, Ohio University.

Online References:

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186
http://www.bu.edu/tech/research/visualization/about/gallery/vortex/http://www.bu.edu/tech/research/visualization/about/gallery/vortex/


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Institute for Energy Technology (2011) [Online article, retrieved on 18th January 2011](http://www.ife.no/index_html-en?set_language=en&cl=en )

Kedrosky, P. (2011) Deepwater Horizon Rig Fire: During and After [Online article, retrieved on

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February 2011] (http://paul.kedrosky.com/archives/2010/04/t_deepwater_hor.html )Lvque, E. (2011) [Online article, retrieved on 7th March 2011]

(http://perso.ens-lyon.fr/emmanuel.leveque/research.php )Pigging Products & Services Association (2011) [Online article, retrieved on 18th February

2011] (http://www.ppsa-online.com/ )StarTrak Pipeline Technologies, Inc. (2011) [Online article, retrieved on 18th January 2011]

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Florida Keys [Online article, retrieved on 23rd February 2011](http://themostimportantnews.com/archives/scientists-gulf-of-mexico-oil-spill-could-soon-reach-the-florida-keys)

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L IST OF P UBLICATIONS

Mustaffa, Z., Van Gelder, P.H.A.J.M. and Hashim, A.M., An Insight in Spatial Corrosion Predic-tion, International Journal of Pressure Vessels and Piping, submitted .

Mustaffa, Z., Van Gelder, P.H.A.J.M. and Dawotola, A.W., A Framework in Dealing with Uncer-tainties of Corrosion Inspection Tools, Measurement, submitted.

Dawotola, A.W., Trafalis, T.B., Mustaffa, Z., Van Gelder, P.H.A.J.M. and Vrijling, J. K., RiskBased Maintenance of a Cross Country Petroleum Pipeline System, Journal of Pipeline Sys-tems Engineering and Practice, submitted.

Mustaffa, Z., Van Gelder, P.H.A.J.M., Shams, G. and Dawotola, A.W., A Dimensionless Ap-proach for the Reliability Assessment of Corrosions in Pipelines, Reliability Engineering andSystem Safety, submitted.

Mustaffa, Z. and Van Gelder, P.H.A.J.M., The Length-Scale Effect on System Reliability ofPipelines, Reliability Engineering and System Safety, to be submitted.

Mustaffa, Z., A Framework on Reliability-Based Maintenance Model,Reliability Engineering and

System Safety, to be submitted. Mustaffa, Z., Measuring the Effectiveness of Corrosion Maintenance in Pipelines,International

Journal of Pressure Vessels and Piping, to be submitted.Dawotola, A.W., Trafalis, T.B., Mustaffa, Z., Van Gelder, P.H.A.J.M. and Vrijling, J. K. (2011)

Data-Driven Risk Based Maintenance Optimization of Petroleum Pipelines Subjected to Cor-rosion, the 21 st International Offshore and Polar Engineering Conference (ISOPE ), Vol 1, pp.122-129.

Mustaffa, Z. and Van Gelder, P.H.A.J.M. (2010) Supporting New Insight in Pipeline Hydrody-namics Using Stochastic Approaches on External Corrosion Damage, the 29 th InternationalConference on Ocean Mechanics and Arctic Engineering (OMAE) .

Mustaffa, Z. and Van Gelder, P.H.A.J.M. (2010) A Review and Probabilistic Analysis of LimitState Functions of Corroded Pipelines, the 20 th International Offshore and Polar EngineeringConference (ISOPE ), Vol 4, pp. 625-632.

Mustaffa, Z, Van Gelder, P.H.A.J.M. and Vrijling, J. K. (2009) A Discussion of Deterministic vs.Probabilistic Method in Assessing Marine Pipeline Corrosions,the 19 th International Offshoreand Polar Engineering Conference (ISOPE ), Vol 4, pp. 653-658.

Mustaffa, Z., Shams, G. and Van Gelder, P.H.A.J.M. (2009) Evaluating the Characteristics ofMarine Pipelines Inspection Data Using Probabilistic Approach, the 7 th International Probabil-istic Workshop (IPW), pp. 451-464.

Mustaffa, Z., Shams, G., Van Gelder, P.H.A.J.M. and Vrijling, J. K. (2008) Risk Assess-ment on Aging Marine Pipelines: A Probabilistic Approach, International Conferenceon Environment (ICENV) , pp. 1-9.


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INDEX OF NOTATION AND ABBREVIATIONS

Symbol Description

Intercept of linear regressionequation

Slope of linear regression equa-tionReliability index

o, 1,. Regression coefficients Error of linear regression equa-

tion Scale parameters of PDFs

, Location parameters of PDFs

, Shape parameters of PDFs Mean Standard deviation x

Sum of squared residualsDistance between two points

Corrosion rate

flow Flow stress

Buckingham- parameter

Pi = 3.142* Parameter uncertainty

2 Chi-square test statistics

a Final pitting rate of constant

A Projected corroded area

Ao =dt

b Pitting depth scaling constant

c Corrosion rate inhibition factorc Number of levels of longitudi-

nal distance

CaCO3 Calcium carbonate

CO2 Carbon dioxide

CO3- Carbonate ion

C.O.V Coefficient of variation

d Corrosion defect depth

D Pipe diameter

do Defect depth measured at timeTo

dcorr Correlation distance

e Electrone

Ei

Es

Distance between the sea bedand the lower part of the pipe-lineFailure of component i

Failure of system

Er,c Expected frequency countwhen oclock position isr andlongitudinal distance is c

Fe Iron

FeCO3 Iron carbonate

Fe++ Iron ion

f x PDF of X

f R,S Joint probability function of Rand S

Fx CDF of X

g Acceleration of gravityHAc Acetic acid


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H+ Hydrogen ion

Ha Alternative hypothesis

Ho Null hypothesisH2 Hydrogen

H2O Water

H2S Hydrogen sulphide

k Constant

l Corrosion defect longitudinallength

lo Defect longitudinal lengthmeasured at time T o

L Length of pipeline

M Folias/bulging factor

mf Coefficient

n Constant

n

n

Number of observations in thesampleNumber of pipeline sections

nr Number of observations fromlevel r of oclock positions

nc Number of observations fromlevel c of longitudinal distance

Or,c Observed frequency countwhen oclock position isr andlongitudinal distance is c

PCO2 CO2 partial pressure

Pcorr Allowable corroded pipe pres-sure

Pb Burst pressure

Pf Probability of failure

Po Applied/operating pressure

r Residuals

r Number of levels of oclock po-sitions

R Strength

Rd Radial corrosion rate

RL Longitudinal corrosion rate

R2 Coefficient of determination

S Load

T Exposure time

T Any future time

T Wave period

To Time of last inspection

t Pipe wall thickness

U Velocity

w Corrosion defect circumferen-tial width

x Realization of X

x1* Bootstrap realization of X

X Realization of X

y Realization of Y

Y Realization of Y

Z Limit state function

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Symbol Description

AGA American Gas Association

AXGR Axial grooving

AXSL Axial slotting

CDF Cumulative distribution func-tion

CI Corrosion inhibitor

CIGR Circumferential grooving

CISL Circumferential slotting

DF Degree of freedom

DNV Det Norske VeritasERF Estimated repair factor

FFS Fitness-for-service

FORM First order reliability method

G Gas

GENE General

ILI In line inspection tool

IP

IQRLHS

Intelligent pigging

Interquantile rangeLeft hand side

LSF Limit state function

MCS Monte Carlo simulation

MIC Microbially-induced corrosion

MFLMLE

MOM

Magnetic flux leakageMaximum Likelihood Estimate

Method of Moment

O

RHS

Oil/condensate

Right hand side

SCC Stress corrosion cracking

SMTS Specified minimum tensilestrength

SMYS Minimum specified yield stress

SORM Second order reliability method

SRB Sulphate-reduced bacteriaSSE Sum of squares, error

SSR Sum of squares, regression

SST Sum of squares, total

PDF Probability density function

PF Failure pressure

PINH Pinhole

PPF Percent point function

TLC Top-of-line corrosion

W Water

WC Water cut


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L IST OF F IGURES

Figure 1.1 Different types of pipeline hazards ...................................................... 14 Figure 1.2 Pipeline failures, by reported cause, as compiled by the Committee

on the Safety of Marine Pipelines (1994).......................................... 15 Figure 1.3 Oil spill disasters (a) Extinguished efforts to control a DeepwaterHorizon rig that caught fire and finally sank in April 2010 in theUSA (Kedrosky, 2011) (b) Concerned researchers and scientistsinvestigating the 2010 oil spills in the Gulf of Mexico (The MostImportant News, 2011) ..................................................................... 16

Figure 1.4 Fault tree analysis for offshore pipeline ............................................... 17 Figure 1.5 Distribution of oil and natural gas reserves among the world's 50

largest oil companies. (Wikipedia: Petroleum Industry, 2011).......... 19 Figure 1.6 Traditional (deterministic) approach of safety analysis considered in

engineering system (Adapted and modified from Singh et al., 2007) 21 Figure 1.7 Comparison in load and strength from two different methods............. 21 Figure 1.8 Risk matrix applied in the qualitative risk assessment........................ 22 Figure 1.9 Brief illustration on candidate pipelines utilized in different chapters

of the thesis ...................................................................................... 25 Figure 2.1 Different types of probability distribution functions plotted based on

corrosion defect depth (d , measured in %); with best fit taken froma lognormal distribution function. ....................................................32

Figure 2.2 Scattergram of two random variables x and y ..................................... 36 Figure 2.3 Failure space as a function of basic variables ...................................... 41 Figure 2.4 Illustration of numerical integration and Monte Carlo sampling

(Adapted and modified from Korving, 2004).................................... 43 Figure 2.5 Representations of series system.......................................................... 44 Figure 2.6 Representations of parallel system....................................................... 45 Figure 3.1 (a)(b) Examples of pipeline failures due to internal corrosions

(Institute for Energy Technology, 2011) (c) Sketch on irregularlength, width, and depth of a typical corrosion defect (Adaptedfrom Cosham et al., 2007) ................................................................ 49

Figure 3.2 Laboratory illustrations on pit corrosions (Adapted and modifiedfrom Rivas et al., 2008) .................................................................... 49


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Figure 3.3 Different forms of corrosion developed on a particular metal surface(Adapted and modified from Freeman, 2002) ................................... 49

Figure 3.4 Different types of scales formed in pipelines (Adapted from Bufton

and Cochran, 2008) ..........................................................................52 Figure 3.5 Laboratory work by Nei , and Lee (2003) showing a cross section of

a steel specimen including an iron carbonate scale acting as abarrier to corrosion (Adapted from Ne i , and Lee, 2003) ................52

Figure 3.6 Different flow regimes that may present in multiphase flows(Adapted from Zhou, 1993)..............................................................53

Figure 3.7 Water vapour condensation of internal pipeline wall ........................... 54 Figure 3.8 Example of hydrates formed in pipelines (Adapted from Bufton and

Cochran, 2008) .................................................................................55

Figure 3.9 Circumferential stress in a pipeline pressurized internally andexternally (Adapted and modified from Palmer and King, 2008)..... 57 Figure 3.10 Pig cleaning philosophy ..................................................................... 63 Figure 3.11 Placing a pig in the pig trap system (United Kingdom Society for

Trenchless Technology, 2011; PETRONAS Technical Standard,1998).................................................................................................63

Figure 3.12 Pig lost in pipeline (StarTrak Pipeline Technologies, Inc., 2011)....... 64 Figure 3.13 Some examples of pigging tools (Pigging Products &

Services Association, 2011)............................................................... 65

Figure 4.1 Corrosion defect distributions as captured by an intelligent pigging(IP) tool ........................................................................................... 70 Figure 4.2 Number of defects according to categories as recorded at one IP

inspection year.................................................................................. 71 Figure 4.3 Number of defects along the longitudinal distance of pipeline as

recorded at one IP inspection year ...................................................71 Figure 4.4 Corrosion depth, d (%) distribution along the pipeline ....................... 72 Figure 4.5 Remaining wall thickness (mm) distribution along the pipeline.......... 72 Figure 4.6 Corrosion defects mapping along the circumference (oclock

orientation) length of pipeline......................................................... 73 Figure 4.7 Simple statistical representation of corrosion data .............................. 74 Figure 4.8 Illustration of initial and extreme values (minimum and maximum)

of a typical normal distribution function of a histogram .................. 75 Figure 4.9 An example of probability density function of corrosion depth, d (%)

measured with respect to pipeline wall thickness.............................. 76 Figure 4.10 An example of extreme value distribution of corrosion depth, d (%)

measured with respect to pipeline wall thickness.............................. 78 Figure 4.11 Decrease in reliability over time as reduced section loss causes an

increase in the bending stress on the girders (Adapted from Esteset al., 2004) ...................................................................................... 79

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Figure 4.12 Realization of a continuous random load process Q (t ) and thepotential exceedence of the deteriorating structural resistance R(t )(Adapted from Melchers, 2005) ........................................................ 80

Figure 4.13 Experimental works by Rivas et al. (2008) showing the growth ofpit depth over time at different exposure times (Adapted fromRivas et al., 2008)............................................................................. 80

Figure 4.14 Historical corrosion development in an offshore pipeline atdifferent times of operation............................................................... 81

Figure 4.15 Systematic error observed in pipeline in

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