evaluating the characteristics of marine pipelines

Van Gelder, Proske & Vrijling: Proceedings of the 7th International Probabilistic Workshop, Delft 2009

451

Evaluating the Characteristics of Marine Pipelines Inspection Data Using Probabilistic Approach

Z. Mustaffa 1,2, G. Shams 1, P.H.A.J.M van Gelder1 1 Section of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences,

Delft University of Technology, Delft, The Netherlands 2 Civil Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia

Abstract: This paper presents a probabilistic approach on analyzing internal corrosion defects formed in a Malaysian gas pipeline using an intelligent pigging (IP) device. The significant parameters of IP data and their characteristics in the pipeline are analysed through correlation and multivariate regression methods. Some insensitive parameters like the defect circumferential width which is normally ignored in the current design codes is given high priority in this analysis. These governing parameters are later evaluated to pro-duce a new dimensionless limit state function that can best describe the characteristics of the defects. Results from the probabilistic analysis are compared to the results from the existing pipeline failure assessment codes.

1 Introduction

Marine pipelines, a complex system comprises a total length of thousands of kilometers, have been the most practical and low price means of transporting hydrocarbon in the offs-hore oil and gas industry. As the structure operates with time, it is exposed to many types of defects. The typical form of defect is the corrosions, as shown in Fig. 1, which may oc-cur in the form of individual pits, colonies of pits, general wall-thickness reduction, or in combinations. Inspection for corrosions in pipelines is normally conducted using an Intel-ligent Pigging (IP) (Fig. 2), a probe that records any internal and external defects that de-veloped along the pipelines. It is able to report the size of the defects with respect to its orientation and location.

2 Case study

A 128 km steel pipeline type API 5LX-65 located at the east coast of Malaysia (Fig. 3) was selected for the analysis. The pipeline transports gas from offshore to onshore. 554 inter-nal corrosion defects of various types were reported by the IP. The IP summarized com-prehensive data on the corrosion defect parameters, represented by the pipeline defect depth (d), longitudinal length (l) and circumferential width (w) as well as the defects orien-tation and location. Descriptive statistics of the IP data for this pipeline is as shown in

Mustaffa et. al.: Evaluating the characteristics of marine pipeline inspection data using probabilistic approach

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Table 1. The wall loss was calculated up to 30% from the actual wall thickness. Fu and Kirkwood (1995) numerically classified defects in this pipeline as shallow (d/t < 0.30), short (l/D < 0.20) and broad (w/t > 0.50) type of corrosions.

Fig. 1: Internal corrosions in pipelines

Fig. 2: Inline inspection using an intelligent pigging (IP) tool to detect the external and internal metal losses in pipeline cross sections [Photo courtesy of TRANSCO]

Fig. 3: Location of the pipeline selected in the study

Defect length

Wall thickness

Defect depth


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Tab. 1 Descriptive statistics of the IP data for pipeline API 5LX-65

Variable Distribution Mean value Standard

Symbol Description Unit deviation variation

d Defect depth mm Weibull 1.90 1.19

l Defect longitudinal length mm Exponential 32.64 23.52

w Defect circumferential width mm Gamma 36.76 33.17

Tab. 2: Failure pressure (PF) models used to compute remaining strength of pipeline subjected to corrosion

Failure pressure models

Failure pressure expression, PF Bulging factor, M

Modified ASME B31G

2( *69) 1 0.85 ( / )1.111 0.85 ( / ) /

SMYS t d tPFD d t M

⎡ ⎤+ −= ⎢ ⎥−⎣ ⎦

2 4 2

1 0.63 0.0034l D l DMD t D t

⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞= + −⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

for 2

50l DD t

⎛ ⎞ ⎛ ⎞ ≤⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

2

3.3 0.032 l DMD t

⎛ ⎞ ⎛ ⎞= + ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

for 2

50l DD t

⎛ ⎞ ⎛ ⎞ >⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

DNV RP F101 2 1 ( / )1 ( / ) /

SMTS t d tPFD t d t M

⎡ ⎤−= ⎢ ⎥− −⎣ ⎦ 2

1 0.31 lMDt

⎛ ⎞= + ⎜ ⎟⎝ ⎠

SHELL-92 1.8 1 ( / )1 ( / ) /

SMTS t d tPFD d t M

⎡ ⎤−= ⎢ ⎥−⎣ ⎦ 2

1 0.805 l DMD t

⎛ ⎞ ⎛ ⎞= + ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

RSTRENG [ ]2 1 ( / ) /SMTS tPF d t MD

= − 2 4 2

1 0.63 0.0034l D l DMD t D t

⎛ ⎞ ⎛ ⎞ ⎛ ⎞ ⎛ ⎞= + −⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

3 Regression analysis of corrosion defects

Several regression analysis techniques were carried out on the pipeline IP data. The aim of the analysis was to examine the relative importance of the parameter defect circumferential width, w. This is due to the fact that the existing design codes/equations of failure pressure (PF) models (Table 2), which are used to compute the remaining strength of pipeline sub-jected to corrosions, have omitted the w term while only the defect depth, d and longitudi-nal length, l terms are used as the governing parameters.


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3.1 Bivariate regression

Preliminary work on the regression analysis involved bivariate computation between two parameters. Even though poor negative correlation was noticed between d and w (Fig. 4a), but l and w seemed to have high correlation between each other, as shown in Fig. 4b. Re-gardless of poor correlation between w and d (25%), good correlation between w and l (39%) is expected to provide good relationship between w and d indirectly. This will be further analyzed using the multivariate function.

R2 = 0.253

012345678

0 50 100 150 200 250 300 350

w (mm)

d (m

m)

(a) Defect depth d vs. defect circumferential width, w

R2 = 0.3849

0

50

100

150

200

250

300

0 50 100 150 200 250 300 350

w (mm)

l (m

m)

(b) Defect longitudinal length, d vs. defect circumferential width, w

Fig. 4: Bivariate regressions for pipeline API 5LX-65


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3.2 Multivariate regression

The results from Section 3.1 have supported the idea to further expand the analysis using the multivariate function. The aim of the analysis was to examine the relative importance of the predictor variable w with respect to d. In addition, since the PF models as proposed by the Modified ASME B31G, DnV or SHELL 92 codes (Table 2) have incorporated the d and l terms, so it is wise to examine the degree of correlation between d and w with the inclusive of l into the multivariate analysis. Many trials were made in choosing the best regression model to determine the best equation describing the dependency of d, l and w terms. The parameter d was chosen as the criterion variable while l and w were the predic-tor variables. It was found that the nonlinear model as given by equation (1) produced the best results.

d = 3.3139 l0.4393 w-1.3071 (1)

The best fit between dpredicted and dobserved which results in R2 value of 75% as shown by Fig. 5a revealed that most of the data were correlated between each other, and this value is statistically very good and acceptable. A histogram and residuals for the data were also plotted for better understanding on the results.

The multivariate regressions carried out for pipeline API 5LX-65 above have resulted with promising founding. In order to support this hypothesis, another multivariate regression analysis was also conducted for another type of corrosion scenario. Similar pipeline with 307 external corrosion defects were analyzed and the results were later compared with the internal defects obtained earlier. Fig. 6a-c below present the best regression results using a nonlinear model as well, as shown in equation (2), with R2 between the dpredicted and dobserved

of nearly 79%, which is also statistically very good and acceptable. This outcome has af-firmed results established in the previous internal corrosion scenario. These two founding from multivariate regression analysis has acknowledged the fact that w is highly dependent upon d and l.

d = 1.9156 l0.5667w-0.8118 (2)


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-1 -0.5 0 0.5 1 1.5 2

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

d(observed)

d(predicted)

(a)

-4 -3 -2 -1 0 1 2 3 40

10

20

30

40

50

60

70

80

90

100

Data

Number of data

0 100 200 300 400 500 600

-4

-3

-2

-1

0

1

2

3

4

Data

Residuals

(b) (c)

Fig. 5: Results obtained from multivariate regression analysis for pipeline API 5LX-65 containing internal defects (a) Comparison between predicted and observed data

(b) Histogram of the standardised residual (c) Residuals


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0 0.5 1 1.5 2

0

0.5

1

1.5

2

2.5

d(observed)

d(predicted)

(a)

-3 -2 -1 0 1 2 30

10

20

30

40

50

60

Data

Number of data

0 50 100 150 200 250 300 350-3

-2

-1

0

1

2

3

Data

Residuals

(b) (c)

Fig. 6: Results obtained from multivariate regression analysis for pipeline API 5LX-65 containing external defects (a) Comparison between predicted and observed data



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4 Limit state function formulation

Results from Section 3 earlier have recognized the importance of defect circumferential width, w as one of the governing parameters representing the characteristics of corrosions in marine pipelines. Since the PF models have ignored this term when determining the re-maining strength of pipelines subjected to corrosions, there is a necessity to formulate an-other equation which takes into account the w term. One of the well known methods in the field of Hydraulics Engineering called the Buckingham-π Theorem was applied to develop the corresponding equation for the above-mentioned problem. A brief discussion on this method is presented in the next section.

4.1 Dimensionless parameters selection

The Buckingham-π Theorem is a method that forms dimensionless parameters from seve-ral possible governing parameters for a certain scenario under investigation. It is one ap-proach applied to select the most significant parameters describing the characteristics of the scenario while omitting the less ones. Interested readers are recommended to refer to book chapter on Dimensional Analysis from any Hydraulics or Fluid Mechanics books for further discussion about this method.

For this case study in particular, seven parameters that are assumed to significantly con-tribute to the remaining strength of pipelines subjected to corrosions were selected, namely burst pressure (Pb), specified minimum tensile strength (SMTS), pipeline wall thickness (t), diameter (D), defect depth (d), defect longitudinal length (l) and defect circumferential width (w). It is important to highlight that the IP device is unable to compute Pb for any pipelines, thus this can best be determined through experimental or numerical studies. Therefore, the Pb database for this study utilized DnV Technical Report (1995). This re-port is a compilation of laboratory tests of corroded pipelines from four institutions, namely American Gas Association (AGA), NOVA, British Gas and University of Water-loo. These participants have conducted many experimental tests for longitudinally cor-roded pipes under internal pressure for different corrosion defect depths, longitudinally lengths and circumferential widths. Out of the 151 burst pressure database reported, only 31 of them was utilized in this work after considering the suitability of the current and re-ported work.

From the Buckingham-π Theorem, four dimensionless parameters were identified, namely Pb/SMTS, t/D, d/t and l/w. The selection of these terms was also technically supported by literatures. For instance, Fu and Kirkwood (1995) in their work denoted the dimensionless term d/t represents the corrosion shape (parabolic, rectangular) in particular, while l/D symbolizes the length of the corrosion model (pitting, grooving or uniform).


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The dependency between the four dimensionless parameters was later formulated using the multivariate regression analysis once again and the nonlinear model was chosen to best describe the parameters, as given in equation (3). This equation is also known as the equa-tion describing the remaining strength (R) of the corroded pipeline.

0.8442 0.0545 0.0104

bP t d lSMTS D t w

− −⎛ ⎞ ⎛ ⎞ ⎛ ⎞= ⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠

(3)

Fig. 7a-c presents statistical description of the multivariate regression analysis that formed this equation. The R2 value between the (Pb/SMTS)predicted and (Pb/SMTS)observed was found to give good correlation with a value of 53%, as shown in Fig. 7a. A histogram and re-siduals for the data were also plotted for better understanding on the results.

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.0450

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

(Pb/SMTS)Observed

(Pb/SMTS)Predicted

(a)

-2 -1.5 -1 -0.5 0 0.5 1 1.5 20

0.5

1

1.5

2

2.5

3

3.5

4

Standardised Residual

Frequency

Histogram for Residuals

0 5 10 15 20 25-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Data

Residuals

Residual Plot

(b) (c)

Fig. 7: Results obtained from multivariate regression analysis for the dimensionless limit state function equation (a) Comparison between predicted and observed data



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4.2 Reliability computation

This section presents the computation of probability of failure (Pf) for pipeline API 5LX-65. Besides results from this study, the failure pressure (PF) models in Table 1 and another recent study by Teixeira et. al. (2008) were included for comparisons. It is important to highlight here, however, that the PF models are completely deterministic, in which the equations are mostly represented by safety factors. Moreover, Teixeira et. al. (20087) had chosen partial deterministic equation in their limit state equation. In principle, any theo-retical equations represented by safety factors should not be treated in a probabilistic man-ner (Mustaffa et. al., 2009). Most of the literatures are somewhat contradict to this idea, as seen in Ahammed & Melchers (1994, 95, 96, 97), Ahammed (1998), Pandey (1998), Caleyo et.al (2002), Lee et. al. (2003, 06), Santosh et. al. (2006), Lee et. al. (2006), Khelif et. al. (2007) and many more. These include studies on offshore and onshore pipelines transporting either hydrocarbon or water. Thus the approach used in Section 3 is consid-ered to be feasible for this study purpose.

As mentioned earlier, the dimensionless equation (3) above will be used to represent the remaining strength (R) of the corroded pipelines while pipeline operating pressure, Po will be used as the load (S). Following the strength term, this load was also made dimensionless by dividing it with the SMTS. Thus, the limit state function equation, Z can be written as,

Z = R – S (4) Inserting equation (3) into (4), the final limit state function equation is given by,

0.8442 0.0545 0.0104oPt d lZ

D t w SMTS

− −⎡ ⎤⎛ ⎞ ⎛ ⎞ ⎛ ⎞= −⎢ ⎥⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎢ ⎥⎣ ⎦

(5)

The limit state is described by Z = 0. The probability of failure (Pf) is then given by equa-tion (6). Failures takes place when the failure surface falls in the region of Z < 0 while Z > 0 is a survival region.

Pf = P(Z ≤ 0) = P(L ≥ S) (6)

The Pf in equation (6) was simulated using the analytical approximation methods called First Order Reliability method (FORM) incorporated in the Prob2B software. Apart from data taken from Table 1, other random variables used for Pf computations for equation (5) and PF models in Table 2 are presented in Table 3.


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Tab. 3 Random variables for API 5LX-65 pipeline reliability analysis

Variable Distribution Mean value Standard

Symbol Description Unit deviation

D Diameter mm Normal 711.2 21.3

T Wall thickness mm Normal 25.1 1.3

SMTS Specified minimum tensile strength

MPa Normal 530.9 37.2

SMYS Specified minimum yield stress

MPa Normal 448.2 31.4

Po Operating pressure MPa Normal 14 - 30 1.4 – 3.0

1.0E-09

1.0E-06

1.0E-03

1.0E+00

0

0.01

0.02

0.03

0.04

0.05

0.06

Po /SMTS

Pro

babi

lity

of F

ailu

re

Dimensionless LSFDnV RP F101Shell 92RSTRENGModif ied ASME B31GTeixera et.al. (2008)

Fig. 7: Probability of failure, Pf for different design codes

Fig. 7 presents the probability of failure computed using equation (5) for different dimen-sionless load terms. The results were compared with the existing PF models taken from different design codes, namely DnV RP F101, Modified ASME B31G, Shell 92 and RSTRENG. A recent work by Teixeira et. al. (2007) was also included in the graph. A common trend seemed to appear from all plots, in which the probability of failure increases as the loads increases. The graph indicates that Shell 92 produced the highest probability of failure with respect to load exerted to the pipeline as compared to others, while the Modified ASME B31G was the lowest. The present work turned out to lie in between the two extremes; lower than the Shell 92, RSTRENG and Teixeira et. al. (2007), while higher than the DnV RP F101 and Modified ASME B31G. It seemed that the reliability of cor-


462

roded pipelines computed from the three lower codes were overestimated while the two higher codes were underestimated when compared to the current work.

This hypothesis may be true because the extra term w included in the present work has made the analysis more comprehensive and complete to represent the whole corrosion area. From the past until the present, there have been many arguments about the parame-ters represented by those codes, particularly the selection for corrosion shapes and a sum-mary of this discussion can be found in BjØrnØy and Marley (2001). The best solution to overcome all doubts pertaining to corrosion shapes is to include its actual size when ana-lysing it, thus by adding the w term, one is looking at a severe scenario of corrosion sizes.

5 Conclusions

This study evaluates the significance of the circumferential width term, w of corrosion de-fects in marine pipelines using the regression analysis. The dimensionless limit state func-tion developed using this term has showed favourable results. The analysis has showed that the probability of failure of the present work was lower than the Shell 92, RSTRENG and Teixeira et. al. (2007), while higher than the DnV RP F101 and Modified ASME B31G. This indicates that the this study apparoach is able to provide better judgement on the reliability of corroded pipeline because it has incorporated all the important parameters (d, l, w) governing the characteristics and development of corrosions. It is proven that the w term should not be given less consideration when assessing the reliability of corroded pipelines.

The study is still in a preliminary stage, thus require more improvements. It may be inter-esting to see the discrepancies when separately analysing the IP data according to different types of corrosions rather than summarizing all IP data as a whole. It may be wise then to consider forming one model that can suit all corrosions scenario in different pipelines.

6 Acknowledgement

The authors would like to thank Petroliam Nasional Berhad (PETRONAS), Malaysia and Taye B. Chanyalew for providing data for this project, which was financed by Universiti Teknologi PETRONAS, Malaysia. Also, special thanks to AliReza Amiri-Simkooei for his preliminary technical support.


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