Jpe Part2

2nd Quarter, 2009 69

*Author’s contact details:

tel: +55 21 3211 7264

email: [email protected]

A practical approach to pipelinecorrosion modelling: Part 2 –Short-term integrity forecasting

by Dr Érika S M Nicoletti*, Ricardo Dias de Souza,and Dr Sérgio da Cunha Barros

Petrobras Transporte SA, Rio de Janeiro, RJ, Brazil

THE PIPELINE industry is continuously being required to meet the expectations of its many stakeholders,driven by the market’s rising energy demands, and the requirements for increased profitability,

operational safety, and environmentally-friendly procedures. Consequently, more-sophisticated fitness-for-purpose analyses are required in order to achieve maintenance cost reductions while keeping orimproving the system’s overall reliability. In such a complex context, limit-state approaches are best fittedto achieving successful outcomes for those wide-ranging but conflicting expectations. Indeed, cutting-edgepipeline defect-assessment codes have embraced this philosophy, but none have included clear and conciseguidance on the subjects of forecasting corrosion growth and estimating in-line inspection (ILI) toolmeasurement error. Current work has been undertaken aiming to provide a set of guidelines on modellingand analysis procedures for corrosion metal-loss growth on ageing pipelines, using as its input corrosion-monitoring and inspection data. In the preliminary stage, ILI results and electrical-resistance probe (ERP)readings from several oil pipelines were evaluated in order to define the typical variances in pipelinecorrosion. This investigative work gave rise to the development of a predictable relationship between thegrowth rate and its standard deviation, and a short-term forecasting model has been developed based onthe premise of a steady metal-loss rate coefficient of variation. In this paper, the mathematical frameworkfor this is detailed based on different configurations of the input data: single and multiple ILI, with or withoutthe addition of ERP results. Additionally, two case studies are given which illustrate the model’s applicationand results. The model is easily implemented using commercially-available mathematical spreadsheets, andthe entire procedure demands little skilled work. The results are highly reproducible, with their overallquality relying mostly on the consistency of the input data.

PIPELINE OPERATORS often make use of periodical

in-line inspection (ILI) to manage their systems’

corrosion. Another widely-used practice is in-service

monitoring, such as by using electrical-resistance probes

(ERP). While the latter captures corrosive conditions at

particular locations as they vary with time, the former maps

the accumulated damage due to corrosion, along the whole

pipeline length, at a single moment in time. Both techniques

can independently produce vast amounts of data; providing

altogether a valuable resource for estimating future corrosion

– at least when the past and future operating conditions are

expected to be similar.

However, there is no consistent guidance in the technical

literature concerning the use of those data for estimating

corrosion rates, particularly when a combination of ILI and

ERP is available. The current work has therefore been

developed with the aim of providing a systematic approach

for inferring corrosion growth rates from the available

collected inspection and monitoring data. The overall

objective was to determine the pipeline’s short-term

acceptability for continued service.

In the preliminary stage, ILI results and electrical-resistance

probe readings from several oil pipelines were evaluated in

an attempt to characterize typical metal-loss rate values.

The study provided evidence that the standard deviation of

the data is roughly proportional to the mean, making the

ratio (commonly known as the coefficient of variation) a

suitable parameter for representing the pipeline corrosion

processes.

The Journal of Pipeline Engineering70

Subsequent work included the development of a

mathematical model for forecasting metal loss due to

corrosion, based on the premise that each operating regime

for each pipeline could be characterized by a modelling

metal-loss process considering steady relative variability.

This could be determined by using either ERP or ILI data,

according to the operational history particulars of each

case.

Detailed formulae are presented for each of the possible

configurations of data sets. The procedures have been

validated and calibrated for short-term applications,

including the prediction of the locations of possible failure

sites, ascertaining rehabilitation needs, and establishing re-

inspection intervals as well as maximum operating pressure

profiles. In order to demonstrate the model’s application

and results, two case studies are briefly presented.

Overview of assumptions

In Part 1 of this paper, the simplifying assumptions for the

long-term model were defined. For the short-term model,

the principal difference is that the growth of axial and

longitudinal flaws is disregarded.

Before introducing the specific aspects of the current work,

for the sake of general understanding, the remaining and

unmodified simplifying assumptions are summarized below,

together with the postulated ‘principle of local corrosion

activity’.

Unmodified assumptions

• the defect population for analysis should be defined

based on a dimensional threshold related to the ILI

tool’s accuracy;

• the corrosion process can be characterized by a

constant probability density function (pdf) based

on past process behaviour;

• the distribution of all data is assumed to follow a

Gaussian curve

• the internal and external corrosion processes should

be analysed separately;

• all defects were instantaneously formed at their first

environmental exposure;

• a coating degradation time is assumed for external

defects;

• cathodic protection remains in the steady-state

condition during the service life of the pipeline.

The principle of local corrosion activity

The principle postulates that incidences of metal loss

located close to each other and on the same side of the pipe

wall (either external or internal) will be subjected to similar

conditions of corrosion attack. Each defect is associated

with a local zone of influence of the corrosion process,

which is individually defined by its axial up- and downstream

extent and its range length, as specified in Equn 1. The zone

of influence will include a predetermined number of

adjacent metal-loss anomalies, empirically defined by the

vicinity parameter (n). In order to be as representative as

possible, the following ranges of the control parameters are

recommended: vicinity parameter greater than 25 (n > 25),

and segment length average larger than 1km (Li>1000).

L H Hi i n i n

= −+ − (1)

As typical corrosion-rate histograms generally present

tailored patterns, one of the major advantages of the

application of the local corrosion activity principle is its

normalizing effect on the population of corrosion rate

data, as demonstrated by the histograms in Fig.1.

Hot spot considerations

Given that the current model has the primary aim of

pipeline rehabilitation, safety measures have been

introduced in order to prevent underestimating the growth

of metal loss in the presence of highly-localized corrosion

conditions. The general logic for this is presented in Fig.2;

0

1000

2000

3000

4000

5000

6000

7000

0,05

6

0,06

4

0,07

2

0,08

0

0,08

8

0,09

6

0,10

4

0,11

2

0,12

0

0,12

8

mm/year

Local

Individual

Fig.1. The normalizingeffect of the application

of the local corrosionactivity principle.


the following additional considerations are also applicable:

• stray current influence zones: use characteristic

lengths (Li) not greater than 100m;

• microbiologically induced corrosion (MIC):

individual corrosion rates greater than their local

99 percentile must be individually determined,

taking into account specific evolution times. These

should be established based on expert judgment,

independently of the pipeline’s service life (!ts).

Note that both onshore approach areas subject to tidal

variations (the tide zones on offshore pipelines), and regions

around insulating joints (such as on piers and industrial

pipelines) could also require special consideration.

Relative variability of metal loss

The short-term forecasting project included a preliminary

study in which ILI and ERP metal-loss rate data from

several different pipelines were evaluated. Using the

Gaussian behaviour premise, these data were characterized

by their expected value (the mean) and their relative

variability or coefficient of variance, as represented by

Equn 2. The results that were obtained are presented in

Tables 1 and 2, for the ILI and ERP data evaluations,

respectively.

cvR

r=σ

(2)

A comparison of the values of average corrosion rates

shows that there are few strong similarities between the

results from the two techniques. In this regard, it is worth

noting that ILI represents the overall damage accumulated

during the pipeline’s entire service life, whilst ERP data are

usually restricted to a relatively short period. Thus, as ILI

data have consistently produced larger averages than ERP,

this could be interpreted as evidence of a thriving company

strategy for internal corrosion mitigation. Indeed, further

investigation, incorporating historical weight-loss coupon

data (out of the scope of this article), has given ample

confirmation of this.

Despite the fact that ERP monitoring data are theoretically

better fitted to reflect the most recent operational

circumstances, they can only capture variations in the

severity of the corrosion process attack over time at their

specific location. Due this restriction, they have

conventionally been used as a qualitative indication to

characterize the trend of the process, and not as a quantitative

measurement of the continuity of the pipeline’s corrosion.

Therefore, in order to produce a consistent profile of the

corrosion rate along the pipeline’s length, ILI data should

be used. However, when significant changes in the system’s

operating conditions have taken place, the run-comparison

approach is preferred1.

Special considerations for ERP

ERP data usually contain large amounts of electronic noise,

and therefore a filtering procedure is strongly advised. In

the current study, a 3-hr sampling period was averaged for

Fig.2. Logic flowchart for metal-lossgrowth under general hot-spotconditions.

1 If this approach is not feasible, a proportionality study can be made

based on available data from ERP or coupons.

dINSP

d i>=perc 0.8dLi. dLi = di

Loop i >N

∑+=

−= +=

inj

nij

j

Lin

dd

12

cvd LiLi .=σ

Y

N


the daily value and the corrosion rate was obtained using a

five-point algorithm that minimizes the effect of the noise

on the numerical derivative. Figures 3a and 3b illustrate

this procedure: firstly in a standard situation, where the

slope of the trend line corresponds to the local EPR-

measured metal-loss growth; and secondly, where a change

in the operational regime is illustrated by a shift in the slope

of the trend line.

It is worth noting, for instance, that when the flow regime

is expected to present very low corrosivity conditions, the

use of ERP data should be avoided, given that – under such

conditions – it could became difficult to differentiate

between electronic noise and a real sensor response.

Furthermore, data-acquisition periods must be

representative of the pipeline’s future operational service

conditions2.

Framework for single runs

According to the principle of local corrosion activity, each

defect will have an associated population, defined as being

the (n) – the vicinity parameter – defects immediately up-

and downstream. The defect-analysis population will have

Table 1. EPR data evaluation results.

Table 2. ILI data evaluation results.

2 When seasonal operational changes are expected, greater acquisition

periods are recommended.

)raey/mm( VC

1RPE 4000.0 011.0

2RPE 6150.0 873.0

3RPE 6100.0 520.0

4RPE 3000.0 881.0

5RPE 6720.0 240.0

6RPE 4000.0 391.0

7RPE 3100.0 978.0

8RPE 3400.0 222.0

9RPE 6050.0 210.0

01RPE 9110.0 420.0

11RPE 3000.0 962.0

naem 410.0 312.0

etarlacoL vclacoL efilecivreS

1ILI 560.0 891.0 72

2ILI 780.0 082.0 91

3ILI 410.0 019.0 32

4ILI 080.0 031.0 33

5ILI 540.0 032.0 23

6ILI 940.0 340.0 24

8ILI 390.0 082.0 13

9ILI 870.0 091.0 13

naem 460.0 382.0


its local corrosion rates, defined as random variables, with

their average established by Equns 3aa and 3ab – respectively

- for the internal or external anomalies being considered.

The associated standard deviation values are defined by

Equn 3b. As previously discussed, the coefficient of variance

(cv) values should be determined based on ILI or ERP data,

depending on which is the most appropriate for representing

the future anticipated short-term corrosion process.

Rd

tLi

i

s

=∆ (3)

R

d

n tLi

jj i n

j n i

s

=+

= −

= +

∑( ).2 1 ∆

(3a-a)

R

d

n t tLi

jj i n

j n i

s c

=+ −

= −

= +

∑( ).2 1 ∆ ∆

(3a-b)

σLi

R cvLi

= . (3b)

Future defect depth

Once defect corrosion rates have been determined, the

future defect depth can then be defined as being the

original defect depth added to the metal loss which should

be expected within the time period under consideration

(Equn 4a). The associated dispersion of future defect

depths should take account of tool measurement error on

ILI-measured depths as well as the expected deviation on

the overall metal-loss rate over the period of time considered,

as shown in Equn 4b.

d d R tfi i Li f

= + .∆ (4a)

σ σfi f Li

ttE

c= ( ) +

∆

22

(4b)

Damage tolerance

Several metal-loss defect-assessment criteria can be used to

determine damage tolerance. In each case, the analyst

should choose an appropriate criterion in order to find out

the maximum allowable pressure in the defect region

according to its forecast depth, as represented by Equn 5.

P f d l wif f i i

= ( , , ) (5)

0,0322

0,0323

0,0324

0,0325

0,0326

0,0327

0,0328

0 500 1000 1500 2000 2500 3000 3500

h

mm

0,0373

0,0374

0,0375

0,0376

0,0377

0,0378

0,0379

0 500 1000 1500 2000 2500 3000 3500

h

mm

Fig.3. Examples of ERP-acquireddata: (a – top) standard case understeady corrosive attack (trend line inred); (b – bottom) after a change inthe pipeline operating conditions.

3 The maximum allowable pressure profile can be determined based on

hydraulic simulation of worst-case operational scenarios. Otherwise, it

can be assumed to be constant.


Defect relativity acceptance

The failure pressure associated with a defect’s future depth

(Pif) should not be exceeded by the maximum operating

pressure expected at the defect’s location (MAOPi)3. This

failure pressure is represented by the limit-state function

shown in Equn 6, where Pif is characterized by a normal

distribution, while the MAOPi is a deterministic value; in

other words, the probability of the pipeline exceeding the

limit-state condition at each defect (POEi) can be determined

as the area on the left-hand side of the maximum allowable

operating pressure under the Pif probability density function

(pdf), as shown in Fig.4.

MAOP Pi if

− < 0 (6)

The widely-known Pipeline Operator’s Forum concept of

‘estimated repair factor’ (ERF) has been adapted to the

current approach. Using this, each defect has its operational

acceptability determined by Equn 7, where APF is allowable

probability of failure at each defect location, which should

be previously determined based on ROW reliability studies.

ERFPOE

APFi

i

i

= (7)

The single run procedure:a case study

A 100-km long trunk line with a constant 22in diameter

and 6.35mm wall thickness (referred to in Part 1 as Pipeline

3) was chosen to demonstrate the single-run model. The

pipeline has recently been rehabilitated to meet a flow-

capacity expansion, and hydraulic simulation was used to

define its new maximum operating pressure profile. Pipeline

degradation had principally been caused by internal

corrosion, and the accumulated channelling damage is

extensive. ERP data were available.

The pipeline’s future integrity condition was ascertained

considering a five-year metal-loss growth of the anomalies

reported by internal inspection. The single-run modelling

procedure was used to forecast the acceptability of each

defective region, considering both ILI and ERP cvs.

Additionally, in order to provide a reference, ERF was also

determined using a traditional deterministic approach.

Figure 5 presents the results obtained for the 200 worst

pipeline anomalies: blue and red dots representing single-

run model results for ILI and ERP cv, respectively, and the

green indicating ERFs settled on deterministically. In the

figure, the results of the first two procedures present a

remarkable match, demonstrating the model’s overall

robustness. They also provide a clear distinction of defect

impact on pipeline reliability, easily permitting their

categorization by risk. The deterministic approach results,

on the other hand, show only a very slight variation among

the defects that are considered, concealing their true

operational risk.

Framework for run comparisons

When two sets of ILI data are available, and an estimate of

the corrosion rates based on the operational period between

the inspections is required, data resulting from both runs

can be compared4. In such a case, the quality of the results

would depend on a number of factors, including:

• Tool technologies: must be the same or similar,

otherwise comparison of the raw signals is necessary.

• Tool accuracy: both inspections should have been

performed using tools of a similar accuracy.

• Run performance: both runs must have been

successfully completed.

• Data alignment: independent of the segmentation

strategy adopted, the quality of the data alignment

could have a considerable impact on the results.

Fig.4. Plot of the future probabilisticfailure pressure of a defect versus its

deterministic MAOP.

4 The proposed run-comparison procedure should preferentially use

non-clustered data.


Segmentation strategy

A common procedure when dealing with run comparisons

is to divide the pipeline into a number of sections;

traditionally, this is on the basis of constant length (e.g. 1

or 10km), or zones of similar characteristics. The latter

could be based on distinctive features affecting the corrosion

process that takes place along the pipeline, such as stray

current influence zones, changes of flow regime, etc.

Alternatively, instead of pipeline sections, a population

segmentation process can also be adopted in which the

global population is separated into sub-groups which contain

defects with similar characteristics. In this case, the division

criteria should be determined based on statistical analysis

and expert judgment. Some examples of such a procedure

are:

• Cathodic protection effectiveness: within a specific

distance from the pipeline rectifiers or anode beds.

• ROW topography (water accumulation at low

points).

• Coating effectiveness (field/plant applied coatings)

Mathematical formulae

After having been defined, inspection data sub-populations

must be paired with those from the preceding inspection,

and both should then have their average depths determined.

The metal-loss growth rate between these inspections can

be inferred based on the average depth differences. In the

current work, the corrosion rate was assumed to be

represented by a Gaussian distribution, and can be

determined based on Equns 8a and 8b, in which the cv

value is based on the most recent inspection or ERP data.

Rd d

trc

i

=−2 1

∆ (8a)

σrc

R cvrc

= . (8b)

A broad outline of the run-comparison logic is shown in the

flowchart in Fig.6. Future defect geometry and acceptability

can be determined, as has been discussed above5.

The run comparison procedure:a case study

A 2.5-km long subsea oil pipeline section of constant 34in

diameter, with wall thicknesses ranging from 0.375 to

0.5in, and with a service life of 35 years, was chosen to

demonstrate the run-comparison model. No ERP data

were available. The two last ILIs were performed using MFL

tools, with an interval of seven years. Several internal

corrosion-mitigation actions have been implemented over

the last decade. Only the internal metal-loss anomaly

population has been assessed.

Figure 7 depicts local depth histograms of the internal

metal-loss anomalies, considering the population reported

by the two most-recent ILI inspections; by comparing them,

one can clearly note the growth in overall metal loss. The

corrosion rate has been generically defined for the whole

segment, according to the formulae presented in the previous

section and also, individually, as stated by the proposed

0

1

2

3

4

5

0 20 40 60 80 100 120 140 160 180 200

worst anomalies

ER

F

a

b

c

Fig.5. Five-year ERF of the worst internal metal anomalies determined by:(a) the single-run probabilistic approach based on ILI data;

(b) the single-run probabilistic approach based on ILI and ERP data;(c) the traditional deterministic approach.

5 Application of the current procedure is not recommended when the

relative variability of the metal-loss rate is greater than unity.


single-run methodology for both inspections. The results

from these procedures demonstrate that the mitigation

strategy has reduced the metal-loss rate by almost 50%.

The pipeline’s future integrity was assessed taking into

account a time-interval of five years and defect geometries

as forecast by the run-comparison and single-run procedures

(using as input to the latter the data from the most-recent

inspection). The resulting acceptability condition for the

200 worst anomalies is displayed, as ERFs, in Fig.8. The use

of the run-comparison procedure has reduced the

rehabilitation scope by more than 70%.

Conclusions

In recent years growing quantities of pipeline metal-loss

data derived from ILI and ERP monitoring are becoming

available worldwide. Both represent a considerable body of

Fig.6. Flowchart for determining the corrosion rate by run comparison on a pipeline by splitting its defect population intotwo sub-groups.

Fig.7. Histogram oflocal metal-lossaverage depths

from the run-comparison case

study inspections 1and 2.

2,7

3,2

0,00

0,05

0,10

0,15

0,20

0,25

2,00 2,25 2,50 2,75 3,00 3,25 3,50 3,75 4,00 4,25

d [mm]

f(x)

INSP1

INSP2

Segmetation

Criterion

INSP1A

Y Y

d1A

INSP2A

d2A

RrcA = (d2A – d1A)∆ti

σrcA = RA.cv INSP1B

d1B

RrcB = (d2B – d1B)∆ti

σrcB = RB.cv

d2B

INSP2B

Segmetation

Criterion

N

INSP2

N

Loop j

<N1 >N1

Loop j

<N2

>N2

INSP1


evidence regarding past behaviour of the corrosion process,

but there is a lack of industrial guidelines regarding their

use in corrosion-rate estimation.

This paper introduces a simple approach for accomplishing

short-term metal-loss forecasting through the use of ILI

data, where necessary juxtaposed with available ERP data.

The project also considers long-term forecast modelling,

which was presented in the first part of this work. As the

latter was aimed at remaining-life estimation, the current

work has been mainly directed towards the prediction of

rehabilitation needs and the definition of re-inspection

intervals.

The project was undertaken based on two innovative

principles: local corrosion activity, and the steady relative

variability in metal-loss growth under typical pipeline

operational conditions. The work included the development

of an independent mathematical framework suitable for

different input data sets, which include data from a single

ILI run, and comparison of data between two ILI runs.

Available ERP data can be incorporated into both when it

is necessary to reflect the most recent operational

circumstances.

The single ILI modelling procedure can incorporate special

considerations to avoid underestimation of the metal-loss

growth rate at hot-spot sites. Also, the proposed strategy for

dividing the pipeline defect population into sub-groups for

run-comparison purposes could considerably enhance the

result’s significance.

Implementation of the model is straightforward and does

not require special skills. Its application is simple, only

requiring expert judgment in order to define its validity in

non-standard cases and for interpretation of general results.

It is worth noting that the entire study was carried out, and

consequently consistently calibrated, using downstream

pipeline system data. Thus, it is strongly recommended that

a validation analysis of the proposed values of the model’s

empirical parameters is established for upstream

applications.

Acknowledgments

The authors would like to thank Petrobras Transporte S.A.

for permission to publish this paper, and their colleagues

Carlos Alexandre Martins and João Hipólito de Lima

Oliver for many contributions and enlightening discussions.

Nomenclature

"r: standard deviation on a population of

corrosion rate values [mm]

"fi: forecast defect depth standard deviation

[mm]

"Li: local corrosion rate standard deviation

[mm/year]

"rc: standard deviation of corrosion growth rate

produced by run comparison [mm/year]

!ti: re-inspection interval [years]

!tc: coating degradation lag [years]

0

1

2

3

4

0 25 50 75 100 125 150 175 200

worst anomalies

ER

F

single run procedure

run comparison procedure

Fig.8. Five-year ERF of the 200 worst internal metal anomalies determined by single run and run comparison procedures.


!tf: forecasting lag [years]

!ts: pipeline service life [years]

APFi: allowable probability of failure

c: confidence level Gaussian adjustment

parameter

cv: coefficient of variance of corrosion rate

population

d1: previous inspection (INSP1) metal-loss depth

average [mm]

d1A/B

: metal-loss depth average of a INSP1 sub-

population [mm]

d2: newest inspection (INSP2) metal-loss depth

average [mm]

d2A/B

: metal-loss depth average of a INSP1 sub-

population [mm]

dfi: defect future depth [mm]

di: individual metal-loss depth [mm]

dj: individual metal-loss depth [mm]

dINSP

: defect depth population reported by ILI

[mm]

dLi: the local average for a defect metal-loss depth

[mm]

ERFi: estimated repair factor for defect future

geometry

Et: tool measurement error [mm]

Hi: defect odometer [m]

INSP1: defect depth population reported by the first

ILI

INSP1A/B:

INSP1 sub-population

INSP2: defect depth population reported by the

second ILI

INSP2A/B:

INSP2 sub population

li: defect length [mm]

Li: local segment length [m]

N: analysis defect population

n: vicinity parameter

Pif: defect forecast failure pressure [kg/cm2]

POEi: defect probability of exceedance in the limit-

state condition

RLi: local defect depth corrosion rate [mm/year]

Rrc: corrosion growth rate determined by run

comparison, in a defect population sub-

group [mm/year]

wi: defect width [mm]

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