Paper published in The Journal of Pipeline Engineering: A practical Approach to Pipeline Corrosion Modelling: Part 2 - Short-term integrity forecasting

1. 2nd Quarter, 2009 69
A practical approach to pipeline
corrosion 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
T HE 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 or
improving the system’s overall reliability. In such a complex context, limit-state approaches are best fitted
to achieving successful outcomes for those wide-ranging but conflicting expectations. Indeed, cutting-edge
pipeline defect-assessment codes have embraced this philosophy, but none have included clear and concise
guidance on the subjects of forecasting corrosion growth and estimating in-line inspection (ILI) tool
measurement error. Current work has been undertaken aiming to provide a set of guidelines on modelling
and 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 pipeline
corrosion. This investigative work gave rise to the development of a predictable relationship between the
growth rate and its standard deviation, and a short-term forecasting model has been developed based on
the premise of a steady metal-loss rate coefficient of variation. In this paper, the mathematical framework
for this is detailed based on different configurations of the input data: single and multiple ILI, with or without
the addition of ERP results. Additionally, two case studies are given which illustrate the model’s application
and results. The model is easily implemented using commercially-available mathematical spreadsheets, and
the entire procedure demands little skilled work. The results are highly reproducible, with their overall
quality relying mostly on the consistency of the input data.
P IPELINE OPERATORS often make use of periodical
in-line inspection (ILI) to manage their systems’
corrosion. Another widely-used practice is in-service
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
monitoring, such as by using electrical-resistance probes ERP is available. The current work has therefore been
(ERP). While the latter captures corrosive conditions at developed with the aim of providing a systematic approach
particular locations as they vary with time, the former maps for inferring corrosion growth rates from the available
the accumulated damage due to corrosion, along the whole collected inspection and monitoring data. The overall
pipeline length, at a single moment in time. Both techniques objective was to determine the pipeline’s short-term
can independently produce vast amounts of data; providing acceptability for continued service.
altogether a valuable resource for estimating future corrosion
– at least when the past and future operating conditions are In the preliminary stage, ILI results and electrical-resistance
expected to be similar. 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
*Author’s contact details:
ratio (commonly known as the coefficient of variation) a
tel: +55 21 3211 7264 suitable parameter for representing the pipeline corrosion
email: erika_nicoletti@petrobras.com.br processes.

2. 70 The Journal of Pipeline Engineering
7000
6000
5000
4000 Local
3000 Individual
2000
1000
0
Fig.1. The normalizing
2
6
4
0
8
6
4
2
0
8
07
05
06
08
08
09
10
11
12
12
effect of the application
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
mm/year of the local corrosion
activity principle.
Subsequent work included the development of a • the internal and external corrosion processes should
mathematical model for forecasting metal loss due to be analysed separately;
corrosion, based on the premise that each operating regime • all defects were instantaneously formed at their first
for each pipeline could be characterized by a modelling environmental exposure;
metal-loss process considering steady relative variability. • a coating degradation time is assumed for external
This could be determined by using either ERP or ILI data, defects;
according to the operational history particulars of each • cathodic protection remains in the steady-state
case. condition during the service life of the pipeline.
Detailed formulae are presented for each of the possible The principle of local corrosion activity
configurations of data sets. The procedures have been
validated and calibrated for short-term applications, The principle postulates that incidences of metal loss
including the prediction of the locations of possible failure located close to each other and on the same side of the pipe
sites, ascertaining rehabilitation needs, and establishing re- wall (either external or internal) will be subjected to similar
inspection intervals as well as maximum operating pressure conditions of corrosion attack. Each defect is associated
profiles. In order to demonstrate the model’s application with a local zone of influence of the corrosion process,
and results, two case studies are briefly presented. 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
Overview of assumptions adjacent metal-loss anomalies, empirically defined by the
vicinity parameter (n). In order to be as representative as
In Part 1 of this paper, the simplifying assumptions for the possible, the following ranges of the control parameters are
long-term model were defined. For the short-term model, recommended: vicinity parameter greater than 25 (n > 25),
the principal difference is that the growth of axial and and segment length average larger than 1km (Li>1000).
longitudinal flaws is disregarded.
Li = H i +n − H i −n (1)
Before introducing the specific aspects of the current work,
for the sake of general understanding, the remaining and As typical corrosion-rate histograms generally present
unmodified simplifying assumptions are summarized below, tailored patterns, one of the major advantages of the
together with the postulated ‘principle of local corrosion application of the local corrosion activity principle is its
activity’. normalizing effect on the population of corrosion rate
data, as demonstrated by the histograms in Fig.1.
Unmodified assumptions
• the defect population for analysis should be defined
based on a dimensional threshold related to the ILI Hot spot considerations
tool’s accuracy; Given that the current model has the primary aim of
• the corrosion process can be characterized by a pipeline rehabilitation, safety measures have been
constant probability density function (pdf) based introduced in order to prevent underestimating the growth
on past process behaviour; of metal loss in the presence of highly-localized corrosion
• the distribution of all data is assumed to follow a conditions. The general logic for this is presented in Fig.2;
Gaussian curve

3. 2nd Quarter, 2009 71
dINSP
j = n +i dj
d Li = ∑ 2n + 1
j =i − n
σ Li = d Li .cv
d i>=perc 0.8dLi. Y dLi = di
N
Fig.2. Logic flowchart for metal-loss >N Loop i
growth under general hot-spot
conditions.
the following additional considerations are also applicable: shows that there are few strong similarities between the
results from the two techniques. In this regard, it is worth
• stray current influence zones: use characteristic noting that ILI represents the overall damage accumulated
lengths (Li) not greater than 100m; during the pipeline’s entire service life, whilst ERP data are
• microbiologically induced corrosion (MIC): usually restricted to a relatively short period. Thus, as ILI
individual corrosion rates greater than their local data have consistently produced larger averages than ERP,
99 percentile must be individually determined, this could be interpreted as evidence of a thriving company
taking into account specific evolution times. These strategy for internal corrosion mitigation. Indeed, further
should be established based on expert judgment, investigation, incorporating historical weight-loss coupon
independently of the pipeline’s service life (%ts). data (out of the scope of this article), has given ample
confirmation of this.
Note that both onshore approach areas subject to tidal
variations (the tide zones on offshore pipelines), and regions Despite the fact that ERP monitoring data are theoretically
around insulating joints (such as on piers and industrial better fitted to reflect the most recent operational
pipelines) could also require special consideration. 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
Relative variability of metal loss conventionally been used as a qualitative indication to
characterize the trend of the process, and not as a quantitative
The short-term forecasting project included a preliminary measurement of the continuity of the pipeline’s corrosion.
study in which ILI and ERP metal-loss rate data from Therefore, in order to produce a consistent profile of the
several different pipelines were evaluated. Using the corrosion rate along the pipeline’s length, ILI data should
Gaussian behaviour premise, these data were characterized be used. However, when significant changes in the system’s
by their expected value (the mean) and their relative operating conditions have taken place, the run-comparison
variability or coefficient of variance, as represented by approach is preferred1.
Equn 2. The results that were obtained are presented in
Tables 1 and 2, for the ILI and ERP data evaluations, Special considerations for ERP
respectively.
ERP data usually contain large amounts of electronic noise,
and therefore a filtering procedure is strongly advised. In
σr the current study, a 3-hr sampling period was averaged for
cv = (2)
R
1 If this approach is not feasible, a proportionality study can be made
A comparison of the values of average corrosion rates based on available data from ERP or coupons.

4. 72 The Journal of Pipeline Engineering
(mm/year) CV
EPR1 0.000 4 0.11 0
EPR2 0.051 6 0.3 7 8
EPR3 0.001 6 0.0 2 5
EPR4 0.000 3 0.1 8 8
EPR5 0.027 6 0.0 4 2
EPR6 0.000 4 0.1 9 3
EPR7 0.001 3 0.8 7 9
EPR8 0.004 3 0.2 2 2
EPR9 0.050 6 0.0 1 2
E P R 10 0.011 9 0.02 4
E P R 11 0.000 3 0.2 6 9
mea n 0.014 0.2 13
Table 1. EPR data evaluation results.
Local rate L o c al c v Service li fe
I L I1 0.06 5 0.19 8 27
I L I2 0.08 7 0.28 0 19
I L I3 0.0 1 4 0.91 0 23
I L I4 0.08 0 0.13 0 33
I L I5 0.04 5 0.23 0 32
I L I6 0.04 9 0.04 3 42
I L I8 0.09 3 0.28 0 31
I L I9 0.07 8 0.19 0 31
mea n 0.0 64 0.283
Table 2. ILI data evaluation results.
the daily value and the corrosion rate was obtained using a Furthermore, data-acquisition periods must be
five-point algorithm that minimizes the effect of the noise representative of the pipeline’s future operational service
on the numerical derivative. Figures 3a and 3b illustrate conditions2.
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 Framework for single runs
in the operational regime is illustrated by a shift in the slope
of the trend line. According to the principle of local corrosion activity, each
defect will have an associated population, defined as being
It is worth noting, for instance, that when the flow regime the (n) – the vicinity parameter – defects immediately up-
is expected to present very low corrosivity conditions, the and downstream. The defect-analysis population will have
use of ERP data should be avoided, given that – under such
conditions – it could became difficult to differentiate
2 When seasonal operational changes are expected, greater acquisition
between electronic noise and a real sensor response. periods are recommended.

5. 2nd Quarter, 2009 73
0,0328
0,0327
0,0326
mm
0,0325
0,0324
0,0323
0,0322
0 500 1000 1500 2000 2500 3000 3500
h
0,0379
0,0378
0,0377
mm
0,0376
0,0375
0,0374
Fig.3. Examples of ERP-acquired 0,0373
data: (a – top) standard case under 0 500 1000 1500 2000 2500 3000 3500
steady corrosive attack (trend line in h
red); (b – bottom) after a change in
the pipeline operating conditions.
its local corrosion rates, defined as random variables, with original defect depth added to the metal loss which should
their average established by Equns 3aa and 3ab – respectively be expected within the time period under consideration
- for the internal or external anomalies being considered. (Equn 4a). The associated dispersion of future defect
The associated standard deviation values are defined by depths should take account of tool measurement error on
Equn 3b. As previously discussed, the coefficient of variance ILI-measured depths as well as the expected deviation on
(cv) values should be determined based on ILI or ERP data, the overall metal-loss rate over the period of time considered,
depending on which is the most appropriate for representing as shown in Equn 4b.
the future anticipated short-term corrosion process.
dfi = di + RLi .∆tf (4a)
d
RLi = i (3)
∆ts
2
E 
σ fi = ∆tf (σ Li )
2
+ t  (4b)
j =n +i
 c 
∑d
j =i − n
j
RLi = (3a-a)
(2n + 1).∆ts
Damage tolerance
j =n +i
∑d j
Several metal-loss defect-assessment criteria can be used to
RLi =
j =i − n
(3a-b) determine damage tolerance. In each case, the analyst
(2n + 1).∆ts − ∆tc 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.
σ Li = RLi .cv (3b)
Pif = f (df , li ,wi ) (5)
Future defect depth
Once defect corrosion rates have been determined, the 3 The maximum allowable pressure profile can be determined based on
future defect depth can then be defined as being the hydraulic simulation of worst-case operational scenarios. Otherwise, it
can be assumed to be constant.

6. 74 The Journal of Pipeline Engineering
Fig.4. Plot of the future probabilistic
failure pressure of a defect versus its
deterministic MAOP.
Defect relativity acceptance reported by internal inspection. The single-run modelling
procedure was used to forecast the acceptability of each
The failure pressure associated with a defect’s future depth defective region, considering both ILI and ERP cvs.
(Pif) should not be exceeded by the maximum operating Additionally, in order to provide a reference, ERF was also
pressure expected at the defect’s location (MAOPi)3. This determined using a traditional deterministic approach.
failure pressure is represented by the limit-state function
shown in Equn 6, where Pif is characterized by a normal Figure 5 presents the results obtained for the 200 worst
distribution, while the MAOPi is a deterministic value; in pipeline anomalies: blue and red dots representing single-
other words, the probability of the pipeline exceeding the run model results for ILI and ERP cv, respectively, and the
limit-state condition at each defect (POEi) can be determined green indicating ERFs settled on deterministically. In the
as the area on the left-hand side of the maximum allowable figure, the results of the first two procedures present a
operating pressure under the Pif probability density function remarkable match, demonstrating the model’s overall
(pdf), as shown in Fig.4. robustness. They also provide a clear distinction of defect
impact on pipeline reliability, easily permitting their
MAOPi − Pif < 0 (6) categorization by risk. The deterministic approach results,
on the other hand, show only a very slight variation among
The widely-known Pipeline Operator’s Forum concept of the defects that are considered, concealing their true
‘estimated repair factor’ (ERF) has been adapted to the operational risk.
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.
Framework for run comparisons
When two sets of ILI data are available, and an estimate of
POEi the corrosion rates based on the operational period between
ERFi = (7)
APFi 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:
The single run procedure: • Tool technologies: must be the same or similar,
a case study otherwise comparison of the raw signals is necessary.
• Tool accuracy: both inspections should have been
A 100-km long trunk line with a constant 22in diameter performed using tools of a similar accuracy.
and 6.35mm wall thickness (referred to in Part 1 as Pipeline
3) was chosen to demonstrate the single-run model. The • Run performance: both runs must have been
pipeline has recently been rehabilitated to meet a flow- successfully completed.
capacity expansion, and hydraulic simulation was used to
define its new maximum operating pressure profile. Pipeline • Data alignment: independent of the segmentation
degradation had principally been caused by internal strategy adopted, the quality of the data alignment
corrosion, and the accumulated channelling damage is could have a considerable impact on the results.
extensive. ERP data were available.
The pipeline’s future integrity condition was ascertained 4 The proposed run-comparison procedure should preferentially use
considering a five-year metal-loss growth of the anomalies non-clustered data.

7. 2nd Quarter, 2009 75
5
4
3 a
ERF
b
2 c
1
0
0 20 40 60 80 100 120 140 160 180 200
worst anomalies
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.
Segmentation strategy
d 2 − d1
Rrc = (8a)
A common procedure when dealing with run comparisons ∆ti
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 σ rc = Rrc .cv (8b)
could be based on distinctive features affecting the corrosion
process that takes place along the pipeline, such as stray A broad outline of the run-comparison logic is shown in the
current influence zones, changes of flow regime, etc. flowchart in Fig.6. Future defect geometry and acceptability
can be determined, as has been discussed above5.
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 The run comparison procedure:
defects with similar characteristics. In this case, the division
criteria should be determined based on statistical analysis a case study
and expert judgment. Some examples of such a procedure
are: A 2.5-km long subsea oil pipeline section of constant 34in
diameter, with wall thicknesses ranging from 0.375 to
• Cathodic protection effectiveness: within a specific 0.5in, and with a service life of 35 years, was chosen to
distance from the pipeline rectifiers or anode beds. demonstrate the run-comparison model. No ERP data
were available. The two last ILIs were performed using MFL
• ROW topography (water accumulation at low tools, with an interval of seven years. Several internal
points). corrosion-mitigation actions have been implemented over
the last decade. Only the internal metal-loss anomaly
• Coating effectiveness (field/plant applied coatings) population has been assessed.
Mathematical formulae Figure 7 depicts local depth histograms of the internal
metal-loss anomalies, considering the population reported
After having been defined, inspection data sub-populations by the two most-recent ILI inspections; by comparing them,
must be paired with those from the preceding inspection, one can clearly note the growth in overall metal loss. The
and both should then have their average depths determined. corrosion rate has been generically defined for the whole
The metal-loss growth rate between these inspections can segment, according to the formulae presented in the previous
be inferred based on the average depth differences. In the section and also, individually, as stated by the proposed
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 5 Application of the current procedure is not recommended when the
value is based on the most recent inspection or ERP data. relative variability of the metal-loss rate is greater than unity.

8. 76 The Journal of Pipeline Engineering
INSP1 INSP2
Y Y
Segmetation Segmetation
Criterion Criterion
INSP1A INSP2A
N N
Loop j Loop j >N2
d1A d2A
>N1
<N1
<N2
RrcA = (d2A – d1A)∆ti
σrcA = RA.cv
INSP1B INSP2B
d1B d2B
RrcB = (d2B – d1B)∆ti
σrcB = RB.cv
Fig.6. Flowchart for determining the corrosion rate by run comparison on a pipeline by splitting its defect population into
two sub-groups.
0,25
2,7 INSP1
0,20
3,2 INSP2
0,15
f(x)
0,10
Fig.7. Histogram of
0,05
local metal-loss
average depths
0,00
from the run-
2,00 2,25 2,50 2,75 3,00 3,25 3,50 3,75 4,00 4,25
comparison case
d [mm] study inspections 1
and 2.
single-run methodology for both inspections. The results of the run-comparison procedure has reduced the
from these procedures demonstrate that the mitigation rehabilitation scope by more than 70%.
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 Conclusions
as forecast by the run-comparison and single-run procedures
(using as input to the latter the data from the most-recent In recent years growing quantities of pipeline metal-loss
inspection). The resulting acceptability condition for the data derived from ILI and ERP monitoring are becoming
200 worst anomalies is displayed, as ERFs, in Fig.8. The use available worldwide. Both represent a considerable body of

9. 2nd Quarter, 2009 77
4
3
single run procedure
run comparison procedure
ERF
2
1
0
0 25 50 75 100 125 150 175 200
worst anomalies
Fig.8. Five-year ERF of the 200 worst internal metal anomalies determined by single run and run comparison procedures.
evidence regarding past behaviour of the corrosion process, not require special skills. Its application is simple, only
but there is a lack of industrial guidelines regarding their requiring expert judgment in order to define its validity in
use in corrosion-rate estimation. non-standard cases and for interpretation of general results.
This paper introduces a simple approach for accomplishing It is worth noting that the entire study was carried out, and
short-term metal-loss forecasting through the use of ILI consequently consistently calibrated, using downstream
data, where necessary juxtaposed with available ERP data. pipeline system data. Thus, it is strongly recommended that
The project also considers long-term forecast modelling, a validation analysis of the proposed values of the model’s
which was presented in the first part of this work. As the empirical parameters is established for upstream
latter was aimed at remaining-life estimation, the current applications.
work has been mainly directed towards the prediction of
rehabilitation needs and the definition of re-inspection
intervals. Acknowledgments
The project was undertaken based on two innovative The authors would like to thank Petrobras Transporte S.A.
principles: local corrosion activity, and the steady relative for permission to publish this paper, and their colleagues
variability in metal-loss growth under typical pipeline Carlos Alexandre Martins and João Hipólito de Lima
operational conditions. The work included the development Oliver for many contributions and enlightening discussions.
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 Nomenclature
is necessary to reflect the most recent operational
circumstances. Tr: standard deviation on a population of
corrosion rate values [mm]
The single ILI modelling procedure can incorporate special Tfi: forecast defect depth standard deviation
considerations to avoid underestimation of the metal-loss [mm]
growth rate at hot-spot sites. Also, the proposed strategy for TLi: local corrosion rate standard deviation
dividing the pipeline defect population into sub-groups for [mm/year]
run-comparison purposes could considerably enhance the Trc: standard deviation of corrosion growth rate
result’s significance. produced by run comparison [mm/year]
%ti: re-inspection interval [years]
Implementation of the model is straightforward and does %tc: coating degradation lag [years]

10. 78 The Journal of Pipeline Engineering
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