IPC_2010

Proceedings of the International Pipeline Conference
IPC 2010
September 27 – 1 October, 2010, Calgary, Alberta, Canada

IPC2010-31576

ESTIMATION OF CORROSION RATES BY RUN COMPARISON: A STOCHASTIC
SCORING METHODOLOGY

Érika S. M. Nicoletti Ricardo D. de Souza
Petrobras Transporte S.A Petrobras Transporte S.A
Rio de Janeiro, RJ, Brazil Rio de Janeiro, RJ, Brazil

ABSTRACT

Pipeline operators used to map and quantify corrosion
damage along their aging pipeline systems by carrying out NOMENCLATURE
periodical in-line metal-loss inspections. Comparison with the
data sets from subsequent runs of such inspections is one of the • σi: standard deviation of depth metal-loss population
most reliable techniques to infer representative corrosion in a defect neighborhood [mm];
growth rates throughout the pipeline length, within the period • σli: standard deviation of depth metal-loss population
between two inspections. Presently there are two distinct in a defect neighborhood [mm];
approaches to infer corrosion rates based on multiple in-line • ∆tf: forecasting time interval [years];
inspections: individual comparison of the detected defective • ∆ti: time interval between inspections [years];
areas (quantified by more than one inspection), and comparison • d1: earlier inspection metal-loss depth average [mm];
between populations. The former usually requires a laborious • d2: latest inspection metal-loss depth average [mm];
matching process between the run-data sets, while the drawback • dfi: forecast defect depth [mm];
of the latter is that it often fails to notice hot-spot areas. The
• di: pig-reported defect depth [mm];
object of this work is to present a new methodology which
• dj: pig-reported defect depth [mm];
allows quick data comparison of two runs, while still
• dli: metal-loss depth local average [mm];
maintaining local distinct characteristics of the corrosion
process severity. There are three procedures that must be • Et: tool measurement error [mm];
performed. Firstly, ILI metal-loss data set should be submitted • Fsi: defect scoring factor for corrosion activity at defect
to a filtering/adjustment process, taking into consideration the vicinity;
reporting threshold consistency; the possible existence of • Hi: defect odometer [m];
systematic bias and corrosion mechanisms similarity. Secondly, • N: Population defects total number;
the average metal-loss growth rate between inspections should • N1: Population 1 defects total number;
be determined based on the filtered populations. Thirdly, the • N2: Population 2 defects total number;
defects reported by the latest inspection should have their • n: vicinity parameter;
corrosion growth rates individually determined as a function of • Rrc: corrosion growth rate determined by run
the mean depth values of the whole population and in the defect comparison [mm/year].
neighborhood. The methodology allows quick and realistic
damage-progression estimates, endeavoring to achieve more
cost-effective and reliable strategies for the integrity
management of aged corroded systems. Model robustness and
general feasibility is demonstrated in a real case study.

1 Copyright © 2010 by ASME

INTRODUCTION states that a metal-loss defect could have its growth rate
determined as a function of the average metal-loss damage in
In the past few decades, many of the most catastrophic
the adjoined region.
pipeline industry accidents have had as their root failure the
mechanism diagnosed as corrosion. Presently, operators are The concept of adjoined region or neighborhood (Lni)
able to rely upon several codes, standards and consolidated is expressed by the vicinity parameter (n) and is characteristic
practices to assess the remaining strength in quantified to each location of each pipeline, being dependent on the defect
corrosion metal-loss areas, ranging from the most simple and population overall number as well as its local distribution. The
straightforward, such as ASME B-31G, to those more refined vicinity parameter of a pipeline defect population should be
and time demanding, such as Finite Element analysis. determined in order to comply with the requirements expressed
Additionally, the market is now offering in-line inspection (ILI) by Equations (1) and (2) below:
technologies able to map and quantify, with reasonable
accuracy, metal-loss damage distribution along pipelines, and n >= 3 (1)
the choice has grown considerably.
i = N −n
However, the definition of a proper pipeline integrity ∑ i = n +1
( H i + n − H i −n )
0.1 ≤ ≤ 10 (2)
management strategy also requires damage progression N − 2(n − 1)
forecasting. Indeed, growth rate estimation plays a fundamental
role in the optimization of the re-inspection intervals and
maintenance rehabilitation scope, and therefore has a direct
effect on pipeline maintenance cost-effectiveness and Simplistic assumptions1
operational reliability and safety. • linear damage progression within the considered time
intervals (between inspections and forecast period);
The data-set comparison of subsequent ILI could • all external corrosion active sites are associated with coat
provide solid ground for such estimates. Typically, corrosion damage areas where the coat protection effectiveness is
rates are estimated by individual defect matching or by assumed to be null;
population comparison. The results achieved by the former • new defect generation as well as stop growth rate is not
could be quite accurate, especially when raw signal evaluation considered.
takes place, but the process applicability is rather limited, due to
its characteristic of being time-demanding, while also requiring
special computational tools and skilled people. On the other CONSTRUCTING REPRESENTATIVE POPULATIONS
hand, ordinary procedures to perform run comparison by taking
into account entire populations could be quickly carried out, but Regarding the construction of the populations to be
usually give rise to large errors in the resulting estimate. assessed, special consideration should be given to tool
measurement error significance, in order to guarantee relative
This paper aims at presenting a straightforward scoring significance of the ILI reported threshold. As a general rule, it is
methodology which considerably enhances corrosion rate recommendable that depth values which do not comply with
outputs from ILI population comparison. The work was Equation (3) below should be dismissed.
developed based on the Principle of Local Corrosion Activity
[1, 2] to take into account the neighborhood metal-loss 2 Et (3)
characteristics of each defect individually. Methodology di ≥
formulation could easily be put into practice on any commercial 1.28
spreadsheet with basic statistic functions. A case study is
presented in order to demonstrate that, despite its simplicity, the Also, data population to be compared must have been
model’s results are quite robust and realistic. collected by ILI tools of similar performance (technology,
accuracy, peculiarities related to each run such as, line cleaning
MODEL BACKGROUND and flow regime) and they should both have been properly
validated by field measurements. Possible bias should be
The difficulty in accurately measuring and predicting identified and corrected. Depending on the adopted
time dependent behavior of the parameters, which control the segmentation strategy, quality of data alignment could have a
corrosion process in large structures such as pipelines, make it significant impact on model outputs.
hard to accomplish effective forecasting of the metal-loss
progression rates under operational conditions, by means of
mechanistic approaches. A brief outline of segmentation strategy: the only mandatory
segmentation procedure is the obvious partition of defects
Stochastic modeling based on ILI data is better fitted
to accommodate the inherent randomness of corrosion in
1
pipelines. Accordingly, the principle of local corrosion activity Further details in references [1] and [2].


located on the outside of the pipeline from those on the inside. Moreover, population segmentation could be also especially
Common segmentation based on axial distance is not recommendable when coating material changes are involved
particularly required, given the account of local corrosion (different degradation lags to be considered).
activity. But, when it is previously known that different
mechanisms are acting concomitantly and their attack severity is
expected to be highly dissimilar, further population
segmentation could be attempted, especially when those could
give rise to distinct circumferential distributions.

Inspection 1 Inspection 2
Reported Data Reported Data
Filtering

2 Et
di ≥
1.28
Assessment of
Population Significance

Correction of Measurement Bias

Data Alignment

Segmentation Strategy

Population 1 Population 2

d 1
d2

d 2 − d1
Rrc =
∆ti

Figure 1: Flowchart of the primary run comparison.


2. MATHEMATICAL FRAMEWORK

1. Two sets of ILI reported depth values; named Population 1 Each individual depth measurement for Population 2 should be
and Population 2 (referred as former and latter inspections, associated to a characteristic local depth Gaussian distribution,
respectively), should be “fitted” for analysis. The “fitting” as defined by Equation [5a] and [5b].

(∑ )
procedure should incorporate data consistency checking,
j =i + n
measurement bias assessment and correction, besides possible dj
j =i − n (5a)
segmentation strategy (which could require data alignment). d li =
The average corrosion growth in the time interval between (2n + 1)
inspections ( ∆ti) should be determined as the ratio of the
difference between the population’s arithmetic mean and ∆ti as
expressed by Equation (4).
σ li =
(∑ j =i + n
j =i − n
(d j − d li )
2
(5b)
d 2 − d1 (n − 1)
Rrc = (4)
∆ti

Population 2

=
(∑ j =i + n
j =i − n
dj ) (∑ j =i + n
j =i − n
(d j − d li )
2

dli σ li =
(2n + 1) (n − 1)

Y d2 − di
d i ≥ d Li + 1.75σ Li Fsi = 1 +
d2

N

d 2 − d li
Fsi = 1 +
d2

d fi = d i + Fsi Rrc ∆t f

Forecasting of Population 2
evolving for ∆t f years

FIGURE 2: Flowchart of the process to forecast defects future population.


3. Subsequently, a corrosion activity coefficient (Fsi) should also
be individually calculated by Equation (6a), in order to express Table 1 gives some additional details regarding the internal
the region’s corrosion activity potential. defect populations reported in the 2007 and 2009 inspections,
as well as their respective arithmetic means. Also, Figure 3
d 2 − d li presents the axial distribution of defects with reported depths
Fsi = 1 + (6a)
d2 over 40%.

d 2 − di Procedure: as a consequence of the considerable difference
Fsi = 1 + (6b) between inspection reporting thresholds, the population of
d2 internal defects reported by the 2009 inspection (N2) was almost
a third of the 2007 (N1) inspection. In order to deal with this
3a. Hot Spot Conditional Structure. The underestimation in hot problem the following procedure was adopted:
spot regions should be prevented by modifying equation (6a)
into (6b), when the conditional structure stated in 1. As a starting point, it was assumed that the N2 deepest
Equation (7) is fulfilled: defects reported by the 2007 inspection were the same N2
reported in 2009, those defects being labeled as Population
d i ≥ d li + 1.75σ Li 1’;
(7)
2. A first approximation of the average corrosion rate in the
4. Finally, future defect depth should be determined by period between the considered inspections (R’rc) was
Equation (8). determined by Equation (4), considering the Populations 2
and 1’;
d fi = d i + Fsi Rrc ∆t f (8)
3. All defects originally reported by the 2007 inspection
(Population 1o) have had their future geometry forecast
A broad outline of run comparison logic is depicted in the according to the procedure detailed in the flowchart of
flowchart presented in Figure 2. Figure 2;
4. Population 1 was then defined as being the N2 defects
which presented the deepest forecast depths for 2009;
CASE STUDY 5. A new Rrc value was calculated by Eq. (4), considering
Populations 1 and 2;
Background: The methodology described was applied to 6. Finally, Population 2 growth was forecast for a time
estimate the corrosion rate distribution in a 3.5 km segment of interval of 3 years, resulting in the data set, labeled as
an oil production line with 16” diameter, 9.5 mm thickness, Population 3.
manufactured in API 5L X65. The pipeline had been used in the
interconnection of onshore production fields of depleted Results: Figure 4 compares the defect depth histograms of
reservoirs and the BSW content of the fluid conveyed ranged 2007 and 2009 ILI measurements (Populations 1 and 2,
from 80-90%. In late 2007 and late 2009, the line underwent respectively) with 2012 predicted population (Population 3).
MFL ILI surveys. Both tool accuracy and performance were The steadiness of damage progression becomes much more
fairly similar but the reporting threshold was 10% in 2007, evident when those populations are normalized by the Local
while in the 2009 inspection, only depths above 35% were Activity Principle as could be noted in Figure 5.
reported. No attempt to identify and correct measurement bias Model Robustness: In order to assess the model output fitness,
has been carried out. Only the anomalies located in the internal Population 1 (2007) evolution was projected towards 2009. The
pipeline surface have been considered in the analysis. histogram of these predicted depths is compared with the
histogram of 2009 ILI reported results in Figure 6. The match
TABLE 1: Defect depth populations. between them demonstrates the model robustness and its
suitability for common pipeline fitness-for-purpose evaluations.
POPULATION

THRESHOLD
INSPECTION

NUMBER OF

AVERAGE
DEFECTS

DEPTH

The slight tendency to overestimate the frequencies of higher
% wt

% wt

depths can almost be overcome by dismissing the hot spot
conditional structure in the algorithm.
1 12,833 32% 38.11
2007 1o 33,833 10% ---
2009 2 12,833 35% 40.29


70

65
defect depth, wt%

60

55

50

45

40

35
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
axial distance, km

FIGURE 3: Defects axial distribution in 2009.

3500
2007 ILI
3000
2009 ILI
2500 2012 PROJECTION
defect's number

2000

1500

1000

500

0
31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
defect depth, w t%

FIGURE 4: Histogram of defect depth populations.

4500 2007 ILI 2009 ILI 2012 PROJECTION
4000
3500
defect's number

3000
2500
2000
1500
1000
500
0
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
local defect depth, wt%

FIGURE 5: Histograms of local defect depth.


3500

3000 measured
projected
2500
defect's number
2000

1500

1000

500

0
35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67
defect depth, w t%

FIGURE 6: 2009 Histogram of defects depth.

3. Nicoletti, E.S.M.; de Souza, R. D.A; Olivier, J.H.L.
CONCLUSION
External Corrosion Growth on an Ageing Pipeline: a Case
Study. INTERCORR 2010. Fortaleza, 2010.
A stochastic scoring methodology to estimate corrosion growth
by ILI run comparison is presented. The model could easily be 4. Nicoletti, E.S.M.; de Souza, R. D.A; Olivier, J.H.L. Metal
implemented in any mathematic commercial package and loss Growth Rate Distributions: A Case Study on Ageing
allows quick and reasonably accurate projections of the defect- Transmission System. Rio Pipeline Conference 2009. Rio
depth population evolution throughout time. de Janeiro, 2010.

Output reliability, however, is highly dependent on data
consistency as much as the significance of the comparison
procedure, and so the differences in the tool technologies,
performances and reporting thresholds should be carefully
evaluated and assessed. Also, it is assumed that significant tool
measurement bias had been previously identified and properly
treated.

ACKNOWLEDGMENTS

The authors would like to thank PETROBRAS Transporte
S.A. for permission to publish this paper, and their colleagues
Dr. Sérgio Cunha and João Hipólito de Lima Oliver for many
contributions and enlightening discussions.

REFERENCES

1. Nicoletti, E.S.M.; de Souza, R. D. A Practical Approach in
Pipeline Corrosion Modeling: Part 1 – Long Term Integrity
Forecasting. Journal of Pipeline Engineering. Vol. 8, no. 1.
March 2009.
2. Nicoletti, E.S.M.; de Souza, R. D. A Practical Approach in
Pipeline Corrosion Modeling: Part 2 – Short Term Integrity
Forecasting. Journal of Pipeline Engineering. Vol. 8, no. 2.
June 2009. (Correction Note Published in Vol. 8, no. 3.
June 2010).


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