Jpe Part2


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

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Jpe Part2

  1. 1. 2nd Quarter, 2009 69A practical approach to pipeline corrosion modelling: Part 2 – Short-term integrity forecastingby Dr Érika S M Nicoletti*, Ricardo Dias de Souza, and Dr Sérgio da Cunha BarrosPetrobras 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 andmonitoring, 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 approachparticular locations as they vary with time, the former maps for inferring corrosion growth rates from the availablethe accumulated damage due to corrosion, along the whole collected inspection and monitoring data. The overallpipeline length, at a single moment in time. Both techniques objective was to determine the pipeline’s short-termcan 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-resistanceexpected 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) atel: +55 21 3211 7264 suitable parameter for representing the pipeline corrosionemail: processes.
  2. 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 shouldmathematical 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 firstfor each pipeline could be characterized by a modelling environmental exposure;metal-loss process considering steady relative variability. • a coating degradation time is assumed for externalThis 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-statecase. condition during the service life of the pipeline.Detailed formulae are presented for each of the possible The principle of local corrosion activityconfigurations of data sets. The procedures have beenvalidated and calibrated for short-term applications, The principle postulates that incidences of metal lossincluding the prediction of the locations of possible failure located close to each other and on the same side of the pipesites, ascertaining rehabilitation needs, and establishing re- wall (either external or internal) will be subjected to similarinspection intervals as well as maximum operating pressure conditions of corrosion attack. Each defect is associatedprofiles. 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 ofOverview of assumptions adjacent metal-loss anomalies, empirically defined by the vicinity parameter (n). In order to be as representative asIn Part 1 of this paper, the simplifying assumptions for the possible, the following ranges of the control parameters arelong-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 presentunmodified simplifying assumptions are summarized below, tailored patterns, one of the major advantages of thetogether with the postulated ‘principle of local corrosion application of the local corrosion activity principle is itsactivity’. 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. 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 NFig.2. Logic flowchart for metal-loss >N Loop igrowth under general hot-spotconditions.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 tidalvariations (the tide zones on offshore pipelines), and regions Despite the fact that ERP monitoring data are theoreticallyaround insulating joints (such as on piers and industrial better fitted to reflect the most recent operationalpipelines) 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 haveRelative variability of metal loss conventionally been used as a qualitative indication to characterize the trend of the process, and not as a quantitativeThe short-term forecasting project included a preliminary measurement of the continuity of the pipeline’s in which ILI and ERP metal-loss rate data from Therefore, in order to produce a consistent profile of theseveral different pipelines were evaluated. Using the corrosion rate along the pipeline’s length, ILI data shouldGaussian behaviour premise, these data were characterized be used. However, when significant changes in the system’sby their expected value (the mean) and their relative operating conditions have taken place, the run-comparisonvariability or coefficient of variance, as represented by approach is preferred1.Equn 2. The results that were obtained are presented inTables 1 and 2, for the ILI and ERP data evaluations, Special considerations for ERPrespectively. 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 madeA comparison of the values of average corrosion rates based on available data from ERP or coupons.
  4. 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 feI L I1 0.06 5 0.19 8 27I L I2 0.08 7 0.28 0 19I L I3 0.0 1 4 0.91 0 23I L I4 0.08 0 0.13 0 33I L I5 0.04 5 0.23 0 32I L I6 0.04 9 0.04 3 42I L I8 0.09 3 0.28 0 31I 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 befive-point algorithm that minimizes the effect of the noise representative of the pipeline’s future operational serviceon the numerical derivative. Figures 3a and 3b illustrate conditions2.this procedure: firstly in a standard situation, where theslope of the trend line corresponds to the local EPR-measured metal-loss growth; and secondly, where a change Framework for single runsin the operational regime is illustrated by a shift in the slopeof the trend line. According to the principle of local corrosion activity, each defect will have an associated population, defined as beingIt 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 haveuse of ERP data should be avoided, given that – under suchconditions – it could became difficult to differentiate 2 When seasonal operational changes are expected, greater acquisitionbetween electronic noise and a real sensor response. periods are recommended.
  5. 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 0,0374Fig.3. Examples of ERP-acquired 0,0373data: (a – top) standard case under 0 500 1000 1500 2000 2500 3000 3500steady corrosive attack (trend line in hred); (b – bottom) after a change inthe pipeline operating conditions.its local corrosion rates, defined as random variables, with original defect depth added to the metal loss which shouldtheir 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 defectThe associated standard deviation values are defined by depths should take account of tool measurement error onEqun 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 depthOnce defect corrosion rates have been determined, the 3 The maximum allowable pressure profile can be determined based onfuture 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. 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 eachThe 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 alsopressure expected at the defect’s location (MAOPi)3. This determined using a traditional deterministic approach.failure pressure is represented by the limit-state functionshown in Equn 6, where Pif is characterized by a normal Figure 5 presents the results obtained for the 200 worstdistribution, 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 thelimit-state condition at each defect (POEi) can be determined green indicating ERFs settled on deterministically. In theas the area on the left-hand side of the maximum allowable figure, the results of the first two procedures present aoperating 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 amongThe 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 operationalacceptability determined by Equn 7, where APF is allowableprobability of failure at each defect location, which shouldbe 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 beenA 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 Pipeline3) was chosen to demonstrate the single-run model. The • Run performance: both runs must have beenpipeline has recently been rehabilitated to meet a flow- successfully completed.capacity expansion, and hydraulic simulation was used todefine its new maximum operating pressure profile. Pipeline • Data alignment: independent of the segmentationdegradation had principally been caused by internal strategy adopted, the quality of the data alignmentcorrosion, 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 useconsidering a five-year metal-loss growth of the anomalies non-clustered data.
  7. 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 ∆tiis to divide the pipeline into a number of sections;traditionally, this is on the basis of constant length (e.g. 1or 10km), or zones of similar characteristics. The latter σ rc = Rrc .cv (8b)could be based on distinctive features affecting the corrosionprocess that takes place along the pipeline, such as stray A broad outline of the run-comparison logic is shown in thecurrent 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 populationsegmentation process can also be adopted in which theglobal population is separated into sub-groups which contain The run comparison procedure:defects with similar characteristics. In this case, the divisioncriteria should be determined based on statistical analysis a case studyand expert judgment. Some examples of such a procedureare: 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 reportedAfter 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. Theand both should then have their average depths determined. corrosion rate has been generically defined for the wholeThe metal-loss growth rate between these inspections can segment, according to the formulae presented in the previousbe inferred based on the average depth differences. In the section and also, individually, as stated by the proposedcurrent work, the corrosion rate was assumed to berepresented by a Gaussian distribution, and can bedetermined based on Equns 8a and 8b, in which the cv 5 Application of the current procedure is not recommended when thevalue is based on the most recent inspection or ERP data. relative variability of the metal-loss rate is greater than unity.
  8. 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 = INSP1B INSP2B d1B d2B RrcB = (d2B – d1B)∆ti σrcB = RB.cvFig.6. Flowchart for determining the corrosion rate by run comparison on a pipeline by splitting its defect population intotwo 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 thefrom 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 intoaccount a time-interval of five years and defect geometries Conclusionsas 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-lossinspection). The resulting acceptability condition for the data derived from ILI and ERP monitoring are becoming200 worst anomalies is displayed, as ERFs, in Fig.8. The use available worldwide. Both represent a considerable body of
  9. 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, onlybut there is a lack of industrial guidelines regarding their requiring expert judgment in order to define its validity inuse 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, andshort-term metal-loss forecasting through the use of ILI consequently consistently calibrated, using downstreamdata, where necessary juxtaposed with available ERP data. pipeline system data. Thus, it is strongly recommended thatThe project also considers long-term forecast modelling, a validation analysis of the proposed values of the model’swhich was presented in the first part of this work. As the empirical parameters is established for upstreamlatter was aimed at remaining-life estimation, the current has been mainly directed towards the prediction ofrehabilitation needs and the definition of re-inspectionintervals. AcknowledgmentsThe 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 colleaguesvariability in metal-loss growth under typical pipeline Carlos Alexandre Martins and João Hipólito de Limaoperational conditions. The work included the development Oliver for many contributions and enlightening discussions.of an independent mathematical framework suitable fordifferent input data sets, which include data from a singleILI run, and comparison of data between two ILI runs.Available ERP data can be incorporated into both when it Nomenclatureis necessary to reflect the most recent operationalcircumstances. Tr: standard deviation on a population of corrosion rate values [mm]The single ILI modelling procedure can incorporate special Tfi: forecast defect depth standard deviationconsiderations to avoid underestimation of the metal-loss [mm]growth rate at hot-spot sites. Also, the proposed strategy for TLi: local corrosion rate standard deviationdividing the pipeline defect population into sub-groups for [mm/year]run-comparison purposes could considerably enhance the Trc: standard deviation of corrosion growth rateresult’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. 10. 78 The Journal of Pipeline Engineering %tf: forecasting lag [years] 3. R.Bea et al., 2003. Reliability based fitness-for-service %ts: pipeline service life [years] assessment of corrosion defects using different burst pressure APFi: allowable probability of failure predictors and different inspection techniques. 22nd c: confidence level Gaussian adjustment International Conference on Onshore Mechanics and Arctic Engineering, June 8-13, Cancun. parameter 4. J.M.Race, S.J.Dawson, L.Stanley, and S.Kariyawasam, 2006. cv: coefficient of variance of corrosion rate Predicting corrosion rates for onshore oil and gas pipelines. population International Pipeline Conference, Calgary. d1: previous inspection (INSP1) metal-loss depth 5. Ahammed, 1998. Probabilistic estimation of remaining life average [mm] of a pipeline in the presence of active corrosion defects. d1A/B: metal-loss depth average of a INSP1 sub- Int.J.Pressure Vessels and Piping, 75, pp 321-329. population [mm] 6. A.Valor a, F.Caleyo, L.Alfonso, D.Rivas, and J.M.Hallen, d2: newest inspection (INSP2) metal-loss depth 2007. Stochastic modeling of pitting corrosion: a new model average [mm] for initiation and growth of multiple corrosion pits. Corrosion Science, 49, pp 559–579. d2A/B: metal-loss depth average of a INSP1 sub- 7. A.Ainouche, 2006. Future integrity management strategy of population [mm] a gas pipeline using Bayesian risk analysis. 23rd World Gas dfi: defect future depth [mm] Conference, Amsterdam. di: individual metal-loss depth [mm] 8. P.J.Laycock and P.A.Scarf, 1989. Exceedances, extremes, dj: individual metal-loss depth [mm] extrapolation and order statistics for pits, pitting and other dINSP: defect depth population reported by ILI localized corrosion phenomena. Corrosion Science, 35, 1-4, pp [mm] 135-145, 193. dLi: the local average for a defect metal-loss depth 9. J.L.Alamilla and E.Sosa, 2008. Stochastic modelling of [mm] corrosion damage propagation in active sites from field inspection data. Corrosion Science, 50, pp 1811–1819. ERFi: estimated repair factor for defect future 10. J.L.Alamilla, D.De Leon, and O.Flores, 2005. Reliability geometry based integrity assessment of steel pipelines under corrosion. E t: tool measurement error [mm] Corrosion Engineering, Science and Technology, 40, 1. Hi : defect odometer [m] 11. S.A.Timashev, 2003. Updating pipeline remaining life INSP1: defect depth population reported by the first through in-line inspection. International Pipeline Pigging ILI Conference, Houston. INSP1A/B: 12. S.A.Timashev et al., 2008. Markov description of corrosion INSP1 sub-population defect growth and its application to reliability based inspection INSP2: defect depth population reported by the and maintenance of pipelines. 7th International Pipeline Conference, Calgary. second ILI 13. G.Desjardins, 2002. Optimized pipeline repair and inspection INSP2A/B: planning using in-line inspection data. Pipeline Pigging, INSP2 sub population Integrity Assessment & Repair Conference, Houston. li: defect length [mm] 14. B.Gu, R.Kania, S.Sharma, and M.Gao, 2002. Approach to Li : local segment length [m] assessment of corrosion growth in pipelines. 4th International N: analysis defect population Pipeline Conference, Calgary. n: vicinity parameter 15. G.Desjardins, 2001. Predicting corrosion rates and future Pif: defect forecast failure pressure [kg/cm2] corrosion severity from in-line inspection data. Materials POEi: defect probability of exceedance in the limit- Performance, August, 40, 8. 16. J.Race et al., 2007. Development of a predictive model for state condition pipeline external corrosion rates. Journal of Pipeline Engineering, RLi: local defect depth corrosion rate [mm/year] 1st Quarter, pp15-29. Rrc: corrosion growth rate determined by run 17. ASME B 31G: Manual for determining the remaining strength comparison, in a defect population sub- of corroded pipelines. group [mm/year] 18. H.Plummer and J.Race, 2003. Determining pipeline corrosion wi: defect width [mm] growth rates. Corrosion Management, April. 19. F.Caleyo et al., 2002. A study on the reliability assessment methodology for pipelines with active corrosion defects. Int.J.of Pressure Vessels and Piping, 79, pp77-86.Bibliography 20. G.Pognonec, 2008. Predictive assessment of external corrosion on transmission pipelines. IPC.1. S.A.Timashev and A.V.Bushinskaya, 2009. Diligent statistical 21. R.L.Burden and J.D.Faires, 1993. Numerical Analysis, 5th analysis of ILI data: implications, inferences and lessons Ed., PWS Publishers. learned. The Pipeline Pigging and Integrity Management Conference, Houston.2. R.G.Mora et al., 2009. Dealing with uncertainty in pipeline integrity and rehabilitation. The Pipeline Pigging and Integrity Management Conference, Houston.