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IPC_2010
1.
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
2.
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]. 2 Copyright © 2010 by ASME
3.
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. 3 Copyright © 2010 by ASME
4.
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. 4 Copyright © 2010 by ASME
5.
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 5 Copyright © 2010 by ASME
6.
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. 6 Copyright © 2010 by ASME
7.
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). 7 Copyright © 2010 by ASME
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