8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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University of Akron Ohiorsquos Polytechnic University

IdeaExchangeUAkron

H3 Ramp3amp$ P+amp$43ltamp D G9 B Pamp- S W--3 H3

C--ampamp

S 2015

Proposed Statistical Model for Corrosion FailurePrediction and Lifetime Extension of Pipelines

Jessica Y Ripple +923amp

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lt3 H3 Ramp3amp$ P+amp$4 3 4 4 9 ampamp amp $$amp33 9 4amp ltamp D G9 B Pamp- S W--3 H3 C--ampamp 4

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F amp 4 -amp3amp $4$4 +amp ltamp U6amp349 A 3 O3 P-94amp$$ U6amp349 (=777amp)

Ramp$ampamp C44R-amp Jamp33$ P3amp S4434$- Mamp- C3 F-amp Pamp$4 Lamp4amp E84amp3 Pamp-amp3 (2015) Honors

Research Projects Pamp 101

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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PROPOSED STATISTICAL

MODEL FOR CORROSIONFAILURE PREDICTION AND

LIFETIME EXTENSION OF

PIPELINES

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8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 1

Table of ContentsExecutive Summary 2

Introduction 3

Background 5

General Corrosion 5

Oil Pipeline Corrosion 5

Corrosion Models 6

Model Discussion 8

Data Input 8

Monte Carlo Simulation 9

Economic Analysis 13

Recommendations and Conclusions 17

Recommendations 17

Conclusions 17

References 18

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 2

Executive Summary

Corrosion is an unavoidable global issue that has serious safety and environmentalconsequences if left unmitigated Currently the majority of methods used to address corrosionare reactive in nature meaning that industries are responding to leaks spills and catastrophes

after they have already occurred Ideally the asset owners of the corroding infrastructure andequipment should use predictive modeling to take proactive measures before a corrosion inducedrisk becomes a danger to society

This project focuses primarily on internal pipeline corrosion The US has hundreds ofthousands of miles worth of pipelines currently in operation These pipelines are oftentransporting hazardous materials such as natural gas and crude oil To protect society and theenvironment from loss of containment the US Department of Transportation requires mostpipelines to be analyzed for internal corrosion every five years using an in-line inspection (ILI)tool

The intended audience for this model are pipeline asset owners and operators The goal ofthis project was to develop a ldquouser-friendlyrdquo model that allows a user to analyze the datacollected from the ILI tool The user can choose to accept the default analysis options or easilymake adjustments to tailor the results to their data

This report presents the results from developing a predictive model that estimates theprobability of failure of a pipeline The model utilizes data collected with an in-line inspectiontool to generate a Monte Carlo simulation based on a desired statistical distribution The resultsof the Monte Carlo simulation provide the user with the probability of failure that each anomalyon the pipeline presents over a specific time period Using these probabilities the modelconducts an economic analysis to recommend an optimal repair schedule

Going forward this model can be improved by adding new distributions which willallow the data to be categorized more accurately and new predictive equations which permits theuser to adjust how conservative the results should be Also the model would greatly benefit froma second set of ILI data of the same pipeline to validate the calculated results

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 3

Introduction

The target audience for this research project is oil and gas pipeline operators owners orother responsible parties The final product a computer model utilizes raw industry dataaccepted industry standards statistical analysis and economical evaluations to recommend

optimal mitigation methods and scheduling As with any model the results are only as accurateas the data and engineering judgement that is input

Figure 1 shows the main process steps the model utilizes The raw data comes from in-line inspection (ILI) tools also known as ldquosmart pigsrdquo and consists of wall thickness andanomaly width and depth An anomaly is considered to be any defect in the pipe internal surfaceSmart pigs have the ability to catalog hundreds of kilometers worth of pipeline data Within themodel this data is then fit to an appropriate distribution curve which can be chosen based onminimizing standard error The next step entails performing a Monte Carlo simulation togenerate probabilistic pipeline conditions These conditions are individually assessed whichprovides a wide range of operating pressure estimates Once the pipeline operation has been

simulated over a specified range of time an economic analysis can be performed which willmake recommendations based on specified costs of failure and repair The final output of themodel includes a recommended year of repair at each specific anomaly location and theeconomic value of risk avoided by performing said repair

Every step is completed independently and requires various amounts of user inputs Theoverarching idea for this model is to allow the user to be a detailed or simplistic as he or sheprefers The model has default settings for the steps but has the flexibility for tailoring at anypoint

Figure 1 Pipeline reliability model execution steps

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1 983122983137983159 983108983137983156983137

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8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 4

Currently the main limitations of the model include the inability to distinguish betweeninternal corrosion and erosion as well as the inability to evaluate external corrosion Also themodel does not differentiate a failure as either a leak or a rupture Lastly the model assumes aconstant corrosion rate with time It cannot predict a sudden acceleration in corrosion Howeverfrequent and thorough data collection should be able to give an indication of a step change in the

corrosion rateThe data presented in this report is not meant to be extrapolated to represent other

pipelines The purpose of displaying and analyzing the data in this report is to showcase thetypes of analyses that the model can perform

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

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ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

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Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

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Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

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References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

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Ripple 1

Table of ContentsExecutive Summary 2

Introduction 3

Background 5

General Corrosion 5

Oil Pipeline Corrosion 5

Corrosion Models 6

Model Discussion 8

Data Input 8

Monte Carlo Simulation 9

Economic Analysis 13

Recommendations and Conclusions 17

Recommendations 17

Conclusions 17

References 18

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 2

Executive Summary

Corrosion is an unavoidable global issue that has serious safety and environmentalconsequences if left unmitigated Currently the majority of methods used to address corrosionare reactive in nature meaning that industries are responding to leaks spills and catastrophes

after they have already occurred Ideally the asset owners of the corroding infrastructure andequipment should use predictive modeling to take proactive measures before a corrosion inducedrisk becomes a danger to society

This project focuses primarily on internal pipeline corrosion The US has hundreds ofthousands of miles worth of pipelines currently in operation These pipelines are oftentransporting hazardous materials such as natural gas and crude oil To protect society and theenvironment from loss of containment the US Department of Transportation requires mostpipelines to be analyzed for internal corrosion every five years using an in-line inspection (ILI)tool

The intended audience for this model are pipeline asset owners and operators The goal ofthis project was to develop a ldquouser-friendlyrdquo model that allows a user to analyze the datacollected from the ILI tool The user can choose to accept the default analysis options or easilymake adjustments to tailor the results to their data

This report presents the results from developing a predictive model that estimates theprobability of failure of a pipeline The model utilizes data collected with an in-line inspectiontool to generate a Monte Carlo simulation based on a desired statistical distribution The resultsof the Monte Carlo simulation provide the user with the probability of failure that each anomalyon the pipeline presents over a specific time period Using these probabilities the modelconducts an economic analysis to recommend an optimal repair schedule

Going forward this model can be improved by adding new distributions which willallow the data to be categorized more accurately and new predictive equations which permits theuser to adjust how conservative the results should be Also the model would greatly benefit froma second set of ILI data of the same pipeline to validate the calculated results

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 3

Introduction

The target audience for this research project is oil and gas pipeline operators owners orother responsible parties The final product a computer model utilizes raw industry dataaccepted industry standards statistical analysis and economical evaluations to recommend

optimal mitigation methods and scheduling As with any model the results are only as accurateas the data and engineering judgement that is input

Figure 1 shows the main process steps the model utilizes The raw data comes from in-line inspection (ILI) tools also known as ldquosmart pigsrdquo and consists of wall thickness andanomaly width and depth An anomaly is considered to be any defect in the pipe internal surfaceSmart pigs have the ability to catalog hundreds of kilometers worth of pipeline data Within themodel this data is then fit to an appropriate distribution curve which can be chosen based onminimizing standard error The next step entails performing a Monte Carlo simulation togenerate probabilistic pipeline conditions These conditions are individually assessed whichprovides a wide range of operating pressure estimates Once the pipeline operation has been

simulated over a specified range of time an economic analysis can be performed which willmake recommendations based on specified costs of failure and repair The final output of themodel includes a recommended year of repair at each specific anomaly location and theeconomic value of risk avoided by performing said repair

Every step is completed independently and requires various amounts of user inputs Theoverarching idea for this model is to allow the user to be a detailed or simplistic as he or sheprefers The model has default settings for the steps but has the flexibility for tailoring at anypoint

Figure 1 Pipeline reliability model execution steps

983093 983109983139983151983150983151983149983145983139

983105983150983137983148983161983155983145983155

983092 983119983152983141983154983137983156983145983150983143

983120983154983141983155983155983157983154983141

983109983155983156983145983149983137983156983145983151983150

983091 983117983151983150983156983141

983107983137983154983148983151

983123983145983149983157983148983137983156983145983151983150

983090 983108983137983156983137

983108983145983155983156983154983145983138983157983156983145983151983150

983110983145983156983156983145983150983143

1 983122983137983159 983108983137983156983137

983113983150983152983157983156

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 4

Currently the main limitations of the model include the inability to distinguish betweeninternal corrosion and erosion as well as the inability to evaluate external corrosion Also themodel does not differentiate a failure as either a leak or a rupture Lastly the model assumes aconstant corrosion rate with time It cannot predict a sudden acceleration in corrosion Howeverfrequent and thorough data collection should be able to give an indication of a step change in the

corrosion rateThe data presented in this report is not meant to be extrapolated to represent other

pipelines The purpose of displaying and analyzing the data in this report is to showcase thetypes of analyses that the model can perform

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 5

Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

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Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

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983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

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Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

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Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

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Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

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Ripple 2

Executive Summary

Corrosion is an unavoidable global issue that has serious safety and environmentalconsequences if left unmitigated Currently the majority of methods used to address corrosionare reactive in nature meaning that industries are responding to leaks spills and catastrophes

after they have already occurred Ideally the asset owners of the corroding infrastructure andequipment should use predictive modeling to take proactive measures before a corrosion inducedrisk becomes a danger to society

This project focuses primarily on internal pipeline corrosion The US has hundreds ofthousands of miles worth of pipelines currently in operation These pipelines are oftentransporting hazardous materials such as natural gas and crude oil To protect society and theenvironment from loss of containment the US Department of Transportation requires mostpipelines to be analyzed for internal corrosion every five years using an in-line inspection (ILI)tool

The intended audience for this model are pipeline asset owners and operators The goal ofthis project was to develop a ldquouser-friendlyrdquo model that allows a user to analyze the datacollected from the ILI tool The user can choose to accept the default analysis options or easilymake adjustments to tailor the results to their data

This report presents the results from developing a predictive model that estimates theprobability of failure of a pipeline The model utilizes data collected with an in-line inspectiontool to generate a Monte Carlo simulation based on a desired statistical distribution The resultsof the Monte Carlo simulation provide the user with the probability of failure that each anomalyon the pipeline presents over a specific time period Using these probabilities the modelconducts an economic analysis to recommend an optimal repair schedule

Going forward this model can be improved by adding new distributions which willallow the data to be categorized more accurately and new predictive equations which permits theuser to adjust how conservative the results should be Also the model would greatly benefit froma second set of ILI data of the same pipeline to validate the calculated results

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Ripple 3

Introduction

The target audience for this research project is oil and gas pipeline operators owners orother responsible parties The final product a computer model utilizes raw industry dataaccepted industry standards statistical analysis and economical evaluations to recommend

optimal mitigation methods and scheduling As with any model the results are only as accurateas the data and engineering judgement that is input

Figure 1 shows the main process steps the model utilizes The raw data comes from in-line inspection (ILI) tools also known as ldquosmart pigsrdquo and consists of wall thickness andanomaly width and depth An anomaly is considered to be any defect in the pipe internal surfaceSmart pigs have the ability to catalog hundreds of kilometers worth of pipeline data Within themodel this data is then fit to an appropriate distribution curve which can be chosen based onminimizing standard error The next step entails performing a Monte Carlo simulation togenerate probabilistic pipeline conditions These conditions are individually assessed whichprovides a wide range of operating pressure estimates Once the pipeline operation has been

simulated over a specified range of time an economic analysis can be performed which willmake recommendations based on specified costs of failure and repair The final output of themodel includes a recommended year of repair at each specific anomaly location and theeconomic value of risk avoided by performing said repair

Every step is completed independently and requires various amounts of user inputs Theoverarching idea for this model is to allow the user to be a detailed or simplistic as he or sheprefers The model has default settings for the steps but has the flexibility for tailoring at anypoint

Figure 1 Pipeline reliability model execution steps

983093 983109983139983151983150983151983149983145983139

983105983150983137983148983161983155983145983155

983092 983119983152983141983154983137983156983145983150983143

983120983154983141983155983155983157983154983141

983109983155983156983145983149983137983156983145983151983150

983091 983117983151983150983156983141

983107983137983154983148983151

983123983145983149983157983148983137983156983145983151983150

983090 983108983137983156983137

983108983145983155983156983154983145983138983157983156983145983151983150

983110983145983156983156983145983150983143

1 983122983137983159 983108983137983156983137

983113983150983152983157983156

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Ripple 4

Currently the main limitations of the model include the inability to distinguish betweeninternal corrosion and erosion as well as the inability to evaluate external corrosion Also themodel does not differentiate a failure as either a leak or a rupture Lastly the model assumes aconstant corrosion rate with time It cannot predict a sudden acceleration in corrosion Howeverfrequent and thorough data collection should be able to give an indication of a step change in the

corrosion rateThe data presented in this report is not meant to be extrapolated to represent other

pipelines The purpose of displaying and analyzing the data in this report is to showcase thetypes of analyses that the model can perform

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 5

Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

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Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

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Ripple 3

Introduction

The target audience for this research project is oil and gas pipeline operators owners orother responsible parties The final product a computer model utilizes raw industry dataaccepted industry standards statistical analysis and economical evaluations to recommend

optimal mitigation methods and scheduling As with any model the results are only as accurateas the data and engineering judgement that is input

Figure 1 shows the main process steps the model utilizes The raw data comes from in-line inspection (ILI) tools also known as ldquosmart pigsrdquo and consists of wall thickness andanomaly width and depth An anomaly is considered to be any defect in the pipe internal surfaceSmart pigs have the ability to catalog hundreds of kilometers worth of pipeline data Within themodel this data is then fit to an appropriate distribution curve which can be chosen based onminimizing standard error The next step entails performing a Monte Carlo simulation togenerate probabilistic pipeline conditions These conditions are individually assessed whichprovides a wide range of operating pressure estimates Once the pipeline operation has been

simulated over a specified range of time an economic analysis can be performed which willmake recommendations based on specified costs of failure and repair The final output of themodel includes a recommended year of repair at each specific anomaly location and theeconomic value of risk avoided by performing said repair

Every step is completed independently and requires various amounts of user inputs Theoverarching idea for this model is to allow the user to be a detailed or simplistic as he or sheprefers The model has default settings for the steps but has the flexibility for tailoring at anypoint

Figure 1 Pipeline reliability model execution steps

983093 983109983139983151983150983151983149983145983139

983105983150983137983148983161983155983145983155

983092 983119983152983141983154983137983156983145983150983143

983120983154983141983155983155983157983154983141

983109983155983156983145983149983137983156983145983151983150

983091 983117983151983150983156983141

983107983137983154983148983151

983123983145983149983157983148983137983156983145983151983150

983090 983108983137983156983137

983108983145983155983156983154983145983138983157983156983145983151983150

983110983145983156983156983145983150983143

1 983122983137983159 983108983137983156983137

983113983150983152983157983156

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Ripple 4

Currently the main limitations of the model include the inability to distinguish betweeninternal corrosion and erosion as well as the inability to evaluate external corrosion Also themodel does not differentiate a failure as either a leak or a rupture Lastly the model assumes aconstant corrosion rate with time It cannot predict a sudden acceleration in corrosion Howeverfrequent and thorough data collection should be able to give an indication of a step change in the

corrosion rateThe data presented in this report is not meant to be extrapolated to represent other

pipelines The purpose of displaying and analyzing the data in this report is to showcase thetypes of analyses that the model can perform

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Ripple 5

Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 4

Currently the main limitations of the model include the inability to distinguish betweeninternal corrosion and erosion as well as the inability to evaluate external corrosion Also themodel does not differentiate a failure as either a leak or a rupture Lastly the model assumes aconstant corrosion rate with time It cannot predict a sudden acceleration in corrosion Howeverfrequent and thorough data collection should be able to give an indication of a step change in the

corrosion rateThe data presented in this report is not meant to be extrapolated to represent other

pipelines The purpose of displaying and analyzing the data in this report is to showcase thetypes of analyses that the model can perform

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Ripple 5

Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 5

Background

General Corrosion

Corrosion is simply defined as the ldquodegradation of a material due to a reaction with its

environmentrdquo (1) A wide variety of materials can fall victim to corrosion including metalspolymers and ceramics Additionally these materials can undergo various forms of corrosionmost often simultaneously (2) Figure 2 displays the corrosion mechanism using a basicelectrochemical cell schematic (2) Corrosion occurs due to the oxidation reaction taking place atthe anode The loss of electrons at the anode is balanced by the reduction reaction occurring atthe cathode Depending on the chemical concentration and properties of the environment (shownas the electrolyte in Figure 2) the corrosion rate may be almost negligible (less than 1 mpy) orvery severe (greater than 200 mpy) (3)

Figure 2 Corrosion representation using electrochemical cell

Unmitigated corrosion has significant safety environmental and economicconsequences on a global scale In 2002 a comprehensive study determined the total direct costsof corrosion in a wide variety of industry sectors in the United States totaled $276 billion (4) Tocombat corrosion several mitigation strategies are available The most common include materialselection coatings inhibitors and cathodic protection (5)

Oil Pipeline Corrosion

For this specific project the primary focus was of internal corrosion of oil pipelines in theUnited States Crude oil itself is not corrosive Instead the corrosion is caused by carbon dioxide(CO2) hydrogen sulfide (H2S) and especially water (6 ) At high velocities the water will remainentrained in the crude oil and will not be able to settle out and cause corrosion (7 ) However atlower velocities there is a greater risk of two phase flow and thus corrosion can occur in thewater phase Light crude oils containing water are at a higher risk due to the greater difference in

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 921

Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1321

Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

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Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 6

density to water (7 ) Therefore as refineries are forced to run heavier crudes over time the crudeoil pipelines will actually decrease their risk of corrosion

Today there are limits in place on water concentration in transporting crude oil inpipelines In 2012 the length of US oil pipelines was approximately 152000 miles (8 ) Forabout 90 of hazardous liquid pipelines pipeline operators have the option of using a ldquosmart

pigrdquo to collect integrity data (9) Figure 3 illustrates a smart pig created by Nord Stream whichuses magnetic flux technology which is one of the most common methods (10) These devicesare capable of detecting the location of pipeline defects such as wall thinning mechanicaldamage material defects and cracks (9) The data often referred to as ldquoin-line inspectionrdquo or ILIdata was the primary input to the developed model The United States Department ofTransportation requires pipeline owners to complete a smart pig run every five years although itis common for them to be performed even more frequently (11)

Figure 3 Nord Stream magnetic flux smart pig

Corrosion Models

Currently in industry it is common to use one of the previously developed deterministicmodels to estimate the service lifetime before corrosion induced pipeline failure Figure 4 compares the pipeline failure pressure as a function of defect length using different models (12)The models all require similar information (anomaly length and depth pipe thickness etc) tocalculate the failure pressure For the purpose of this model the Modified B31G was used sinceit is a standard produced by the American Society of Mechanical Engineers (ASME) and is lessconservative than the original B31G model (13) The ASME Modified B31G method wasprimarily developed on older lower strength steels while some of the ldquonewerrdquo models including

the DNV-99 method were developed based on modern pipeline materials (14)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

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Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 921

Ripple 7

Figure 4 Pipeline failure pressure vs defect length for various models

A Level 1 Modified B31G evaluation was conducted based on the steps outlined in theASME Technical Standard document (13) Equation 1 shows the formula used to calculate thefailure pressure for the model where YS is yield strength t is the pipe thickness d(T) is theanomaly depth at time T L(T) is the anomaly length at time T and D is pipe diameter (12) Thisequation assumes a ldquoflow stressrdquo expression appropriate for a material with specified minimumyield strengths below 483 MPa and operating temperatures below 120degC (13)

Equation 1

1 06275

0003375

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1021

Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1121

Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

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Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1021

Ripple 8

Model Discussion

Data Input

The ILI data used to develop this model spanned 100 kilometers of industrial pipeline

The source of this pipeline data cannot be identified due to confidentiality although it wasprovided to the University of Akron with the intention of creating a predictive model It is worthnoting that only the raw data points were provided Any subsequent analyses statisticalmodeling or calculations were completed as part of this research project

Within the 100 kilometers of data over 3400 anomalies were documented Figure 5 displays the frequency of the data utilized in the model This analysis was carried out Potentialoutliers were not removed because these sites likely represent spots of local acceleratedcorrosion which was considered important to keep in the model Corrosion rates were determinedbased on the assumption that the pipe had been corroding for 10 years Ideally data from twoseparate ILI audits would be used to estimate corrosion rates more accurately

Figure 5 Pipeline ILI data histograms from a private industrial source

Figure 6 shows the probability density function (PDF) curves that were necessary forrunning the model The corrosion rate is shown on the left while the longitudinal growth rate isshown on the right These distribution curves are required for performing the Monte Carlosimulation Figure 7 shows an example of how the distribution curves were chosen for thelongitudinal growth rate The fit that yielded the lowest standard error was selected to representthe data shape Therefore the corrosion rate was fit to a normal distribution curve while thelongitudinal growth rate was fit to a lognormal distribution curve

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1121

Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1321

Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1121

Figure 6 Probability density fu

Figure 7 Distribution curve fitti

Monte Carlo Simulation

Once the raw data is enteevaluated the probability of failMonte Carlo simulation (MCS)

ction curves used in the model

ng for longitudinal growth rate

red and the appropriate distribution curve parare over time for each anomaly location is calcurocess The Monte Carlo simulation is defined

Ripple 9

eters arelated via theas ldquoa numerical

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1321

Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1221

Ripple 10

experimentation technique to obtain the statistics of the output variables of a system computationmodel given the statistics of the input variablesrdquo (15) Figure 8 outlines the individual stepsrequired to complete a MCS A random number generator was used in Step 1 This randomnumber was associated with a corrosion and anomaly longitudinal growth rate using the inversenormal and lognormal functions respectively A MCS simulation including 1000 trials was

performed for each specific anomaly location Therefore the rate of corrosion depth andlongitudinal growth were statistical inputs to the simulation while the anomaly length and depthwere not For each anomaly the cumulative distribution function (CDF) of the calculated failurepressure was plotted The CDF is defined by NIST as the ldquoprobability that the variable takes avalue less than or equal to xrdquo (16 ) For this model the ldquovariablerdquo is the pressure calculated usingEquation 1 and ldquoxrdquo is the pipeline operating pressure Therefore the failure probability is foundby plotting the CDF and finding the probability that corresponds to the pipeline operatingpressure as shown in Figure 10 The model was set up on the basis for 15 years prediction

Figure 8 Calculation steps of a MCS process

1 Choose a random number between

0 and 1

2 Calculate the desired variable usingoptimal distribution arameters thatcorrespond to the random number

3 Repeat the first two steps for manyrepetitions (n = 1000)

4 Plot the cumulative distributionfunction (CDF)

5 The intersection of the CDF curvewith a value of interest represents the

probability of occurrence

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1321

Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1321

Figure 9 shows CDF equdata (17 ) (18 ) (19) (20) Whenrepresents a probability labeledlength etc) is then calculated byInverse distribution equations ar

and MATLAB The inverse distrand standard deviation) that are

Figure 9 Commonly used distri

Figure 10 shows the MCcorrosion The operating pressurapproximately 71 From this dparticular anomaly corroding to

983118983151983154983149983137983148

991266 19831342〖2991266 σ983155983156983137983150983140

983116983151983143983150983151983154983149983137983148 991266 1〗9831342〖991266 σ983155983156983137983150983140

983127983141983145983138983157983148983148

991266 1991266 α983155983139983137983148983141

ations for distributions that are commonly seena random number is selected in Step 1 of the Mp The desired variable (whether it be corrosionsolving for the x value that corresponds to a spcommonly available in software including Mi

ibution equations require the parameters (suchhown below

utions and their respective cumulative distribu

S output for a singular anomaly after 15 years oof the line is 56 MPa which intersects the C

ta the conclusion is that after 15 years the prohe point of failure is 71

radic2 int983135infin983134 983134〖〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

radic2 int9831350983134 983134〖983148983150 2 〗9831342 〗983154983140 983140983141983158983145983137983156983145983151983150 μ983137983158983141983154983137983143983141

983134〖 〗983134 983137983154983137983149983141983156983141983154 β983155983144983137983152983141 983152983137983154983137983149983141983156983141983154

Ripple 11

in corrosionCS process itrate anomalyecific value of pcrosoft Excel

s the average

ion functions

f predictedF curve at

bability of this

〗

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1421

Figure 10 Monte Carlo simulati

To draw conclusions aboused Figure 11 shows the rangeyear for the same anomaly consiand minimum pressures that werpressure begins to extend belowfailure probability as shown in t

Figure 11 Estimated pressure aat 82190 m from pipeline start

on for a single anomaly located at 82190 m fro

t the anomaly over time the average calculateof pressures calculated and the overall probabilered in Figure 10 The data bars correspond tocalculated in the MCS At year 4 the minimu

the pipeline operating pressure This results in she graph on the right

d probability of failure prediction for singular

Ripple 12

m pipeline start

pressure wasity of failure perthe maximum

calculatedome amount of

nomaly located

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1521

Ripple 13

Lastly Figure 12 illustrates how the individual anomaly analyses were combined toproduce a failure probability ldquosnapshotrdquo of the bulk pipeline after 15 years of predictedcorrosion The percentages represent the probability of failure at each specific location Whilethis image was created manually using MATLAB the intention is to have the simulationgenerate all images automatically

Figure 12 Failure probability of pipeline segment after 15 years

Economic Analysis

There are many ways of accounting for costs associated with corrosion and how toaccount for these savings when employing mitigation strategies Previous studies on costs wereevaluated from the following perspectives ldquothe cost to the economy of a nation and the cost ofselected corrosion control measuresrdquo (17 ) Figure 13 lists some of the costs typically associatedwith corrosion (17 ) For this project capital and design costs were not considered since the focusof this work is protecting existing assets Thus the economic analysis focused on the trade-offbetween control costs (ie repairing the anomaly using inhibitors) and associated costs (ie lossof containment due to pipeline failure pipeline capacity diminished)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1621

Figure 13 Various costs associa

Initially the base cost offailure was estimated as $1000analyzed separately In each yea

the product of the failure penaltythan the economic risk of failureOnce the model recommends a yreduced to zero For example Fi

anomaly and how this comparesis calculated where the ldquonrdquo subsin year 1 should be calculated usthe probability of failure increasanomaly as compared to repairin8

Equation 2

Figure 14 Cost analysis for sin

CapitalDesignCosts

bull Replacementof equipment

and buildingsbull Excess

capacity

bull Redundantequipment

Co

bull Maand

bull Cocon

ted with corrosion

repair was estimated as $10000 and the penalt00 To determine the optimal time for repair e the cost of repair was compared to the cost of

and the probability of failure If the cost of repin a given year it was recommended to do fix tear of repair the probability of failure of the sugure 14 shows the total risk for each given yearto the base cost of repair Equation 2 shows hocript refers to the beginning of year n For exaing the failure probability that corresponds to ys such that it becomes a greater economic riskg it Thus the recommendation is to repair the

le anomaly located at 805 m from pipeline start

trol Costs

ntenancerepair

rosiontrol

Design Costs

bull Materials ofconstruction

bull Corrosionallowance

bull Specialprocessing

bull Lpr

bull TS

bull In

bull In

Ripple 14

of a pipelinech year wasfailure which is

ir was lowerhe anomalysequent years isfor a specific

w the total riskple the total riskar 1 In year 8o not repair thenomaly at year

ssociatedCosts

ss ofoduct

chnicalpport

surance

ventory

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1721

Figure 15 shows the copipelines discounted back to therisk that is accrued as the probabaccumulated with repairs is the abefore the model recommends re

basis the total risk of operating tas adding inhibitors have the poamount

Equation 3 shows how tthe beginning of year n and the sconsidered from the beginning oEquation 3 was used as explicitlbut once the anomaly was repair

Equation 3

Figure 15 Discounted risk comtime

Lastly Figure 16 compaaccepted by not employing mitig

lower than the evaluated cost ofand thus the cost of mitigation sl12 showed it is very common torepair this segment of pipeline ssuch as excavating and labor coscorrosion is estimated at over $4the pipeline is predicted to avoid

parison of risk between repairing and not repaibeginning of year 0The risk of not repairing isility of failure increases annually The amountmount of proportion of the failure penalty that ipairing the anomaly By optimizing repairs on

he pipeline can be dramatically reduced Otherential to reduce the cost of risk by an even mor

e discounted total risk was calculated in Figur

ummation is over the entire length of the pipelithe year the first value of n is 2 For the case

y written For the case with repairs Equation

d the risk for subsequent years was set to 0

arison of repairing vs not repairing all pipeline

res the predicted annual cost of repairs vs the cation methods Excluding year 14 the cost of r

risk Year 14 had the largest number of recommightly exceeds the amount of risk accepted Hosee anomalies that are close together Thereforould be lower than what is predicted due to shats Over 15 years the economic risk that will be1 million By implementing the recommended r$135 million in risk over 15 years

Ripple 15

ing thethe incrementalf risks acceptedn economic

echniques suchdramatic

15 where n ise Since n isith no repairswas still used

anomalies over

st of risk that ispairs is always

ended repairsever as Figure

the cost tored expensesaccrued due topair schedule

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1821

Figure 16 Risk associated withrepairs

not repairing all pipeline anomalies vs the ann

Ripple 16

al cost for

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 1921

Ripple 17

Recommendations and Conclusions

Recommendations

While the model is performing as intended the following recommendations would bebeneficial to its future users

bull Test the predictions of this model by acquiring a second set of ILI data of the samepipeline

bull Diversify the distributions available for fitting the ILI data

bull Include analysis of field data such as the effects of product chemistry waterconcentration and solids content on growth rates

bull Apply the same methodology used in this work for external corrosion

bull Determine methods to estimate the effects of external corrosion mitigation

bull Add additional failure estimation models (such as the Shell or DNV-99) as a comparison

to the Modified B31G

bull

Have model display not only the anomaly locations along the pipeline but also theirorientation within it

Conclusions

This model analyzes federally mandated data for pipeline owners by combiningdistribution fitting techniques statistical analysis and an economic evaluation The model hasbeen constructed to permit easy modifications which allow the user to make the data analysis assimple or as complicated as desired Based on the specific pipeline data analyzed and estimatedrepair costs it is predicted that approximately $135 million dollars of risk can be avoided byimplementing an optimized repair schedule for the entire pipeline over 15 years Based on the

model output the majority of repairs should take place between years 13-15 While the modelstill requires sound engineering judgement the results can still be used to drive management andbudgetary decisions Also as heavier crudes become more popular and general corrosionawareness increases it is predicted that the number of anomalies per pipeline will decrease overtime

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2021

Ripple 18

References

1 NASA Fundamentals of Corrosion and Corrosion Controlhttpcorrosionkscnasagovcorr_fundamentalshtm (accessed March 23 2015)

2 NACE International Corrosion Basics httpswwwnaceorgCorrosion-CentralCorrosion-101Corrosion-Basics (accessed March 23 2015)

3 Callister W D Rethwisch D G Material science and engineering An introduction 8thed Wiley amp Sons New York 2010

4 Koch G Brongers M Thompson N Virmani P Payer J Corrosion costs and

preventive strategies in the United States US Federal Highway Administration McLean2002

5 ASM International Corrosion Understanding the Basics ASM International MaterialsPark 2000

6 Nyborg R Controlling internal corrosion in oil and gas pipelines Business Briefing Exploration and Production The Oil and Gas Review 2005 No 2 70-74

7 Larson K R Managing Corrosion of Pipelines that Transport Crude Oils Materials

Performance 2013 52 (5) 28-35

8 United States Department of Transportation US Oil and Gas Pipeline Mileagehttpwwwritadotgovbtssitesritadotgovbtsfilespublicationsnational_transportation_statisticshtmltable_01_10html (accessed March 23 2015)

9 US Department of Transportation The Changing Face of Transportation Bureau ofTransportation Statistics Washington DC 2000

10 Nord Stream Intelligent PIG httpwwwnord-streamcompress-infoimagesintelligent-

pig-3467page=3 (accessed March 23 2015)

11 Alyeska Pipe Pigging the Trans-Alaska Pipelinehttpdecalaskagovsparperpresponsesum_fy11110108301factsheetsfact_Piggingpdf (accessed March 23 2015)

12 Caleyo F Gonzalez J L Hallen J M A study on the reliability assessmentmethodology for pipelines with active corrosion defects International Journal of Pressure

Vessels and Piping 2002 No 79 77-86

13 ASME Manual for Determining the Remaining Strength of Corroded Pipelines IndustryStandard ASME New York 2009

14 Mustaffa Z Developments in Reliability-Based Assessment of Corrosion In Developments in Corrosion Protection Aliofkhazraei M Ed Intech 2014 pp 681-696

15 Mahadevan S Monte Carlo Simulation In Reliability-Based Mechanical Design CruseT A Ed Marcel Dekker INC New York 1997 p 123

16 NIST Related Distributionshttpwwwitlnistgovdiv898handbookedasection3eda362htm (accessed April 152015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)

8182019 Proposed Statistical Model for Corrosion Failure Prediction and L

httpslidepdfcomreaderfullproposed-statistical-model-for-corrosion-failure-prediction-and-l 2121

Ripple 19

17 Kruger J Cost of Metallic Corrosion In Uhligs Corrosion Handbook 3rd ed Revie RW Ed John Wiley amp Sons Ottawa 2011 pp 15-17

18 MathWorks Normal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatsnorminvhtml (accessed April 20 2015)

19 MathWorks Lognormal inverse cumulative distribution functionhttpwwwmathworkscomhelpstatslogninvhtml (accessed April 20 2015)

20 MathWorks Weibull inverse cumulative distribution functionhttpwwwmathworkscomhelpstatswblinvhtml (accessed April 20 2015)