Pipeline Corrosion Prediction and Reliability

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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 3, MARCH 2013 ISSN 2277-8616

58IJSTR2013www.ijstr.org

Pipeline Corrosion Prediction And ReliabilityAnalysis: A Systematic Approach With Monte

Carlo Simulation And Degradation ModelsChinedu I. OSSAI

Abstract: - In this research, Monte Carlo Simulation and degradation models were used to predict the corrosion rate and reliability of oil and gaspipelines. Discrete random numbers simulated from historic data were used to predict the corrosion rate using Brownian Random walk while the meantime for failure (MTFF) was estimated with the degradation models. The Survivor probability of the pipelines was determined with weibull analysis usingthe MTFF. The result of the study shows that the degradation models and Monte Carlo simulation can predict the corrosion rate of the pipelines to anaccuracy of between 83.3-98.6% and 85.2- 97% respectively.

Index Terms: - corrosion prediction, reliability analysis, degradation models, Monte Carlo simulation, mean time for failure (MTFF), survivor probability.

1INTRODUCTIONEstimation of pipeline corrosion is fundamental to the analysisof pipeline reliability. The corrosion of pipelines can be

described as a systematic degradation of the pipeline wall dueto the actions of operating parameters on the pipeline material.Since corrosion is a fundamental cause of pipeline failures inoil and gas industries [1], [2],[3], the minimization will inevitablyresult in the increased productivity. To prioritize inspectionaccording to the permissible risk level involves theunderstanding of the consequences of failure of a componenton a system [4]. This requires the analysis of the systemaccording to stipulated standards in order to predict theremaining life. For effective monitoring of pipeline reliabilityand remaining life prediction therefore, corrosion riskassessment is necessary. In order to manage corrosion risks,monitoring and inspection program will be incorporated intothe overall activity schedule of an organization. The probability

of failure is estimated based on the type of corrosion damageexpected to occur while the consequences of failure aremeasured against the impact of such a failure evaluatedagainst a number of criteria. The criteria could includepotential hazards to environment, risks associated with safetyand integrity, or risk due to corrosion or inadequate corrosionmitigation procedure [5]. Pipeline used in oil and gasproduction fail due to factors that are operationally, structurallyand environmentally induced. The operational factors areassociated with the components of the fluid flowing throughwhile the environmental factors deal with the electrochemicaland mechanical interactions of the pipeline material and theimmediate surroundings.

The arrangement of the microstructure and composition of thealloying elements essentially determine how the structuramakeup of the pipeline resists corrosion. The impact of latera

and axial stresses on the reliability of pipelines wasinvestigated by some authors [6] who concluded that stresscontributes to pipeline failure especially for undergroundpipelines. Flow assisted corrosion (FAC) have resulted inreduced mechanical strength of pipelines and stress corrosioncracking. Though many methods exists for the measuring othe strength of oil and gas pipelines like ASME B31G andRSTRENG methods, SHELL 92,FORM and the recenLinepipe Corrosion (LPC) method, as presented inBS7910[7],[8],[9]. Experience has shown that older pipelinesare not as tough as the new ones hence making test with mosof these methods speculative [8]. To minimize downtime andits impacts on production facilities requires an integratedapproach that provides the results that experts require to make

timely decision about a facility [10]. In the case of pipelinefailure, different techniques are necessary for estimating themean time for failure in a bid to enhance informed decisionabout in-service inspection times and procedures. Predictingthe corrosion and failure rates is done by using probabilisticapproach through the use of internal corrosion direcassessment(ICDA) ,Monte Carlo simulation, first ordereliability method (FORM), PIGS, ER-probes, H-probes, LPRetc.[3],[5],[7],[8],[10], The primary objective of this research isto predict pipeline corrosion rates using historic data vis--visestimating the reliability of the pipeline over a period of timeThis information is useful to pipeline corrosion experts whoconsistently plan corrosion mitigation activities through riskbased inspections.

2.0 Monte Carlo Simulation and Corrosion RatePredictionMonte Carlo Simulation is a stochastic approach of predictingprobability of the occurrence of an event by random numbegeneration between 0 and 1. This simulation tool helps todevelop a mathematical concept of complex real life systemwith a view to describing the behaviour of the system usingprobability of occurrence. To set up the simulation processinvolves the generation of randomly distributed randomnumbers between 0 and 1 using the mixed congruentiamethod (MCM). The MCM generates sequence of U(0,1)random numbers denoted by r0,r1,r2...rn according to the

Chinedu I. Ossai is interested in the following researchareas: pipeline corrosion, reliability centeredmaintenance, environmental pollution and control &inventory management. He has a Masters Degree inIndustrial Production Engineering from Federal Universityof Technology Owerri Nigeria and has over 12 years ofindustrial experience in Oil& gas and Manufacturingindustries. Email:[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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equation[1] below.

Where,

m is pre-specified positive integer known as modulus

a is pre-specified positive integer less than m known as themultiplier

c is non-negative integer less than m known as the increment.

The steps for the discrete random numbers generation usingMonte Carlo simulation is shown in figure 1. The discreterandom numbers generated is used to determine the previousvalue of corrosion rate (CRp) used for yearly corrosion rateestimation from Brownian Random walk.

Figure 1: Framework for Monte Carlo Simu lat ion of

Discrete Random Numbers

2.1 Estimating Corrosion Rate with Brownian RandomWalkThe corrosion rates in the pipeline is treated as a randomnumber that follows an irregular time series path known asBrownian Walk. This is evidence from the trend of corrosionrate measured over a certain period in the field as shown fromthe case study used in this research. The volatility of thisprocess is regulated by the Monte Carlo uniform distribution

simulation [11].The Brownian random walk can be representedby equation (2)

Where

CR= Change in the corrosion rate from one year to another

CRp= previous value of the corrosion rate (mm/yr).

= average value of the corrosion rate in each pipeline

= annualized volatility or standard deviation of the corrosionrate

T= change in time(in years) from one step to another

= value from a probability distribution (determined with Monte

Carlo Simulation)

Where PRNG= Portable random number generator.

The values of the average corrosion rate and standarddeviation of the pipelines of the studied fields are calculatedfrom the historic data. In this research, the previous value ofthe corrosion rate ( CRp) used in equation(2) is derived fromMonte Carlo simulation process described in the previous

section. The value is described as the annualized corrosionrate (ACR) of the pipelines in mm/yr. ACR is the highesoccurring predicted discrete random number or the average othe predicted discrete random numbers contained in the 10simulation runs carried out in the research. The Brownianrandom walk is used to predict yearly corrosion rate in mm/yrfor the pipelines.

2.2 Pipeline Corrosion wastage EstimationIn order to predict the corrosion wastage of the pipeline, theACR that will give the best estimate of the pipeline corrosionrate is determined. This is done by comparing the predictedcorrosion rates with the historic data using root mean squareerror (RMSE). The RMSE is determined according to equation

(4).

Where

n= number of years of the historic corrosion data used for theprediction

CRpred= Predicted Corrosion rate for ith year usingequation(2)

CRi= Measured field corrosion rate for ith year from historicdata.

The ACR with the lower RMSE is used to predict the corrosionwastage of the pipelines over a stipulated time frame. Thecorrosion wastage represents the cumulative wall thicknessloss of the pipeline at any time interval. The knowledge of thecorrosion wastage aid in decisions concerning risks basedinspection (RBI). If predicted corrosion rates using the bestACR (from equation (2)) for years 1, 2, ...,i are CR1,CR2...,CRi, the corrosion wastage for the nth year (CRn) is given byequation(5)


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The technique for predicting the pipeline corrosion wastageusing Monte Carlo simulation is shown in Figure 2.

3.0 Degradation Analysis and ReliabilityEstimationDegradation is a process of loss of integrity and function of asystem due to ageing, operation and other factors which couldinclude environmental and human factors. This phenomenonis common in pipelines, separators, turbines etc. According toresearch, process water with dissolved organic acidcontributes to a high level of degradation of pipelines andother steel vessels in the oil and gas industries [12]. To predictthe remaining life of the pipeline therefore requires theestimation of the rate of degradation vis--vis the reliability andthe remaining life. Degradation is a continuous progression ofwear and decay, so it can be modeled as a stochastic process.The measured degradation for ithtested device (i=1,2,...,n) will

consist of a vector of mimeasurements made at time pointsti1,...timi. The measured degradation time t can be modeled asthe unknown degradation (t) plus a measurement error term. At the mi time point, the degradation measurement (Yij) ofdevice i is given by equation (6)

The form of degradation () can be chosen to have a strictform or it can be more arbitrary. The forms of used todetermine the degradation of the pipeline is shown inequations (7)-(10)

1.

Linear model:

2. Power model:3. Exponential Model : )9( mTeCR 4. Logarithmic model : )10()ln(

mTCR

Where

CR= degradation of pipeline due to corrosion (mm/yr)

Tm= Time, & are constants of model parameters

The above degradation model equations were used toestimate the time of failure of the pipeline. The time for failureis assumed to be reached when the corroded wall thickness is45%-85% of the original wall thickness. The commuted time offailure (mean time for failure) was used for the life dataanalysis. The mean time for failure (MTFF) for the pipeline wasestablished using the degradation model according to therelationship shown in equation (11)

Where

ti = pipe wall thickness (mm)

p= percentage of corroded wall thickness ( 45%-85%)

CRit= measured corrosion rates along the pipeline (mm/yr) ayears (1, 2,..., n)

The determined MTFF is fitted to weibull analysis in order to

establish the reliability of the pipeline over time therebyestimating the remaining life. The cumulative density function(CDF) for weibull distribution is given by equation (12)

Where

F(t) = cumulative density function of mean time for failure( theprobability that a failure occurs before time t) & are scaleand shape parameters respectively. The value of identifiesthe mode of failure rate. When 1, there isa wear out failure increasing over time .The scale factoparameter is the life at which 63.2% of the unit will fail. Theframework for the degradation analysis for assessing thereliability of pipeline is shown in figure 3.

4.0 Prediction of Corrosion Rate - a CaseStudyExtensive internal corrosion data measured at least twice in ayear were collected over 8 years period from 11 different oiand gas fields in Nigeria OML 63 were used to test this modelThese 114mm diameter pipeline were made of X52 gradesteel material and carries oil and gas for about 700m to14,000m. The corrosion rate was measured with inserted

corrosion coupons and ER-probes. The ER-probes andcorrosion coupons were used to determine the change in thecorrosiveness of the pipelines due to changing operatingparameters. The preparation, installation and analysis of thecoupons and ER-probes were done according to NACEstandard RR0775 and ASTM G.1[13],[14]

4.1 Modeling StudiesTo set up the Monte Carlo simulation experiment for thisresearch, historic corrosion rate of the studied 11 pipelinesover 8 years were used. The Monte Carlo simulation wasrealized through 105trial runs. Microsoft Excel 2007 was usedfor the simulation run. The simulation was achieved by using


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the inbuilt discrete random number generation function. Thisfunction uses the RAND() function to convert the cumulativedensity function into discrete uniform numbers. The discreterandom numbers were generated from the set of historiccorrosion rate measured for a particular pipeline. TheCOUNTIF function was used to determine the frequency ofoccurrence of the discrete random numbers which representthe simulated pipeline corrosion rate in mm/yr. The measured

annual corrosion rate of the pipelines over the 8 year period isshown in Table1.

Figure 3: Degradat ion An alysis Framework for

pipeline Reliability Estim ate

Determine the probability

of the data

Collect Historic data of

pipeline corrosion

Generate N Discrete

Random Numbers (DRNs)

Determine the highest

occurring Discrete Random

Number (DRNfreq)

Calculate the

average

corrosion rate

Determine RMSEfreq

for DRNfreq

Determine

RMSEav for the

average corrosion

Is RMSEfreq

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Pipeline Corrosion Prediction and Reliability

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