Cassandra_BP.pdf

CorrosionPredictionModellingA guide to the use ofcorrosion predictionmodels for risk assessmentin oil and gas productionand transportationfacilitiesA J McMahon, D M E Paisley

Sunbury Report No. ESR.96.ER.066dated November 1997

Main CDContents

Contents

Summary

Acknowledgements

Introduction 1

"Cassandra 98" Corrosion Prediction Spreadsheetby A J McMahon

Introduction 5Quick Start 6Limitations of Corrosion Prediction Models 8Detailed Description of the Spreadsheet 11Comparing Output from the "Cassandra 98" Model with Field Data 27Appendix 1: Henry's Law Constans for CO2 Dissolved in Brine 29

The Use of Corrosion Prediction Models During Designby D E Paisley

Introduction 31Important Factors not Covered by the Corrosion Model 35Effect of Corrosion Inhibitors 42Predicting the Effectiveness of Corrosion Inhibitors - 48'The Inhibitor Availability Model'Recommended Values for use in the Inhibitor Availability Model 51Comparisons of the Inhibitor Availability Model with BP's Previous Model 62Corrosion Rates of Low Alloy Steels 64Preferential Weld Corrosion 65Effects of Pitting 66Choosing an Optimum Corrosion Allowance 67Applying Models to Different Flow Regimes 69Applying Models to Transportation Equipment 72Applying Models to Process Equipment 86Flow Velocities in Process Pipework 89Economic Tools to Use During Materials Selection 92

References 95

Installation of the Cassandra 98 Excel Workbook 97

Page

Summary

This document decribes BP's current approach to Corrosion Prediction and itsuse during the design of pipelines and facilities. It is divided into two sections.

The first section introduces a new prediction spreadsheet called Cassandra 98*which is BP's implementation of the CO2 prediction models published by deWaard et al. It builds on these models to include BP's experience of such systems.The pocket inside the front cover of this report contains a floppy disc whichcontains the necessary programs and spreadsheets to run it together with a setof installation instructions.

The second section discusses how the prediction model may be used for designpurposes and it introduces several improvements from previous guidelines.These include the use of the probabilistic approach to corrosion prediction andthe use of corrosion inhibitor availabilities instead of efficiencies. It also discussesthe use of "corrosion risk categories" as a way of quantifying the corrosion riskat the design stage. The floppy disc also contains a spreadsheet for calculatingthe risk category.

To illustrate the points made examples have been obtained from many BP assetsworldwide. Where financial data are shown it is from 1997.

Since this subject is continually changing it is anticipated that these guidelineswill be updated in future years and so any comments or suggestions regardingeither the content or appearance of them would be very welcome.

*In Greek mythology Cassandra was the daughter of Priam and Hecuba. She was endowed withthe gift of prophecy but fated never to be believed. She is generally regarded as the prophet ofdisaster........especially when disregarded.

Acknowledgements

The authors would like to thank the following BP staff for theircontributions to these guidelines.

Jim CorballyLaurence CowieMike FielderDon HarropBill HedgesWill McDonaldTracy SmithSimon WebsterRichard Woollam

1Introduction

Carbon dioxide corrosion represents the greatest risk to the integrity ofcarbon steel equipment in a production environment. Compared with theincidences of fatigue, erosion, stress corrosion cracking or over-pressurisation, the incidences of CO2 related damage are far more common.Unfortunately, the engineering solutions to eradicating the CO2 corrosion riskrequire high capital investments in corrosion resistant materials. As Figure 1shows, providing a corrosion allowance of 8 mm to carbon steel flowlinescosts a significant sum at circa US$1 million per 5 km but even this isinsignificant in terms of the costs of the various corrosion resistant flowlineoptions.

Similar relative costs are incurred when specifying corrosion resistantmaterials downhole or in facilities. This is rarely justified. For this reason, CO2corrosion of carbon steel will always be a problem that BPX has to deal with.Managing CO2 corrosion therefore becomes a priority and it can becomeexpensive. The replacement of the original Forties MOL and the severedamage to the Beatrice MOL are two examples of high costs that BPX haveincurred in recent years due to unpredicted corrosion rates. Successfulmanagement of CO2 corrosion starts off with the identification of risks andcontinues with the provision of suitable controls and the review of thesuccess of the controls via monitoring - as illustrated in Figure 2.

Figure 1: FullyInstalled Costs forVarious FlowlineMaterials Options inColombia (1997)

0

5

10

15

20

25

30

35

6 8 10 12 14 16 18 20 22 24 26 28 30

Nominal Flowline Diameter - Inches

Cost per

5 Km ($mil l)

Carbon steel 8mm ca

Duplex SS

13%Cr

Bi-metal 13Cr liner

Carbon steel, no ca

2INTRODUCTION

This document sets out BPs approach to the quantification of CO2 corrosionrisk through the use of predictive models. In doing so, it also discusses thereliance that can be placed on corrosion inhibition as the only viable controlmeasure for carbon steel and the importance of suitable corrosionmonitoring. To put the importance of this into context, corrosion costs BPX8.3% of its capex budget and increases lifting costs by 14%, an average ofover 8 cents per barrel. Figure 3 shows that the costs are distributed acrossthe entire range of facilities.

Apply ControlsMonitor Effectiveness

Quantify Risk

Figure 2: The FeedbackLoop that is Required forSuccessful Managementof CO2 Corrosion

3INTRODUCTION

The quantification of corrosion risk is required at several stages during an assetslife. The most obvious period is during the project phase when the originalmaterials of construction are being selected. This process must be repeatedduring the life of the asset if failures or expansions require the procurement ofadditional facilities. Quantifying the corrosion risk is also important in tailoringinspection strategies. Risk based inspection is now widely adopted and, as CO2corrosion represents one of the most important factors governing the probabilityof failure for much equipment, a reasoned approach should be taken. It isimportant that this approach is theoretically sound but also reflects pastexperience.

This version of the BP CO2 prediction model is the first to be published since1993/4 when the guidelines on multiphase and wet gas transport respectivelywere issued. The new guidelines incorporate changes by the authors to the semi-empirical model used in the original guideline as well as comprehensiveguidance on how to use the spreadsheet included with this version. The newmodel also includes the ability to predict the affects of changing flow velocitieson uninhibited corrosion rates.

Downhole13%

Subsea59%

Chemicals4%

Topsides23%

Personnel1%

Figure 3: TheDistribution of Costs ofCorrosion Across TenBPX North Sea Assets,1990 to 1994.

4INTRODUCTION

The new guidelines also consider the probabilistic approach to predicting CO2corrosion. Probabilistic approach to design in general is becoming morewidespread and offers several advantages over the traditional deterministicapproach. The probabilistic approach is neither endorsed nor disallowed but isdiscussed as, in some cases, it may be more appropriate than a deterministicapproach.

The approach to designing for the use of corrosion inhibitors has been changedsignificantly. The previous approach described the affects of an inhibitorthrough the use of an efficiency factor, such as 90%. This does not reflect BPXsrecent field data generated under severe conditions which showed inhibitorscan be more effective than predicted. "Inhibitor efficiencies" have thereforebeen replaced with "inhibitor availabilities" that more closely reflect fieldexperience. There is a general move in the industry towards this methodologyand it offers several advantages.

However, it has become clear that for inhibitors to work effectively thecorrosion management system must be highly organised. Recommendations aretherefore included on methods to ensure that the inhibitor availabilitiesassumed at the design stage occur during the operational stage.

5"Cassandra 98" Corrosion Prediction Spreadsheetby A J McMahon

"Cassandra 98 is BP's new implementation of the 1991, 1993 and 1995 CO2corrosion prediction models published by De Waard et al. The pocket inside thefront cover of this report contains a floppy disc with the programme togetherwith a set of installation instructions.

The 1991 and 1993 De Waard models are already widely used within BP andelsewhere in a variety of customised forms. This report describes the newCassandra 98 spreadsheet for Microsoft Excel. It is based primarily on the 1993De Waard model, incorporates some equations from the 1991 model, and usesthe 1995 model to assess velocity effects. The spreadsheet is intended to captureall the best features of the 1991, 1993 and 1995 models [1,2,3]. Certain extrafeatures from outside the De Waard papers, based on standard physicalchemistry, have also been included. The source, background and limitations ofall the assumptions and equations in the spreadsheet are fully documented inthese guidance notes.

The Cassandra 98 spreadsheet is written in a simple and accessible format withinMicrosoft Excel (version 7.0). It avoids the use of macros or special techniquesso that the logic and the calculations are as transparent as possible. Thisapproach also ensures that the spreadsheet is immediately compatible with newversions of Excel.

The Excel add-in module "CRYSTAL BALL" (from Decisioneering Ltd, 1380Lawrence Street, Suite 520, Denver, Colorado 80204, USA. Tel: +1 303 292 2291.Cost ~100) enables probability distributions to be set for each input cell and itthen uses Monte-Carlo simulation to combine these into a probability distributionfor the resulting corrosion rate. You must buy "CRYSTAL BALL" separately foryour Excel environment. It can't be bundled with this spreadsheet. The detaileduse of CRYSTAL BALL is well covered in the manufacturer's handbook andtherefore is not repeated in these guidelines.

Care is required when comparing the output of any existing in-house version ofthe De Waard models against this new Cassandra 98 spreadsheet. It is very easyfor errors and untested assumptions to be entered into a spreadsheet whichmight then perhaps be passed on from user to user and often compounded withother assumptions. Cassandra 98 has been written from scratch with a detailedre-evaluation of all assumptions, all of which are presented. Cassandra 98 isintended to be a standard, reference version of the De Waard approach for usewithin BP and its partners, until such time that a more consistent approach tocorrosion modelling becomes established within the oil industry. The activities ofthe NORSOK industry forum in Norway are making helpful moves in thisdirection.

INTRODUCTION

6This section gives enough information to allow experienced modellers to makea start. The subsequent section gives a more detailed description of all the inputand output parameters. The spreadsheets themselves also carry frequent "cellnotes". These are marked by a red dot in the top right hand corner of thosecells. Just double click on the cell to read the contents.

To carry out a basic calculation enter the following input values into the cellswith a white background:

Only the inputs in the preceding Table are needed for a straightforwardnumeric calculation. Some further information is required in order to carry outa probabilistic calculation using CRYSTAL BALL. The spreadsheet can easily becustomised by individual users to permit more extensive handling ofprobabilities:

"CASSANDRA 98" CORROSION PREDICTION SPREADSHEET

QUICK START

Input Parameters

Probabilistsic Inputs

P total gas pressure bar F7%CO2 CO2 in gas mole % (NB = v/v%) F8%H2S H2S in gas mole % (NB = v/v%) M8water composition ion ppm values ppm (NB = mg/ltr) A15-L15brine pH enter known value, F17

or enter "d", "o", or "x" to accept one of the calculated values shown in F18-F20(see Page 17)

T System temperature oC F24Ts Scaling temperature, enter

oC F25the calculated scalingtemperature, given in cellF26, or another known orpreferred value

d hydraulic diameter m M24U velocity m/s M25

Parameter Comments Units CellTable 1: Inputparameters for anumeric calculation


7

P F7 use a uniform distribution; set F7 as the maximum; set G7 as the minimum

%CO2 F8 use a normal distribution; adjust standard deviation as necessary

brine pH F17 must enter a known or a calculated value; use a normal distribution; adjust standard deviation as necessary

T F24 use a uniform distribution; set F24 asthe maximum; set G24 as the minimum

d M24 use a uniform distribution; set M24 as the maximum; set N24 as the minimum

U M25 use a uniform distribution; set M25 as the maximum; set N25 as the minimum

Output Parameters

1993 basic Vcor E32 the uncorrected corrosion rate for static conditions

1993 correction factors G32-K32 correction factors for pH, fugacity,scaling, and glycol

1993 corrosion rate G34 the corrosion rate for static conditions corrected for pH

1995 corrosion rate G39 the corrosion rate for dynamic conditions calculated from the components Vr and Vm in G37 and G38

93/95 merged corrosion G41 the average of the 93 and 95 rate corrosion rates; this cell enables

"CRYSTAL BALL" to combine the93 and 95 probability distributions

Parameter Cell Comment

Parameter Cell Comments

The resulting output parameters are described in Table 3. See p23 for a moredetailed description of how to interpret and use these values. Briefly, the 1993rate should be regarded as the minimum. Velocity effects may increase thisminimum rate as shown by the 1995 rate. Hence, the 1993 and 1995 rates willnormally give the lower and upper bounds on the expected corrosion rate. The1995 model is not accurate at low velocities and so it should be ignoredwhenever it falls below the 1993 value.

Table 2: AdditionalInput Parameters for aProbabilisticCalculation

Table 3: OutputParameters

8"CASSANDRA 98" CORROSION PREDICTION SPREADSHEET

The use of simple equations and the precision of the spreadsheet environmentcan lead one to think that the De Waard corrosion models are equally precise.However, this is not the case. The models are only valid over a certain range ofconditions, and even within this range a certain amount of data has beenignored if it doesn't fit the main trends. Each model appears to be constructedby obtaining a large number of corrosion rates over a range of conditions andthen finding an equation which draws a line passing close to the majority of thiscloud of points. The equations appear to be freely adjusted in order to give thebest fit to the data. The primary concern is to obtain a good fit to the data, ratherthan obtaining mechanistically rigorous equations. These are empiricalengineering models rather than scientific theories.

Neither the 1991 or 1993 De Waard papers give many precise details about therange of validity of the models. The 1995 paper does give a more thorough setof figures (see below) but still omits important features such as the type of brineused in the tests, and the elapsed time when the corrosion rates were measured.De Waard's very early work used a 0.1% NaCl solution [4] and this may wellhave been used in all the subsequent studies because his main focus has alwaysbeen low salinity water in gas lines. Table 4 shows the approximate ranges ofvalidity for the different parameters in the Cassandra 98 spreadsheet.

LIMITATIONS OF CORROSION PREDICTION MODELS

Table 4 : Range ofValidity of De WaardModels

P


9

The spreadsheet gives freedom to enter any value for most parameters. Whenthe input value is outside the approximate range of the 1991 and 1993 De Waardmodels then the text will turn RED in the cell as a warning. The predictedcorrosion rate may still be useful but the user must accept the additional risk ofgoing beyond the known limits of the correlations.

To develop the 1995 model [3] corrosion rates were obtained on the IFE flowloop (Kjeller, Norway) using a radiochemical technique to measure corrosionrates. Tests were carried out over 2-3 days but there is no information about thecorrosion rate profile over this time or when the final data point was taken. Datawere obtained for the following conditions.

- St-52 DIN 17100 steel (Cr 0.08%, C 0.18%) which is similar to ASTM A537Gr1

- 0.1, 3.1, 8.5, 13 m/s flow velocity- 20 - 90oC- 0.3 - 20 bara CO2

Certain inconsistencies in the data set were eliminated prior to developing themodel. These included:

- 0.1 m/s excluded- 13 m/s excluded when corrosion rate less than at 8.5 m/s- 90oC excluded- CO2 >6.5 bar excluded

Eventually 221 data points were used in the main correlation (Figure 2 ref 3).The main equations are specific to St-52 steel because, "The equations obtainedfor St-52 showed a complete lack of correlation for the other steels". The 15other steels were normalised steels and quench-and-tempered (Q&T) low alloysteels. These were examined over the following conditions to produce somemodified equations which take account of steel composition.

- 3.1, 8.5, 13 m/s flow velocity- 60oC- ca 2 bar CO2- pH 4,5,6

Limits of the 1995Model

10


For normalised steels a "Cr correction" and a "C correction" can be calculatedseparately and together. For Q&T steels the "C correction" has no effect and onlythe "Cr correction" is relevant. The Cassandra 98 spreadsheet does not includethe steel composition equations due to the poor correlations obtained whenfitted to the model.

Errors in matching equations to data points are defined in the 1995 paper by"coefficients of determination". This is a complicated statistical function rangingfrom 0 (poor correlation) to 1 (perfect correlation). It is not the same as the"correlation co-efficient" in regression analysis which scales from -1 to 1. The"co-efficients of determination" in the paper are 0.91 for the main St-52equations (after excluding the data that doesn't fit), 0.83 for the normalisedsteels, and 0.80 for the Q&T steels. For the main St-52 correlation thiscorresponds to a standard deviation of 25% on the predicted corrosion rate. Thisis the error given in this spreadsheet. Because of this error the predictedcorrosion rates are only shown to one decimal place. A "CRYSTAL BALL"probabilistic analysis gives a more realistic impression of the error on eachprediction.

The De Waard models were all developed using water-only systems in thelaboratory. The 1993 model is intended for nearly static, aqueous conditions andso for all but the lowest velocities (see page 77) it can be regarded as theminimum corrosion rate of a water wet region in a gas/water, water/oil, or awater/oil/gas system. Due to the different hydrodynamics in these field casessome assumptions are required in order to apply the 1995 model effectively.These assumptions will only affect the diameter and velocity values used asinputs in the model. The other inputs will be unaffected. Table 5 gives somesuggested assumptions. However, users are free to develop their ownapproaches to meet the demands of their own particular circumstances. Someof the issues involved in extrapolating the models to the field are discussed inmore detail on pages 27-28.

Errors on CorrosionRates

APPLYING THE MODEL TO DIFFERENT FIELD SITUATIONS

Units are specified for each parameter listed in this section. The same units areassumed in all the equations given below and throughout the Cassandra 98spreadsheet. The spreadsheet has a "units conversion box" at cell P5. The UNITSspreadsheet allows conversions between a wider range of units. The SALTSspreadsheet enables conversion between an ionic analysis of brine and the saltsrequired to prepare a synthetic analogue. The FUGACITY spreadsheet is a data-base used to calculate fugacity corrections at high total pressures.

P...total gas pressure (bara, i.e. bar absolute) INPUT cells F7 and G7

For a multiphase system this is simply the prevailing local P in the gas. For aliquid only system it is the P in the last gas phase which was in equilibrium withthe liquid, e.g. the separator gas in the case of a crude oil export line. For adownhole liquid pressurised above the bubble point then use the bubble pointpressure (Figure 4).

For a simple numeric calculation, enter the P value into cell F7. Cell G7 is thenunused. For a probabilistic calculation using "CRYSTAL BALL", set up a uniformdistribution for P with F7 set as the maximum and G7 as the minimum.


11

DETAILED DESCRIPTION OF THE SPREADSHEET

Total Pressure

Water only use pipe diameter and water velocity

Liquid/Gas use hydraulic diameter (see p 21)use true liquid velocity rather than nominal velocity(see p 22)

Water/Oil use pipe diameter and total liquid velocity(n.b. this ignores the possibility of water drop out orstratification which could lead to the water phase moving more slowly than the oil phase)

Water/Oil/Gas use a specialist multiphase program to calculate the wall shear stress or the "C factor" for the pipe system,then choose diameter and velocity inputs whichreproduce this hydrodynamic value.

Field Situation Recommended ApproachTable 5: Applying the1995 De Waard Modelto Field Situation

12


Figure 4: SchematicDiagram of an OilProduction System(downhole, separator,export)

%CO2...CO2 in gas (mole%, which is same as v/v%) INPUT cell F8

For a multiphase system this is simply the prevailing local %CO2 in the gas. Fora liquid only system it is the %CO2 in the last gas phase which was inequilibrium with the liquid, e.g. the separator gas in the case of a crude oilexport line. For a downhole liquid use the %CO2 in the gas formed at thebubble point. If this gas analysis is not available then use the CO2 dissolved inthe brine, the Henry's constant, and the bubble point pressure to back-calculatethe "effective %CO2" which would be required in the bubble point gas in orderto sustain the known level of dissolved CO2 (see box at cell P19). Indeed, thisprocedure can be followed for any region where the CO2 dissolved in the brineis known, but the gas analysis is unknown.

There may be occasions when it is helpful to apply parts of the Cassandramodel to a water which is in equilibrium with ambient air (e.g. for pHp redictions). The appropriate atmospheric inputs are P = 1 bara and%CO2=0.035 mole%. Remember that under these conditions the corrosionprediction from the model will only relate to the dissolved CO2 component andnot the dissolved O2.

For a probabilistic calculation using "CRYSTAL BALL", set up a normaldistribution for %CO2 using an appropriate standard deviation.

%CO2

13

pCO2...partial pressure of CO2 (bara) OUTPUT cell F9

fCO2...fugacity of CO2 (bar) OUTPUT cell F10

The non-ideality of gases means that at high total pressures the partial pressureis not an accurate description of the activity of a gas component. The fugacityis the true activity of the gas component. The 1991 and 1993 models use pCO2in the main corrosion prediction equations and then at the end apply a fugacitycorrection factor (Ffug) to account for fugacity effects. In Cassandra 98 theequations from the 1991 and 1993 models use fCO2 directly, therefore there isno need to use a fugacity correction factor (Ffug). The equations from the 1995model in Cassandra 98 also use fCO2 directly - instead of pCO2. Hence, inCassandra 98, it is fCO2 which is used as the primary parameter for all theequations which consider CO2 as an input.

Fugacity data from the work of R H Newton [5] are tabulated in theFUGACITY.XLS spreadsheet in the workbook. The Cassandra 98 spreadsheetuses the input values of temperature and total pressure to look-up the correctvalue of the fugacity co-efficient (g) in the FUGACITY spreadsheet,

fCO2 = pCO2 g

The R H Newton data are generally applicable to many pure gases. The datashow fugacity co-efficients as a function of "reduced temperature" and "reducedpressure",

where Tr is reduced temperature (dimensionless)T is the prevailing local temperature (oC)Tc is the critical temperature for the gas (from tables) (oC)

pCO2

fCO2

Tr =TTc

pCO2 =P.%CO2

100

14


where Pr is reduced pressure (dimensionless)P is the total pressure (bar)Pc is the critical pressure for the gas (bar)

Oilfields produce gas mixtures rather than pure gases. Hence, a difficulty arisesin deciding whether it is the Tc and Pc for methane or for CO2 that one shoulduse. In the Cassandra 98 spreadsheet, empirical values of Tc and Pc are assumedwhich allow the Newton model to agree with the CO2/methane mixed gasfugacity data in Figure 5 of the 1993 De Waard paper to 10%. In other wordsthe De Waard data are used to calibrate the Newton model.

The De Waard calibration data are valid up to 140oC and 250 bar. The Newtondata extends beyond these levels up to 300oC and 400 bar. The general trendsin the data will be accurate under these extreme conditions, however, theabsolute values are unchecked. For accurate work it will be necessary tocalculate or obtain the correct value of fugacity from elsewhere and thenmanipulate %CO2 in cell F8 by trial and error in order to obtain the correctfugacity in cell F10.

%H2S...H2S in gas (mole%, which is same as v/v%) INPUT cell M8

H2S is not included in any of the De Waard models. It is only used in theCassandra 98 spreadsheet in the calculation of solution pH by XLpH (seebelow). It can be ignored completely simply by entering zero.

Pr =PPc

CO2 31 73methane -82 45.8

empirical values used to correlate with De Waard data -37 56.7

%H2S

Tc Pc(oC) (bar)

Table 6: ReducedTemperature andReduced PressureValues for CO2 andMethane


15

It is by lowering the solution pH that H2S can potentially increase the corrosionrate, often in synergy with CO2. In practise, H2S tends to promote FeS surfacefilms which reduce the observed general corrosion rate but which increase thelikelihood of localised corrosion whenever the film fails. The CO2 generalcorrosion rate is often assumed as the worst-case localised corrosion rate for theregions with no FeS film.

An alternative approximate approach for handling the presence of H2S is toassume that every 1 mole% H2S has the same corrosivity as 0.01 mole% CO2.This rule of thumb assumes that 1 ppm dissolved CO2 and 200 ppm dissolvedH2S give roughly equal corrosion rates [6], and that H2S is roughly twice assoluble in water as CO2 for a given partial pressure [7].

pH2S ...partial pressure of H2S (bar) OUTPUT cell M9

pH2S = P . %H2S

water chemistry ..ion concentrations (ppm, same as mg/ltr) INPUT cells A15-L15

The water chemistry is used to calculate the solution pH (see below). Enter ppmvalues for Na+, K+, Ca2+, Mg2+, Ba2+, Sr2+, Cl-, HCO3-, SO42-, Fe2+, acetate.(NB enter the sum of all organic acids as acetate). Enter the %v/v value for glycolin cell L15. Use the SALTS spreadsheet to check that the total positive andnegative charges of the ions are roughly balanced. Any significant misbalance(e.g. >10%) may invalidate the pH calculation. Note that ion charges are handledin general chemistry by using the term "equivalents": 1 mole of positive chargesis equal to one equivalent; in other words 0.7 mole of Ca2+ ions is equal to 1.4equivalents of positive charge. Some further aspects of the acetate entry arediscussed on p.19.

T D S...total dissolved solids in water phase (ppm, same as mg/ltr) OUTPUT cell M17

pH2S

LIQUID PARAMETERS

Water Chemistry

Total DissolvedSolids

16


This is the sum of all the individual dissolved ions concentrations. TDS and[HCO3-] are used in the Oddo & Tomson pH calculation. TDS is also used toestimate the "salting-out" of CO2 as salinity increases. This will tend to reducethe concentration of dissolved CO2 and thereby reduce the corrosion rate [8].The box at X19 shows how to apply the salting-out correction. The procedureuses "Henry's Law" to calculate the solubility of a gas in a liquid.

pCO2 = KH XCO2

where KH is Henry's constant (bar/mole fraction)XCO2 is mole fraction of CO2 dissolved in brine.

The Henry's constant from the De Waard paper is only valid for a low salinitybrine (ca 0.1% NaCl). Therefore, by calculating the true Henry's constant for aspecific brine it is possible to apply a salinity correction to the De Waardcorrosion rate.

The salt-correction procedure first calculates the Henry's constant used by theDe Waard model (equation 28 from the 1993 paper- which is used in thederivation of equation 13 in the 1993 paper),

where KH is Henry's constant (mole/ltr bar)

Note that this KH equation from the De Waard paper has different units(mole/ltr bar) from those given earlier (bar/mole fraction). Much of theconfusion over Henry's constants arises from the wide and sometimes awkwardrange of units which can be used to express the parameter. For consistency inthis report the De Waard equation for an aqueous solution can be rewritten inorder to maintain KH in units of (bar/mole fraction)..

where KH is Henry's constant (bar/mole fraction)

log10 KH =1088.76T + 273

- 5.113

log10 KH = -1088.76T + 273

- 5.113

181000


17

The true Henry's constant is a function of both salinity and temperature(Appendix 1) so that,

Therefore, the salt-correction factor, Fsalt, is,

The best way to use Fsalt is to apply it to fCO2 to give an "effective CO2 fugacity".This "effective fCO2" will give the correct dissolved CO2 concentration whenused with the other equations in the Cassandra 98 model. The salt correctioneffect only becomes significant for TDS > 10% w/v.

pH ...brine pH control parameter INPUT cell F17

Enter the known pH value, or else enter a letter to accept one of the calculatedpH values given in cells F18, F19, or F20

m "d" or "D" will accept the De Waard distilled water pH

m "o" or "O" will accept the Oddo & Tomson brine pH

m "x" or "X" will accept the BP XLpH calculated value.

The accepted value is displayed in cell F21 for confirmation.

KHtrue (for 0 - 125 C) = (1.77 T + 47.1)

TDS10000

+ (45.2 T + 559)

KHtrue (for 125 - 200 C) = 250

TDS10000

+ 6500

Fsalt =KH

De Waard

KHtrue

Brine pH

18


When doing a probabilistic calculation using CRYSTAL BALL then a numericvalue of pH (either known or calculated) must be entered. Use a normaldistribution for the probability adjusting the standard deviation so as to coverappropriate minima and maxima.

pH(CO 2)...pH of distilled water containing CO2 OUTPUT cell F18

Equation (8) from the 1995 paper...

pH(CO2) = 3.82 + 0.000384 T - 0.5 log10 (fCO2)

fCO2 is used here rather than the pCO2 quoted in the original paper. Theequation is valid over 10-80oC. It gives the pH for pure water containingdissolved CO2 at the prevailing temperature and fCO2.

pH(act, Oddo) ..Oddo & Tomson calculated pH in brine OUTPUT cell F19

An empirical equation from reference 9...

+0.000000458 (T * 9/5 * 32)2 - 0.0000307 (P * 14.5)...

fCO2 is used here rather than the pCO2 quoted in the original paper. Theequation is valid up to 200oC and 1200 bar, but is inaccurate for low values of[HCO3

-]. The Cassandra 98 spreadsheet is set to give an error for pH(act, Oddo)if [HCO3

-] < 50 ppm.

pH(CO2)

pH(act)

- 0.477TDS

58500

1 / 2

+ 0.193TDS

58500

pH = log10HCO3

-[ ]fCO2 *14.5 *61000

+ 8.68 + 0.00405(T * 9 / 5 * 32)...


19

pH(act, XLpH) ...XLpH calculated pH in brine OUTPUT cell F20

XLpH is an Excel add-in function for calculating both pure water and brine pHswith no restrictions on salinities or component concentrations. It was developedby XTP, Sunbury using well documented code published by the US GeologicalSurvey (the "PHREEQ" model). The original version of XLpH [10] has since beenupdated to include pH2S as an input parameter. XLpH has been validated againstother pH models such as in CORMED and also against literature and recentlaboratory values.

XLpH uses the individual ion concentrations in cells A15-L15. The positive andnegative charges must be approximately balanced (see "water chemistry", p15,above). XLpH will automatically compensate for any small misbalances by addingNa+ or Cl- ions.

Enter the sum of all organic acids as acetate. Note that the pH of CO2-containing-brine will differ depending on whether the acetate is added in the form of sodiumacetate salt or acetic acid...

pH of 0.5 M NaCl / 300 ppm NaHCO3, 1 bar CO2, 25oC plus...

no acetate 6.8 mM Na acetate 6.8 mM acetic acid(i.e. 571 ppm) (i.e. 422 ppm)

5.53 5.41 4.17

XLpH assumes that the acetate value entered in cell K15 is acetic acid, becausethis is the worst case. If one wishes to assume Na acetate then zero should beentered for Ac and the molar equivalent of Na acetate should be added to the Naand Cl entries. Unfortunately a field water analysis will not directly revealwhether Na acetate or acetic acid should be used to simulate the water chemistry.This can only be established by making laboratory pH measurements under CO2saturation and comparing the results with the XLpH model.

Inclusion of the organic acid concentration will always improve the reliability ofa prediction. However, when organic acid data is not available it is possible tomake some rule-of-thumb approximations in order to aid progress. Organic acidsare typically present in formation water at 150ppm, the presence of organic acids is likely to make little difference to thecalculated pH and therefore corrosion rate. In such cases, an API water analysis(which omits organic acids) will often suffice. If the formation water is low inbicarbonate (

20


accepted pH ...confirmation of selected pH OUTPUT cell F21

This is confirmation of the pH value which has been accepted for the corrosionprediction equations.

T...temperature (oC) INPUT cell F24

The prevailing local temperature. When doing a probabilistic calculation usingCRYSTAL BALL then use a uniform distribution for the temperature : set F24 asthe maximum and G24 as the minimum.

Ts...selected scaling T (oC) INPUT cell F25

Enter a preferred value for the scaling temperature or enter "a" (or "A") to acceptthe calculated value shown in cell F26.

Researchers are still actively investigating the issue of what happens tocorrosion rates at temperatures above the scaling temperature. Previous workhas shown that sometimes the scale films are protective and can reduce thecorrosion rate, whereas sometimes the films are non-protective so that thecorrosion rate continues to increase. Choosing one or other of these optionscould on the one hand lead to significant under-design, and on the other handto significant over-design. Therefore, until the matter is fully resolved BPprefers to choose a middle course for design purposes. BP assumes that thecorrosion rate reaches a peak at the scaling temperature and remains on aplateau at the same value for higher temperatures. The Cassandra 98spreadsheet follows this approach. In order to achieve this outcome both fCO2and pH are set to a plateau for T > Ts.

T

Scaling T

IFE, Norwaydata

BP approach

De Waardapproach

Ts

Corrosion Rate

Temperature

Accepted pH

Figure 5: The PossibleEffects of HighTemperature Scaling onthe Corrosion Rate


21

Ts...De Waard calculated scaling T (oC) OUTPUT cell F26

Equation (13) from the 1995 paper,

This is obtained by setting log10 Fscale = 0 (i.e. Fscale = 1) in equation (13) in the1995 paper. Note that the equation above is expressed in oC and uses fCO2 ratherthan the oF and pCO2 used in the paper. The 1993 paper gives a similar equationto the 1995 paper but uses a factor of 0.67 in front of the log term instead of 0.44.

d...hydraulic diameter (m) INPUT cell M24

A diameter input value is only required for the velocity equations in the 1995model. It is not needed for the 1993 model. The 1995 paper actually uses"hydraulic diameter" rather than a simple pipeline diameter. Let Dp be pipelinediameter, and let Dh be hydraulic diameter, then,

..for gas/liquid pipelines, Dh < Dp

Dh = 4 A / S

..where A is the cross-sectional area of the liquid in the pipeS is the cross-sectional perimeter length of the liquid region (i.e. liquid/pipe + liquid/gas interfaces, see Figure 6)

..therefore for a pipeline full of liquid, Dh = Dp

De Waard CalculatedScaling Temperature

Ts =2400

6.7 - 0.44log10fCO2

- 273

Diameter

22


There is a box at cell P39 for calculating hydraulic diameters in gas/liquid lines.The ratio of the liquid and gas cross-sectional areas, or the ratio of the liquiddepth to the pipe radius, is required as an input parameter. Calculation of thisparameter is outside the scope of the Cassandra 98 spreadsheet.

When doing a probabilistic calculation using CRYSTAL BALL then use a uniformdistribution for the hydraulic diameter : set M24 as the maximum and N24 asthe minimum.

U...flow velocity (m/s) INPUT cell M25

A flow velocity input value is only required for the velocity equations in the1995 model. It is not needed for the 1993 model. There is a box at cell P5 whichenables calculation of flow velocity from pipe diameter and flow in liquid onlylines. The calculation is more complicated for the liquid phase in gas/liquidlines, therefore, the box at cell P39 should be used.

When doing a probabilistic calculation using CRYSTAL BALL then use a uniformdistribution for the flow velocity : set M25 as the maximum and N25 as theminimum.

cross-sectional perimeter lengthof the liquid region

Flow Velocity

Figure 6: Explanationof Parameter "S" in aGas/Liquid System

23


Vcor ...basic corrosion rate (mm/yr.) OUTPUT cell E32


The basic corrosion rate is adjusted by multiplying with the pH and occasionallythe glycol correction factors (FpH and Fglyc respectively). The application of eachof these is discussed below.

For the basic corrosion rate and the correction factors, the values reached at thescaling temperature are set to remain the same at higher temperatures. This isto ensure that the corrosion rate reaches a peak at the scaling temperature andthen remains on a plateau at the same value for higher temperatures (see Tssection above). Hence, the BP approach does take account of scaling at hightemperatures but doesn't use the De Waard scaling factor, Fscale, directly.

FpH ...pH correction factor OUTPUT cell G32

Equations (9) and (10) from the 1991 paper,

log10 FpH = 0.32 (pHCO2 - pHact)

for pHCO2 > pHact

where ...pHact is the actual pH of the brine which wets the pipewall...pHCO2 is the pH under the same conditions but in pure,

salt-free water

log10 FpH = - 0.13 (pHact - pHCO2)1.6

for pHCO2 < pHact

These equations show that as pHact rises, FpH will get smaller and so thecorrosion rate will fall.

Outputs : 1993 De Waard Model

Vcor...BasicCorrosion Rate

log10 Vcor = 7.96 -1710

T- 0.67 log10 (fCO2)

pH CorrectionFactor

24


These equations use pHCO2 instead of the "pHsat" used in the De Waard paper.pHsat is the pH at which a brine first becomes saturated with either FeCO3 orFe3O4 as a result of the steel corroding and building up dissolved Fe2+ in thesolution. The problem with pHsat is that it is difficult to define. Even the DeWaard paper only gives some approximate expressions for one particular brinecomposition (10% NaCl). Furthermore, there is serious doubt over the wholeconcept of a fixed saturation pH due to the observation of massivesupersaturation effects by IFE (Norway) and also within BP. Dissolved Fe2+

concentrations can often reach hundreds of ppm and can exceed the theoreticalsaturation values by orders of magnitude. Hence, pHsat is not a reliableconcept.

Until the pHsat issue is resolved BP prefer to use pHCO2 as an alternativereference point. It has the advantage that it is well defined and is valid over awide range of conditions. Therefore, a pure water system will give pHact =pHCO2 and so FpH = 1 in the BP approach. Certain conditions can make pHact< pHCO2 (e.g. high salinity, zero bicarbonate) and so FpH > 1. The presence ofbicarbonate will tend to make pHact > pHCO2 and so FpH < 1.

One way of reconciling these divergent approaches is to say that the direct DeWaard approach uses Fph to derive the initial corrosion rate in a brine beforecorrosion products build up and gradually reduce the corrosion rate until itreaches a steady state. This is the issue discussed in the 1993 De Waard paper.The BP approach on the other hand does not deal with initial corrosion ratesat all. It deals only with steady state corrosion rates and uses Fph to express theeffect of water composition on the steady state rate. This effect is not coveredin the direct De Waard approach. In essence BP have taken an equation fromthe direct De Waard approach and then adapted it for another purpose. Hence,overall, the two approaches are different but consistent.

Ffug ...fugacity correction factor OUTPUT cell J32


Fugacity CorrectionFactor

log10 Ffug = 0.67 0.0031 -1.4

T + 273

P

25


Ffug is not required in the BP approach because fCO2 is used in preference topCO2 throughout the calculation and so fugacity has already been accounted for.

Fscale ...scaling correction factor OUTPUT cell K32


where ... T > Ts otherwise Fscale = 1... Tscale is scaling temperature (defined above)

This factor is not used directly in the BP approach. It is included in thespreadsheet only for completeness.

Fglyc ...glycol correction factor OUTPUT CELL H32


log10 Fglyc = A (log10 W - 2)

where ... A is a constant = 1.6 to a first approximation... W is water content (%) of water/glycol mixture

BP only use this factor for cases without corrosion inhibitor. When a corrosioninhibitor chemical is used or is planned then BP assume that any effect of glycolis included within the corrosion inhibitor efficiency (normally 90%, but seediscussion on pages 42-48).

V'cor ...corrected corrosion rate (mm/yr.) OUTPUT cell G34

This is BP's preferred output from the 1993 DeWaard model. It is the basecorrosion rate multiplied by the FpH correction factor. Note that for the basiccorrosion rate and the correction factor, the values reached at the scalingtemperature are set to remain the same at higher temperatures. This is to ensurethat the corrosion rate reaches a peak at the scaling temperature and thenremains on a plateau at the same value for higher temperatures (see T(s) sectionabove). Hence, the BP approach does take account of scaling effects at hightemperatures but doesn't use the De Waard scaling factor, Fscale, directly.

log10 Fscale = 24001

T + 273-

1Tscale + 273

Glycol CorrectionFactor

CorrectedCorrosion Rate

Scaling CorrectionFactor

26


The 1995 De Waard model is derived in a different fashion from the 1993 model,in particular it does not use the idea of correction factors applied to a basecorrosion rate. Instead, the overall corrosion rate is calculated from twocomponents : the reaction rate Vr and the mass transfer rate Vm.

Vr ...reaction rate (mm/yr.) OUTPUT cell G37


Vm ...mass transfer rate (mm/yr.) OUTPUT cell G38

Equation (10b) from the 1995 paper,

Vcor ...corrosion rate (mm/yr.) OUTPUT cell G39


where Vcor is overall corrosion rateVr is reaction rateVm is mass transfer rate

Outputs : 1995 De Waard Model

Reaction Rate

log10 Vr = 6.23 -1119

T + 273+ 0.0013 T + 0.41log10 (fCO2 ) - 0.34pH act

Mass Transfer Rate

Vm = 2.45U0.8

d0.2fCO2

Overall CorrosionRate

1Vcor

= 1Vr+

1Vm

27


Vcor ...merged corrosion rate (mm/yr.) OUTPUT G41

The merged rate simply takes the average of the 1993 and 1995 values. Thisallows CRYSTAL BALL to combine the probability distributions for the 1993 and1995 rates so that one can see the lower and upper bounds on the expectedcorrosion rate.

The 1993 rate is regarded as the minimum. Velocity effects may increase thisminimum rate as given by the 1995 value. The 1995 model is not accurate at lowvelocities so it is ignored whenever it falls below the 1993 value, and then themerged rate is the same as the 1993 rate.

The validity of any corrosion prediction model depends on how well it agreeswith the measured corrosion rates in the field. However, the comparison is notalways straightforward. This is because the models are developed from wellcharacterised, clean and stable systems in the laboratory, and they are beingapplied to partially characterised, dirty, and variable systems in the field wherethe full operating history is not always known. This is no criticism of fieldactivities. It is simply a fact of life of operations where the aim is to producehydrocarbons, not to generate completely rigorous corrosion data.

The discrepancies between the models and r eal field corrosion data which doexist arise because there are parameters in the field which the model can not takeaccount of effectively, or at all, e.g. surface coatings (scales, corrosion products,biomass), crude oil wetting, local hydrodynamics, weld metallurgy.

The industry generally regards the De Waard model as conservative compared tothe field, i.e. it over-estimates the field corrosion rate. Much of this opinion isbased on anecdotal and semi-quantitative evidence - often not published in theopen literature - but it is confirmed by the occasional formal presentation [12].

1993 & 1995Merged CorrosionRate

Vcormerged =

Vcor1993 + Vcor

1995

2

COMPARING OUTPUT FROM THE Cassandra 98 MODEL WITH FIELD DATA

28


BP is currently compiling a database of field corrosion data from a variety ofsources which will be used to assess the Cassandra 98 spreadsheet presentedhere.

In the meantime Table 7 gives a comparison of the Cassandra 98 spreadsheetagainst new laboratory data; data which were not used in compiling themodel. The final column shows whether the observed corrosion rate falls within15% of the range encompassed by the 1993 and 1995 models and there is someagreement. However, the discrepancies show the pitfalls in trying to push theaccuracy of the model too far. It is best used to gain order of magnitudeestimates of corrosive situations rather than absolute corrosion rates to severaldecimal places.

Table 7: Comparisonof Model Predictionswith Laboratory Data

BP 1993 0.1% NaCl, 3 litre flow loop (15 mm ID)25 1.9 1 5 1.1 5.8 yes25 1.9 0.27 2.2 0.5 1.9 yes35 1.9 0.27 3.4 0.7 2 noBP 1992 Forties brine, beaker test and 5 litre flow loop (15 mm ID)50 0 0.88 2.5 1.5 0.1 no50 1.2 0.88 2.5 1.5 3.2 yesCAPCIS Flow Project Forties brine, flow loop (10 mm ID)25 3.2 1 1.8 0.6 3.3 yes50 1.1 0.88 3.8 1.5 3.2 yes50 1.7 0.88 4.1 1.5 3.9 yes50 2.5 0.88 2.5 1.5 4.4 yes50 3.2 0.88 4 1.5 4.7 yesCAPCIS Flow Project 3% NaCl, flow loop (10 mm ID)25 3.2 1 6 1.2 7.7 yes50 3.2 0.88 12.1 3.1 9.2 no70 3.2 0.88 17.4 5.3 8.4 no50 1.1 0.88 6.8 3.1 4.8 no50 1.7 0.88 7.3 3.1 6.4 yes50 2.5 0.88 8.6 3.1 8.1 yes

corrosion rate (mm/yr.)T U fCO2 observed 93 95 correct?(oC) (m/s) (bar) model model

29


"Henry's Law" describes the solubility of a gas in a liquid,

pCO2 = KH XCO2

where KH is Henry's constant (bar/mole fraction)XCO2 is mole fraction of CO2 dissolved in liquid

Henry's constants are dependant on both temperature and salinity and they areeasily found for CO2 dissolved in pure water [e.g. 13]. The data for brines is lessextensive [14-16]. Figure 7 is compiled using data from all these sources. Thereduced number of points at higher salinity are still sufficient to show that thedata in the 0-10% region can be reliably extrapolated up to ca 30% NaCl. Notethat the 16 and 31% data at 75 and 100oC are actually for MgCl2 in the originalpaper but have been plotted in Figure 7 at the equivalent ionic strength of NaCl.

APPENDIX 1 : "Henry's Law" Constants for CO2 Dissolved in Brine

0

2000

4000

6000

8000

10000

12000

14000

0 5 10 15 20 25 30 35

[NaCl] %w/w

Kh (

bar/

mol fr

ac)

20017515012510075503010

T (oC)

The lines in this figure can be represented by the following equations (to within15%),

Figure 7: Henry'sLaw Constants as aFunction of Salinity

30


where KH is Henry's constant (bar/mole fraction)

Cell AD31 in the spreadsheet uses these equations to calculate the true Henry'sconstant for the input values of T and TDS.

KH (for 0 - 125 C) = (1.77 T + 47.1)TDS

10000+ (45.2T + 559 )

KH (for 125 - 200 C)250TDS

10000+ 6500

31

The Use of Corrosion PredictionModels During Design by D M E Paisley

The value and purpose of predictive corrosion rate models should be neitheroverlooked nor exaggerated. The models (of which CO2 models are oneexample) are tools for the Materials Engineer to use during materials selectionstudies. The models help to quantify the corrosion risk and to help assess theimpact of various process or production scenarios. However, corrosion rateprediction models should always be used in conjunction with other tools such aslife cycle costing as well as previous operational experience if the final materialsselection is to offer the optimal balance between cost and reliability. As eachproject will have unique economic factors, materials selection should reflect theseand the economic assessment will be as important as the corrosion modelling inthe selection of the final materials. In-depth coverage of techniques such as lifecycle costing and estimating values are beyond the scope of this document butboth techniques are briefly covered in a previous publication [17].

Over the past few years, several design guidelines have been issued by BP fordealing with CO2 corrosion risks. Each document deals with a specificapplication. This more general document summarises all previous guidelines butcan not deal with the specific issues to the level of detail possible in theindividual guidelines. The previously issued guidelines are listed in Table 8.

Introduction

THE USE OF PREDICTIVE MODELS DURING DESIGN

32

Table 8: PreviouslyIssued DesignGuidelines

A corrosion philosophy for the transport of wet oil and multiphasefluids containing CO2

This was the first undertaking in recent years to document a BP approach todefining internal corrosion risks and the basic approach is still followed. Itrecommended the use of the de Waard and Milliams model to predict in-situcorrosion rates along with a 90% corrosion inhibitor efficiency. Much of thework is still valid but it is in the areas of high temperature scaling, corrosioninhibitor efficiencies and impact of various flow regimes that the new guidelines

Report Title Authors Report Number Issue Date

A corrosion philosophy for the I D Parker ESR.93.ER.013 1/3/93transport of wet oil and J Pattinsonmultiphase fluids containing A S Green.CO2

A corrosion philosophy for I D Parker ESR.94.ER.016 28/8/94the transport of wet J Pattinsonhydrocarbon gas containing A S Green.CO2

Assessment of a top of line D Paisley Branch Report 5/10/92versus bottom of line corrosion J Pattinson No 124 421ratio for use in the design of S Websterwet natural gas pipelines

The application of pH D Paisley ESR.95.ER.042 10/4/95moderation as a means of corrosion control for wet gas pipelines

The effects of low levels of D Paisley ESR.95.ER.073 22/6/95hydrogen sulphide on carbon R Gourdindioxide corrosion: A review of industry practice and a guideto predicting corrosion rates


33

differ. Most of the recommendations made in these guidelines have beenreproduced or superseded in the present document and therefore the originalguidelines are redundant.

A corrosion philosophy for the transport of wet hydrocarbon gascontaining CO2

This was a companion document to the guidelines on wet oil and multiphasesystems. The basic approach was similar but this document dealt with thespecific wet gas application. Most of the recommendations made in theseguidelines have been reproduced or superseded in the present document andtherefore the original guidelines are redundant.

Assessment of a top of line versus bottom of line corrosion ratio for usein the design of wet natural gas pipelines

Wet natural gas pipelines operating under stratified flow have two distinctcorrosion environments : (a) the bottom of line which is continually wetted bycondensed water, hydrate inhibitor and hydrocarbons, and (b) the top of linewhich is wetted intermittently by condensing liquids. The corrosion rate at thetop of the line is lower than that at the bottom due to the more limited exposureto corrosive species. Predicting this rate is done by predicting the bottom of linerate using models in the normal way and applying a moderating factor for thetop of line rate. Up to 1992, BP used a factor of 0.3, i.e. the top of line corrosionrate was predicted to be 30% of the bottom of line rate. When inhibitors areused to control the bottom of line rate, the top of line corrosion rate becomesthe limiting rate as inhibitors are assumed not to protect against condensingcorrosion. This report reviewed the top of line factor and recommended theadoption of a moderating factor of 0.1. For inhibitor efficiencies up to 90%, thetop of line corrosion rate is therefore not the limiting rate. This approach is nolonger valid since BP have moved away from the direct use of inhibitorefficiencies, as described later in this report. However, the assumption that topof line rates are 1/10th of the predicted uninhibited bottom of line rates can stillbe used. For applications were the 'top of line' corrosion rate is the faster rate(using the 0.1 moderating factor) then a more detailed evaluation should becarried out. Such a scenario does not lend itself to the use of simplifiedguidelines.


34

The application of pH moderation as a means of corrosion control forwet gas pipelines

This technique is not widely applicable but may find niche applications inhighly corrosive wet gas lines utilising recycled glycol for hydrate control. It iscovered in more detail on p75 but if this technique is of interest the fullguideline document should be reviewed.

The effects of low levels of hydrogen sulphide on carbon dioxidecorrosion: A review of industry practice and a guide to predictingcorrosion rates

This document summarised how low levels of H2S influence corrosion ratesdominated by CO2. The conclusion was that H2S at levels below the NACEcriteria for sulphide stress corrosion cracking (ref MR0175, NACE Publications)reduces general metal loss rates but can promote pitting. The pitting proceedsat a rate determined by the CO2 partial pressure and therefore CO2-basedmodels are still applicable at low levels of H2S. Where the H2S concentration isgreater or equal to the CO2 value, or greater than 1 mole%, the corrosionmechanism may not be controlled by the CO2 and therefore CO2 based modelsmay not be appropriate.

Summary of Previous Guidelines

In summary, the old guidelines are generally still applicable. What has changedis BPs views on the reliability and performance of corrosion inhibitors as wellas the availability of updated models incorporating flow affects. The oldguidelines defined a corrosion inhibitor efficiency of 90% with no scope forvariation. There were also stringent velocity restrictions for use undermultiphase conditions which restricted the energy of slug flow to below 20 Pa,later raised to 100 Pa. In light of favourable field data, this approach is nowseen as too pedantic and inhibitor availabilities are seen as a better way ofdescribing the role of inhibitors. These differences in approach are covered inmore detail in the following sections. Furthermore, the corrosion rateprediction model (p5-30) does not cover some aspects that are important duringdesign and these are covered in the next section.


35

The modelling approach outlined in this document deals with all the inputs(mole% CO2, temperature etc.) on a deterministic basis. However, each inputwill have a level of uncertainty associated with it and this can have importanteffects on the outcome. One way to deal with this it to calculate a range ofoutput values, (in this case the predicted corrosion rate) across the whole rangeof input values. Where the model is dealing with several inputs (temperature,pressure, CO2 mole %, pH, scaling factor), this can be time consuming. Also, thevalue of these inputs will not all vary in a uniform manner. Some will behaveuniformly while others may behave in a normal or log-normal manner.

Calculating the impact of all these variables is time consuming, unless aprogramme such as Crystal Ball is used. This is an add-in to Excel and handlesthe variability by performing a Monte Carlo analysis. Any number of iterationscan be performed and the output is displayed in terms of a probability, ratherthan as a discreet value. In general, a minimum of 1,000 iterations, involving tensof thousands of individual calculations are required to show the effects of thevariability in input data. A modern PC can perform such a task in a minute ortwo.

The important factors to consider are the range and type of distribution assumedfor each variable. If process data are available, this will form an ideal basis fordetermining the range and type of distribution but if this is lacking, someassumptions will have to be made.

Using distributions to define variables in a predictive model can have significanteffects on the outcome.

Engineering design traditionally uses worst case inputs so that the final designwill be safe under all foreseeable combinations of events. This approach hasalso been adopted when predicting corrosion rates, where pressure andtemperature etc. are used as inputs to the models. In the past this approach wasthe only viable one as predicting the enormous range of possible outcomes forall variables would have been too time consuming but it can result in substantialover-design. Metal loss corrosion processes do not lead to sudden failure due toa combination of variables over short time periods (unlike high pressure whichcan lead to an instantaneous failure) but rather reflect a combination of varying

Worst Case Design

Important Factors not Covered by the Corrosion Model

The ProbabilisticApproach toPredictive Modelling


36

conditions over a longer time period. Using the worst case values is thereforenot a sensible approach, if a range of more realistic values can be handled.

In defining a range of likely operating variables such as temperatures andpressures, the design values will form the maximum for the respectivedistributions but lower values should be included. Defining this range willrequire inputs from the Process and Reservoir Engineers. Due to the nature ofthe uncertainty, such that all values within the range are as likely as each other,Uniform distributions are probably the most appropriate for these variables.

The yield strength and wall thickness of linepipe are other examples of the typeof variables that can be treated in this manner. The linepipe properties areimportant if using corrosion models to calculate mean time to failure. Ratherthan using the minimum values for each, based on the specified material andthe variation allowed within the specification, typical distributions can bedefined for each value. Such variables tend to be distributed normally arounda mean with the specified minimum properties defining a lower bound.

Many variables in corrosion rate predictions, such as the level of CO2 in the gasphase, are based on best guess or on limited well test data. No attempt ismade to define the uncertainty in these data and this is a major limitation ofdeterministic modelling. In defining the distributions of such variables, themean value should be based on the best guess or well test data in a similar wayto the deterministic approach. However, a range of possible values should beconsidered. In the absence of any other information, the distribution of valuesis likely to be symmetrical around the mean with the greatest probabilityassociated with values close to the mean. The Normal distribution is a familiarexample of this type and should be used.

It should be noted that using a symmetrical distribution, such as a Normaldistibution, does not correspond to using a single value equal to the mean ifthe variable under consideration has a non-linear relationship with the outcome.For example, the corrosion rate prediction model used by BP states that:

Non-LinearRelationships


37

Therefore, the corrosion rates associated with the CO2 partial pressure values inthe Normal distribution that are greater than the mean value are closer to themean corrosion rate than those associated with the values below the mean CO2partial pressure. In other words, defining symmetrical distributions for variableswhose influence is described by a power < 1 produces a non-symmetricaldistribution of outcomes (predicted corrosion rates). The mean value of thisdistribution will be lower than the single value calculated using the mean of theinput variable.

The same applies to all symmetrical distributions, including Uniformdistributions. In the previous section on 'worst case design', the uncertaintiesregarding operating temperature and pressure were discussed. In both cases,Uniform Distributions were used to define the range of possible values. Incorrosion rate modelling, both these inputs have non-linear relationships withthe outcome (predicted corrosion rate). The effect of pressure is moderated bya fugacity coefficient related to the non-ideality of CO2. Therefore, consideringa range of pressures distributed symmetrically around a mean value will tend toreduce the predicted corrosion rate.

The effect of temperature on predicted corrosion rates is strongly non-linear. Athigher temperatures, the role of protective corrosion products or scales can beimportant. There is a great deal of uncertainty in the effects of these scales butthe bounds of the expected values can be defined using existing models. Oneapproach would be to use a log normal distribution, defined as follows:

1. The de Waard & Milliams unscaled rate (upper bound), 2. The de Waard & Milliams fully scaled rate (lower bound), 3. A modal value equivalent to the standard BP approach that uses the scaling

temperature to calculate the corrosion rate for all temperatures above this.

Again, the outcome of considering a range of temperatures symmetricallydistributed around a mean will tend to be a lower corrosion rate estimation thanfound by calculating a single value at the mean temperature.

Each input into a corrosion rate prediction should be considered and a range ofpossible outcomes defined. By consideration of the way in which the value mayvary in practice, a distribution function can also be defined. This may have tobe done subjectively but the following basic rules offer some guidance. In thefollowing examples, distributions are shown that have been used in the CrystalBall software.

Summary of Inputsto a Monte CarloAnalysis

Corrosion Rate CO2 partialpressure( ) 0.67


38

1. Where variations would be due to nature, such as the difference in CO2levels around the field, a Normal Distribution should be used with a meanequivalent to the best guess. Figure 7 shows an example of a NormalDistribution describing the expected variation in CO2 levels, centredaround a mean of 5%.

Figure 7: An Exampleof a NormalDistribution for theconcentration of CO2in a gas. The MeanValue is 5 mole% with arange of 3 to 7 mole%.


39

2. Where an input may vary over a wide range but would be expected to beskewed around the 'best guess' or predicted value, a Log NormalDistribution should be used. The effects of high temperature scalingwould be an example of this type of distribution, or the pit depth at whichinhibitors fail to control corrosion. Figure 8 shows the Log NormalDistribution used to describe the critical pit depth with a modal value of 8mm and a range of 5 to 12mm.

Figure 8: An Exampleof a Log NormalDistribution describingthe critical pit depth.


40

Figure 9: An Exampleof a UniformDistribution Describingthe Flowline OperatingPressure

3. Where a value may occur equally often within the defined range e.gflowline operating pressure, a Uniform Distribution should be used, i.e.all values are equally likely to occur. Figure 9 shows how a range offlowline operating pressures can be described. In this case the range of1,000 to 1,200 psi has been used.

Table 9 summarises the assumptions used in a recent probabilistic study intomean time to failure, based on CO2 corrosion risks. As the study looked atfailure mechanisms as well as corrosion rates, some of the factors apply to thelinepipe steel while others apply to the CO2 prediction model. The 'StandardValue' corresponds to the value that would be used in a deterministic study.The Table does not attempt to fully define the distributions in a statistical sensebut more information is available from the authors if required.


41

Linepipe Wall thickness e.g. 0.75" Mean = 0.75" NormalSD = 0.01

Linepipe Yield Stress SMYS Mean = 70 ksi Normale.g. 65 ksi SD = 2.5 ksi

Linepipe Flow Stress - - - - 1.15 x Yield Stress Normal

Fluids CO2 Content 5 mole% Mean = 5% NormalSD = 0.72

Fluids Temperature 110oC 85 - 110oC UniformFluids Pressure 1,200 psi 1,000 - 1,200 psi Uniform

Corrosion Water pH Cormed * Cormed * Normalmodel prediction 0.25 unitsCorrosion Corrosion rate >Rate at scaling Unscaled to Log Normalmodel scaling ToC temperature fully scaled

Inhibitor Inhibitor 90% 65 - 95% Log Normalefficiency availabilityInhibitor Critical pit depth 8 mm 5 - 12 mm Log NormalefficiencyInhibitor Inhib. effic. > 0% 0 - 90% Uniformefficiency critical pit depth

Table 9: Summary ofVariables Modelled,the Values that wouldbe Assigned Using aStandard Approach,and the Range ofValues Used in theExample Study

Component Variable 'Standard Range Used Distributionin study Value'

Note * Cormed is a software programme which can predict in-situ pH values ofoilfield brines.

Figure 10 shows the output from a Monte Carlo simulation, using 20,000iterations to determine the distribution in outcomes (predicted corrosion rate)due to the variation in inputs detailed above. The most likely corrosion rate iscirca 1 mm/yr. While there is a possibility that higher or lower rates occur, theprobability of such rates decreases the further they are from the most likelyoutcome.


42

This section represents a significant shift from previous BP recommendationsand therefore is covered in some detail.

The guidelines on the reliance to be placed on corrosion inhibitors presentedhere have been based on experience gained with continuous injection systems.The success of batch treatments with corrosion inhibitor is less welldocumented and generally this approach to corrosion control is less reliable.These guidelines should therefore not be used when designing systems that willbe protected with batch treatments - this effectively rules out their use forthe majority of downhole applications. Instead, it is recommended thatrelevant operational experience with batch treatments is sought beforedesigning on the basis of batch inhibition. The authors will be able to assist insourcing relevant operational experience.

Previous BP guidelines have dealt with the affect of corrosion inhibitors on CO2corrosion by assigning a corrosion inhibitor efficiency. This described theextent to which an inhibitor reduced the predicted rate and a figure of 85% wasoriginally used, later raised to 90%. This was despite laboratory observationsthat showed inhibitors could reduce corrosion rates by 95% or more. However,it was accepted that in the field, inhibitor is not delivered at the recommendeddose rate for 100% of the time and therefore a degree of conservatism isnecessary when estimating the benefits of inhibitors.

Frequency Chart

mm/yr

.000

.028

.057

.085

.113

0

565

2260

0.00 1.13 2.25 3.38 4.50

20,000 Trials 313 Outliers

Forecast: Predicted Corrosion RatesFigure 10: TypicalOutcome of the BPCorrosion Rate ModelRun Using aProbabilistic Approach

Effect of Corrosion Inhibitors

Inhibited CorrosionRates

Applicability of theGuidelines


43

One major limitation with inhibitor efficiencies is that it allows no considerationof the effects due to increased dose rates or the development of better chemicals.It is well known that increasing the dose rates of corrosion inhibitors up to acertain level reduces the corrosion rate. Figure 11 shows the relationshipbetween dose rate of inhibitor and corrosion rate on corrosion coupons atPrudhoe Bay. Clearly, the inhibitor efficiency is not a constant value andincreasing the inhibitor concentration (or changing the chemical for a moreefficient one) enables lower corrosion rates to be achieved.

A second major limitation with using a single value for corrosion inhibitorefficiencies is that they are unlikely to be constant across the whole range of fieldconditions. CO2 corrosion models can handle input values across a wide rangeand moderation factors have been developed over the years to reduce theconservatism due to the extrapolation of the data set used to develop the model.However, no such moderation factors have been developed for corrosioninhibitor efficiencies and by applying a blanket efficiency, it is assumed they areconstant across the range of applications.

BP is fortunate in having one of the more corrosive fields in Prudhoe Bay. Thisfield also lends itself to effective corrosion monitoring due to the use of above-ground flowlines and there is a great deal of data on inhibited corrosion rates.There is a good relationship between observed corrosion rate and inhibitorconcentration, as shown in Figure 12. In this Figure, the effect of the increaseddose rate of chemical between January 1994 and September 1996 can be seen inthe increased efficiency of the chemical, based on the predicted corrosion ratesusing BPs CO2 corrosion rate prediction model.

1

10

100

40 50 60 70 80 90 100 110 120 130 140

Corrosion Inhibitor Concentration - ppm

1/co

rros

ion

rat

e (y

ears

per

mm

)

Figure 11: TheImprovement inPerformance of aCorrosion Inhibitorwith IncreasingConcentration


44

In Figure 12 all efficiency values lie within the range 98.6% and 99.7%,apparently extremely good performance but in January 1994 only 40% of theflowlines at PBU had acceptable rates of corrosion, defined as corrosion ratesunder 2 mpy (0.05 mm/yr.) based on corrosion probes - see Figure 13. Theimprovement in performance from January 1994 onwards correlates with theincrease in average dose rates shown in Figure 12.

0

20

40

60

80

100

120

140

Jan-94 May-94 Sep-94 Jan-95 May-95 Sep-95 Jan-96 May-96 Sep-96

Date

Ave

rage

Cor

rosi

on I

nh

ibit

or C

once

ntr

atio

n -

pp

m

98.20%

98.40%

98.60%

98.80%

99.00%

99.20%

99.40%

99.60%

99.80%

'Ave

rage

' C

orro

sion

In

hib

itor

Eff

icie

ncy

Corrosion inhibitor concentration

Corrosion inhibitor efficiency,defined using BP's model

Figure 12: TheRelationship BetweenCorrosion Inhibitor DoseRate and ObservedEfficiency at PrudhoeBay


45

Prudhoe Bay was constructed before the development of the earlier BPguidelines on CO2 corrosion, but if their flowlines were to be constructed todayusing the same materials and corrosion allowances, it would infer a corrosioninhibitor efficiency of approximately 98%. As PBU have now demonstrated thatcorrosion control of their system is possible it is clear that inhibitors can beeffective under highly corrosive conditions. This in turn indicates that either:

m Higher inhibitor efficiencies can be assumed in more aggressiveconditions, or

m Corrosion inhibitor efficiencies are not the correct way to describe the roleof inhibitors in corrosive service.

The former premise does not lend itself to design as it would require a slidingscale of inhibitor efficiencies and the field data is not available to allow this to beproduced. The latter is the belief of several oil companies who do not useinhibitor efficiencies, preferring to use a design corrosion rate for inhibitedsystems in the range 0.1 to 0.3 mm/year. For mildly corrosive conditions(~1.0mm/year) the use of an efficiency of 90% generally works well. However,for highly corrosive conditions (~10mm/year) it would result in a conservativeestimate of the inhibited corrosion rate. This adds weight to the argument thatthe role of corrosion inhibitors can not be described by efficiencies.

Percentage of Production Lines with Corrosion Under Control

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96

2 < CR < 5 CR >5 mpy1 < CR < 2 CR < 1 mpy< 2 mpy by Qtr

Note

Covers 3 phase productionlines >6" in diameter with WLCsincluding LDFs, LP, HP andGHX.

Figure 13: HistoricalRecord of CorrosionRates in PBU FlowlinesShowing ImprovingPerformance SinceJanuary 1994


46

BPs data indicate that inhibited corrosion rates of 0.1 mm/year are possibleunder optimum conditions of high inhibitor dose rates and optimised chemicals.This is confirmed with inspection data from PBU where flowlines which havebeen effectively inhibited have pipewall corrosion rates of less than 0.1 mm/yr.

In general, inhibitors require free and regular access to the steel surface to beeffective. Anything that interferes with this will reduce their effectiveness to lowor negligible levels. Examples of low or stagnant flow situations are vessels,instrument and drain piping and tanks. Historically, inhibitors have not beenassumed to work well in these environments and other corrosion controlmeasures are used, such as coatings and/or cathodic protection.

Inhibitors also perform poorly in low velocity pipework and pipelines,particularly if the fluids contain solids such as wax, scale or sand. Under suchcircumstances, deposits inevitably form at the 6 oclock position, preventingtransportation of the inhibitor to the metal surface. Flow velocities belowapproximately 1.0 m/s should be avoided if inhibitors are to provide satisfactoryprotection and this will be critical in lines containing solids.

The figure of 1.0 m/s is a rule-of-thumb which has been used in the industryfor many years. However, it is now possible to calculate the velocity moreaccurately, using an approach developed by the 'Corrosion in MultiphaseSystems Centre' at Ohio University [18]. The work agrees with the rule of thumbfor most black oil systems but allows more accurate quantification if theminimum velocity is restrictive.

The costs associated with corrosion inhibition are driven by the volume ofchemical used per annum and the chemical cost. There may be some incidentalcosts associated with the provision and maintenance of injection equipment butincreasingly this is being handled by the chemical suppliers and is thereforecovered by the chemical cost.

In general, inhibitors are most attractive when protecting long lengths ofpipeline while they are rarely cost effective when protecting short runs ofprocess piping. The dose rates required are dependent on factors such as liquidthroughput, CO2 partial pressure, pH and flow regime. Dose rates are notdependent on the length of pipeline or pipework being treated and

Operating CostsAssociated WithCorrosion Inhibition

Applications WhereInhibitors Are LessThan Fully Effective


47

Beatrice 40Brae 10Bruce * 46Forties Pipeline * 26Magnus 20Miller * 35Nelson Enterprise * 17Scott Amerada Hess * 9AVERAGE 25

Table 10: Dose Ratesof Corrosion Inhibitorsinto Several North SeaExport Pipelines,Based on Total FluidVolumes

Field Dose Rate (ppm)

Note * - These fields deploy concentrated corrosion inhibitors to improvelogistics offshore. The quoted dose rates correspond to the standard product,manufactured by the same supplier.

At Prudhoe Bay the field-wide average corrosion inhibitor injection rate is 110ppm, with maximum rates of 250 ppm in certain flowlines, based on waterproduction (typical water cuts are 50%). These rates reflect the rapid corrosionexperienced in some PBU flowlines in recent years.

The determination of dosage rates in gas systems is not as straightforward as forliquid filled lines. The three methods which are commonly used to do this are:

1. Based on Gas Flow. This is the most commonly used method and a commonrule of thumb is to apply 1 pint of inhibitor to every 1 million standard cubicfeet of gas (1 pint/MMscf). Actual values are found to vary enormously in therange of 2 and 0.05 pints/MMscf of gas.

2. Based on the Water Content in the Pipe Line. This is the method favouredby corrosion engineers as it usually indicates a very low requirement forinhibitor. It is common to assume a dosage of 200 ppm of chemical in thewater. This method will often give erroneously low values, especially when the

therefore the same operating cost is incurred in protecting 10 metres of pipeworkas is required to protect 20 km of flowline. Corrosion resistant materials arelikely to offer lower life cycle costs for pipework while carbon steel plusinhibition tends to be the cheapest method of constructing and operatingflowlines [19].


48

water content is very low and/or the pipeline is very long. This is becausethe volume predicted will be too low to allow a film to be build up over theentire surface of the pipe.

3. Based on the Formation of a Protective Film. This is probably the leastused method but one whch provides a good check on the values obtainedfrom the first two methods. Typically it is the volume required to form a0.05mil (1 micron) film over the entire internal surface of the pipe. Thisvolume is then applied continuously on a daily basis. If the product is to beapplied as a batch treatment the volume is increased by a factor of ten (x10).

In practice it is sensible to do all three calculations and to use the greatestvolume as the starting point. This should hopefully be the most conservativevolume re q u i red. Again, highly corrosive duties associated with hightemperatures or CO2 partial pressures will tend to require dose rates towardsthe upper end of this scale.

Chemical costs vary from supplier to supplier and may be tied in with theprovision of other services such as corrosion monitoring. However, for thepurposes of life cycle costing a chemical cost of US$8 per US gallon isreasonable. On this basis, corrosion inhibitor costs 0.84 cents to 8.4 cents perbarrel at inhibitor dose rates of 25 to 250 ppm. There will also be costsassociated with monitoring and inspection. These aspects are beyond the scopeof this document but are covered in detail in SELECTING MATERIALS FORWEALTH CREATION: A Material Selection Philosophy Based On Life CycleCosts [17].

Due to the limitations of corrosion inhibitor efficiencies as a design tool, theinhibitor availability model has been adopted. This approach can be used todefine a corrosion allowance as follows:

C At o t a l = CAi n h i b i t e d (x years @ 0.1 mm/yr.) + CAu n i n h i b i t e d (y years @ uninhibited rate)

This approach assumes that the inhibited corrosion rate is unrelated to theuninhibited corrosivity of the system and all systems can be inhibited to 0.1mm/year. The approach also acknowledges that corrosion inhibitor is notavailable 100% of the time and therefore corrosion will proceed at theuninhibited rate for some periods.

Predicting the Effectiveness of Corrosion Inhibitors - The Inhibitor AvailabilityModel


49

In the context of this model, corrosion inhibitor availability infers the presenceof a suitable corrosion inhibitor at sufficient concentration to reduce thecorrosion rate to 0.1 mm/yr. The factors that lead to inhibitor availability below100% are:

m Inhibitor injection equipment is not available on Day 1 of operations.m Injection equipment requires maintenance and repairs.m Operators set the dose rate incorrectly.m Chemical is not available when required.m Chemical dose rate is less than optimum. This can be due to a variety of

reasons including lack of response to increases in throughput, or water cutor sand rate.

m Well stimulation fluids such as hydrochloric acid are produced along withthe crude oil and reduce corrosion inhibitor effectiveness.

m The corrosion inhibitor injection facilities are used for delivery of otheroilfield chemicals such as demulsifiers or combined products such as scaleand corrosion inhibitors.

m Inhibitors are deployed via large bore pipework (instead of via injectionquills) and are not dispersed in the flow stream for some distance, providingpoor protection.

All of these factors and others not listed have lead to less than optimal deliveryof corrosion inhibitor into production equipment in BPX. No asset is immune tosuch problems and therefore the maximum inhibitor availability that should beassumed is 95%. In many instances, a lower availability should be assumed; see,'Recommended Values For Use in the Inhibitor Availability Model, pp 51.'

Words of Caution

Production data from Cusiana shows that their 12 inhibitor injection skidsaveraged 99.2 % availability over the second half of 1996, an identical figure tothat generated at a new gas treatment plant in the Middle East. This is probablyclose to the maximum that inhibitor injection units can be available, bearing inmind the requirements for chemical feedstock, power and the reliability of thepumps. However, this should not be used as a basis for assuming an inhibitoravailability of greater than 95%. Figure 14 shows the delivery of corrosioninhibitor against the target rate for a North Sea platform. There was only oneinstance when the inhibitor injection system was not delivering chemical - duringMarch 1993 - but there were also only 3 short periods where the chemical wasfully available with respect to the target dose rate.


50

At the project stage, it is difficult to determine the availability of inhibitor infuture years but relatively easy to ensure inhibitor is available on day one. Theprovision of chemical injection equipment is often outside the scope of EPICcontracts and therefore assets are brought on-stream without the necessaryfacilities to inhibit valuable equipment. In previous projects, this has taken upto 2 years to correct and therefore the best inhibitor availability that can beachieved will be 90%, assuming a 20 year design life. If the provision ofchemical injection equipment is brought inside the scope of the EPIC contract,measures can be taken to ensure inhibitor is available on day 1 of operations.

Achieving good inhibitor availability during operations is partly down to systemdesign and partly due to management of the changing corrosion risk. Inhibitorinjection systems are simple systems and lend themselves to high levels ofmechanical availability. This can be improved further through the use of lowlevel warning devices on the storage tanks and dose rate gauges such as thesight glass or more complicated dose rate monitoring systems. Together, thesetwo simple measures will help to ensure that the target dose rate is achievedfor a high proportion of the time.

Ensuring the target dose rate is correct is more difficult and requires thatconstant changes to the target are made to reflect changes in production rate,water cut etc In extreme cases, this may require weekly tailoring of the targetdose rate. This is where corrosion control programmes can fail and thereforeit is important that the materials or corrosion engineer concentrates on thisaspect.

100

80

60

40

20

0

January1993

March1993

May1993

July1993

September1993

November1993

January1994

March1994

May1994

Target = 50ppm

Figure 14: TheAvailability ofCorrosion Inhibitorinto a Main-Oil-Lineover an 18 MonthPeriod

THE USE OF PREDICTIVE MODELS DURING

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