corrosion control in the oil and gas industry || modeling – internal corrosion

Modeling – Internal Corrosion

CHAPTER

6
6.1 IntroductionThe primary functions of corrosion professionals are to predict whether a given material is susceptibleto a particular type of corrosion in a given environment; to estimate the rate at which the materialwould corrode in that environment; and to develop, if necessary, mitigation methods to control thecorrosion rate in that environment.
Chapter 3 discusses materials and their classifications, Chapter 4 describes various environmentsthat may prevail in the oil and gas industry, and Chapter 5 discusses various types of corrosion thatmight take place. The suitability of various materials for the given environment with respect to alltypes of corrosion must be evaluated before a particular material is selected. This may be a dauntingtask. Fortunately, several rules of thumb, models, predictive tools, or guidelines have been developedbased on several years of field experience and laboratory experiments. Such predictive tools providethe corrosion professionals with quick answers. But before using such predictive tools, their appli-cability and ability to the particular material, and to the particular type of corrosion in the givenenvironment should be evaluated.

This chapter presents models for predicting hydrogen effects, general corrosion, pitting corrosion,erosion-corrosion, microbiologically influenced corrosion (MIC), high-temperature corrosion, andtop-of-the line corrosion (TLC).

6.2 Hydrogen effectsOne primary criteria for selecting a material to construct an oil and gas industry infrastructure isits ability to withstand the effects of hydrogen (see section 5.18). Many variables influence theeffects of hydrogen. No quantitative models are available to accurately predict the various effectsof hydrogen. However, based on extensive laboratory testing and field experience, many guide-lines have been developed in the form of international standards. Table 6.1 summarizes thestandards for evaluating the susceptibility of a material to hydrogen in a given environment. Ingeneral, the effects of hydrogen depend on the susceptibility of the material and the severity of theenvironment. Some quantitative parameters used to predict hydrogen effects are discussed in thefollowing paragraphs.

Corrosion Control in the Oil and Gas Industry. http://dx.doi.org/10.1016/B978-0-12-397022-0.00006-6

Copyright � 2014 Elsevier Inc. All rights reserved.301

http://dx.doi.org/10.1016/B978-0-12-397022-0.00006-6

Table 6.1 Laboratory Methods to Determine the Susceptibility of a Material to the Effect of Hydrogen

Type of HydrogenEffect Standard Specific Tests Remarks

Sulfide stresscracking (SSC)

MRO175/ISO 15156e2)

MRO175/ISO 15156e3))NACE TMO177EFC Publication 16

Samples are stressed(Applied stress).

Stress-orientedhydrogen inducedcracking (SOHIC)

MRO175/ISO 15156e2) NACE TMO177 The occurrence of thesetypes offailures is relatively rare andis not well understood.Therefore test methods arenot standardized.

Soft-zone cracking(SZC)

MRO175/ISO 15156e2) NACE TMO177

Step-wise cracking(SWC)

MRO175/ISO 15156e2) NACE TMO284 Samples are not stressed(No applied stress).

Hydrogen inducedcracking (HIC)

MRO175/ISO 15156e2) NACE TMO284 Samples are not stressed(No applied stress).

)For carbon steel))For CRAs

302 CHAPTER 6 Modeling – Internal Corrosion

6.2.1 Susceptibility of the materialThe susceptibility of a given material to hydrogen depends on its hardness and the hydrogen atomconcentration within it.

Many studies have conclusively indicated carbon steel with more than 22 hardness on Rockwellscale C (HRC) is susceptible to sulfide stress cracking (SSC) or hydrogen induced cracking (HIC). Thehardness may be different in various locations (i.e., weld, heat-affected zone, and base material) of thematerial. In order to withstand hydrogen effects, the hardness in any location of the material should notbe above 22 HRC. Hardness may also be reported in other units, such as Vickers (HV) or Brinell(HBW). Factors to convert hardness from one unit to other are available (see Table 3.1).1–3 Thehardness requirements of Corrosion-Resistant Alloys (CRAs) are also available.4

In order for HIC to initiate, a minimum hydrogen atom concentration is required in the metal. Thisis commonly known as the threshold hydrogen atom concentration (CTHo), and it varies from materialto material. For HIC-susceptible materials CTHo is low; whereas for resistant material it is high. Thus,the first step in predicting HIC susceptibility is to experimentally determine the hydrogen atomconcentration in a metal (CHo). If this is greater than CTHo, the metal will be susceptible to HIC. Theprinciple behind measuring the hydrogen concentration is described in the following paragraphs, andexperimental details for determining CHo are described in section 8.2.1.

The distribution of hydrogen atoms inside a material is not uniform. The hydrogen concentration atthe inner surface is maximum because hydrogen atoms are generated there (assuming that the cathodicreaction occurring at the inner surface is hydrogen reduction). At the outer surface the concentration iscloser to zero, because the hydrogen atomic hydrogen will combine at the outer surface to producemolecular hydrogen. It is normally assumed that the concentration varies linearly across the thickness

STEEL(CROSS-SECTION)

LOO

CH

L = Wall thicknessX = Distance from internal surface

CH

CH = C0H at X = 0

at X = Lat X = ½ LCH = ½ C0

HCH = 0

0

INTERNALSURFACE

DISTANCE FROMINTERNAL

SURFACE, X

SOUR ENVIRONMENT ATMOSPHERE

HYDROGEN ATOMCONCENTRATION

HYDROGEN ATOMCONCENTRATION

GRADIENT

EXTERNALSURFACE

FIGURE 6.1 Hydrogen Atom Concentration Gradient in Material.5

Reproduced with permission from Wiley.

6.2 Hydrogen effects 303

of the material (L) as shown in Figure 6.1.5 To calculate CHo, the diffusion coefficient (D) is required.Several equations are used to calculate the diffusion coefficient:6

D ¼ L2

6tl(Eqn. 6.1)

L2
D ¼
7:2t1=2(Eqn. 6.2)

L2
D ¼
15:3tH;b(Eqn. 6.3)

" !# 2 �ktrap�2 3

D ¼ L2

6tl: 1þ Ntrap

ktrappuntrap

:6641� NtrapCH puntrap

2h1þ Ntrap

�ktrappuntrap

�iþ.775: (Eqn. 6.4)

where L is the thickness of the specimen (cm), tl is the time at which the permeation rate reaches 63%of the steady state rate, t1/2 is half-life time for hydrogen diffusion to reach steady state, tH,b is thebreakthrough time, CH is the concentration of lattice-dissolved hydrogen, Ntrap is the concentration oftraps in steel, ktrap is the kinetic parameter for the trapping reaction, and puntrap is the kinetic parameterfor the untrapping reaction. Equation 6.4 reduces to Eqn. 6.1 when hydrogen trapping does not occur(i.e., when Ntrap ¼ 0 or ktrap/puntrap ¼ 0).


The hydrogen atom concentration in a material is determined by measuring the permeation currentdensity (Imax) as:

CHo ¼ imax:L

DF(Eqn. 6.5)

where imax is the peak current density; F is the Faraday constant (96,487 C/mol), and D is calculatedusing one of Eqns.6.1 through 6.4. Section 8.2.1 describes the apparatus and procedure for measuringimax.

6.2.2 Severity of the environmentThe severity of the environment with respect to SSC in carbon steel depends on the H2S partialpressure and in situ pH, and for CRAs it depends on the H2S partial pressure, in situ pH, chloride andsulfur concentrations, and temperature. Figure 6.2 provides the relationship between H2S partialpressure and in situ pH with respect to SSC resistant of carbon steel.7 The in situ pH is relativelydifficult to measure; therefore, empirical correlations and equations are used to predict it. Figures 6.3through 6.7 provide in situ pH as a function of partial pressure of H2S, partial pressure of CO2,temperature, bicarbonate, and calcium ions.8–12 Equation 6.6 provides in situ pH as a function ofpartial pressure of H2S, partial pressure of CO2, temperature, and bicarbonate, ion concentrations (seesections 4.12 and 6.3.1).13

FIGURE 6.2 Correlation between H2S Partial Pressure and in Situ pH with Respect to SSC Resistance of Carbon

Steel.7

(X axis is H2S partial pressure in kilopascals and Y axis is in situ pH; 0 region 0 (No SSC), 1 SSC region 1, 2 SSC

region 2, and 3 SSC region 3).

Reproduced with permission from NACE International.

FIGURE 6.3 Effect of Partial Pressures of H2S and CO2 on in Situ pH.8

(1 at 20�C and 2 at 100�C).Reproduced with permission from NACE International.

6.3 General corrosion of carbon steel 305

pH ¼ Constant� logðpH2Sþ pCO2Þ þ log½HCO3� � (Eqn. 6.6)

where [HCO3�] is the concentration of bicarbonate ion, and pH2S is the partial pressure of H2S, and

pCO2 is the partial pressure of CO2. The value of the constant depends on temperature and units used inthe equation.

6.3 General corrosion of carbon steelOnce the material is selected (as per section 6.2), its corrosion characteristics in the given environmentshould be determined. Internal corrosion of carbon and low alloy steels caused by CO2 and H2S is amajor threat. An industry standard approach for assessing the risk of internal corrosion does not exist atthe present time, although some guidelines are provided by the following standards:

• NACE Standard Practice SP0106, ‘Control of Internal Corrosion in Steel Pipelines and PipingSystems’

• NACE Standard Practice SP0206, ‘Internal Corrosion Direct Assessment Methodology forPipelines Carrying Normally Dry Natural Gas (DG-ICDA)’

FIGURE 6.4 Combined Effects of Partial Pressures of H2S and CO2 and Bicarbonate Ions on in Situ pH.9

(1 is at 0 meq/L of HCO3�; 2 is at 0.1 meq/L of HCO3

�; 3 is at 1 meq/L of HCO3�; 4 is at 10 meq/L of HCO3

�; 5 is at

100 meq/L of HCO3�; 6 is at 100�C; and 7 is at 20�C).



• NACE Standard Practice SP0110, ‘Wet Gas Internal Corrosion Methodology for Pipelines’• NACE Standard Practice SP 0208, ‘Internal Corrosion Direct Assessment Methodology for

Liquid Petroleum Pipelines’• CAPP Best Management Practice 2009–13, ‘Mitigation of Internal Corrosion in Sour Gas

Pipeline Systems’, June 2009• CAPP Best Management Practice 2009–14, ‘Mitigation of Internal Corrosion in Sweet Gas

Pipeline Systems’, June 2009.

Several models have been developed, which differ considerably in terms of parameters considered(partial pressure of CO2, partial pressure of H2S, temperature, flow rate, total pressure, chloride ion,acetic acid, oil type, and gas type), approach (empirical, mechanistic, and simulation), and theinfluencing factors that are considered (surface layers: FeCO3, FeS, Fe3C, and Fe3O4).

14–17 As aconsequence of these variations, the numerical corrosion rates predicted by the various models candiffer considerably, even though most agree with one another on the influence of a particular variableon the corrosion rate. The characteristics of some recognized models are described in the followingparagraphs.

FIGURE 6.5 Combined Effects of Partial Pressures of H2S and CO2 and Calcium Carbonate Ion on in Situ pH

at 20�C.10

(1 is at 1,000meq/L of Ca2þ; 2 is at 100meq/L of Ca2þ; 3 is at 10 meq/L of Ca2þ; 4 is at 10 meq/L of HCO3�; 5 is at

30 meq/L of HCO3�; 6 is at 100 meq/L of HCO3

�; In 7 Ca2þ < HCO3�; In 8 Ca2þ ¼ HCO3

�; In 9, Ca2þ > HCO3�).



6.3.1 The de Waard-Milliams models18–21

de Waard and Milliams developed the first and most frequently referenced model to predict sweetcorrosion. During the early 1970s, failures in pipelines transporting wet natural gas containing CO2

prompted them to investigate the corrosivity of carbonic acid.Assuming the cathodic reduction of hydrogen is the rate determining step, they correlated the

corrosion current (Icorr) with pH as:

logIcorr ¼ �ADM :pH þ BDM (Eqn. 6.7)

where ADM and BDM are de Waard and Milliams constants. Based on two sets of laboratory experi-ments conducted at CO2 saturated (Table 6.2 presents the experimental conditions) they found thevalue of ADM as 1.3 and consequently, rewritten the Eqn. 6.7 (Eqn. 6.8):

logicorr ¼ �1:3:pH þ BDM (Eqn. 6.8)

FIGURE 6.6 Combined Effects of Partial Pressures of H2S and CO2 and Calcium Carbonate Ion on in Situ pH at

60�C.11

(1 is at 1,000 meq/L of Ca2þ; 2 is at 100meq/L of Ca2þ; 3 is at 10 meq/L of Ca2þ; 4 is at 10 meq/L of HCO3�; 5 is at

30 meq/L of HCO3�; 6 is at 100 meq/L of HCO3

�; In 7 Ca2þ < HCO3�; In 8 Ca2þ ¼ HCO3

�; In 9, Ca2þ > HCO3�).



Based on the variation in temperature, they found linear relationship between pH and temperature forsweet system to be (Eqn. 6.9):

pH�pCO2 ¼ 1

� ¼ 4:17� 10�3T þ 371 (Eqn. 6.9)

They observed that a black layer covered some samples under stagnant conditions at temperaturesabove 100�F (40�C), and consequently the corrosion rate decreased to a low value. Under flowingconditions, they observed this effect above 140�F (60�C).

They explained their findings by proposing the cathodic reduction of carbonic acid (Eqn. 6.10),rather than the direction reduction of hydrogen ions:

H2CO3 þ e�/H þ HCO3� (Eqn. 6.10)

Based on these results they developed a nomogram (Figure 6.8), which has gained wide accep-tance as the starting point for predicting the corrosion rate of carbon steel in sweet environment.

FIGURE 6.7 Combined Effects of Partial Pressures of H2S and CO2 and Calcium Carbonate Ion on in Situ pH at

100�C (See Fig. 6.6 for Description of 1 through 9).


Table 6.2 Experimental Conditions of the Original de Waard and Milliams Model18

Conditions Short-Term Experiment Long-Term Experiment

Specimen Polished X52 carbon steel bar(length 45 mm and diameter 6 mm)

Grit-blasted X52 carbon steel

Environment 0.1% NaCl saturated with mixtureof CO2 and specially purifiedoxygen-free N2

0.1% NaCl saturated withmixture of CO2 and speciallypurified oxygen-free N2

Solution volume 1 liter

Container Glass Autoclave

Velocity 1 m/s

Corrosion rate measurement Linear polarization resistance(LPR) method

Mass loss and LPR

Duration, days 1 7

Temperature, �C Room to 90 Room to 90


140

TemperatureºC

ScaleFactor

CO2 pressurebar 10

1

0.1

0.01

0.1

20

Example:0.2 bar CO2 at 120ºCgives 10 × 0.7 = 7 mm/y

10

1

0.1

0.02

1

130120110100908070605040

30

20

10

0

FIGURE 6.8 Nomogram to Predict Sweet Corrosion.18–21



Subsequently they derived a simple equation to predict the corrosion rate (Ccorr) in sweet systems as(Eqn. 6.11):

logCcorr ¼ 5:8� 1710

Tþ 0:671:log

�pCO2

�(Eqn. 6.11)

where T is temperature and pCO2 is the partial pressure of CO2. They further simplified the nomogramand included a ‘scale correction’ factor to account for the decrease of corrosion rate due to the for-mation of surface layer (Figure 6.8).

Their co-workers subsequently modified the basic model to include many other effects; asdescribed in the following paragraphs.

6.3.1a Effect of total pressureThe increase in corrosion rate with increasing pressure was accounted for by multiplying the corrosionrate predicted by Eqn. 6.11 by a factor based on fugacity of the gas (Fg) as shown in (Eqn. 6.12):

logFg ¼ 0:67

�0:0031� 1:4

T

�P (Eqn. 6.12)

where P is the operating pressure and T is the temperature.

6.3.1b Effect of surface layerThe precipitation of FeCO3 (or Fe3O4) in itself does not necessarily result in the formation of a protectivelayer. At lower temperatures (typically lower than 140�F (60�C)) the layer has a smudge-like appearance


and is easily removed by flowing liquids. At higher temperatures, the layer is different in texture and ismore protective. The effect of surface layers is accounted for by multiplying the corrosion rate predictedby Eqn. 6.11 by a factor Fscale calculated as given in (Eqn. 6.13):

logFscale ¼ 2400

T� 0:6log

�fCO2

�� 6:7 (Eqn. 6.13)

where FCO2is the fugacity of carbon dioxide.

6.3.1c Effect of temperatureThe temperature at which the corrosion rate starts to decrease due to the formation of protectivelayers is defined as the scaling temperature. The scaling temperature depends on flow rate; a higherflow rate will result in a higher scaling temperature. The scaling temperature, Tscale, is predicted byEqn. 6.14

Tscale ¼ 2400

6:7þ 0:6logð fCO2Þ (Eqn. 6.14)

Figure 6.9 presents the variation of scaling temperature with CO2 partial pressure; the scaling tem-peratures appear as sharp peaks (Y axis: Corrosion rate, mm/y).

6.3.1d Effect of velocityThe protective surface layer can be removed by flowing fluid. For wet gas transport at a superficial gasvelocity of 20 m/s (66 feet/s) the protective surface layers are completely removed, i.e., at that velocitythe corrosion rate continues to increase with increasing temperature.

200

2

4

6

8

10

12

14

40 60 80Temperature, ºC

31

0.3 0.1 barCO2

100 120 140

FIGURE 6.9 Variation of Scaling Temperature with CO2 Partial Pressure.18–22



6.3.1e Effect of hydrocarbonThe presence of crude oil has a beneficial effect on corrosion. The steel is assumed to be wetted withoil if the water is entrained in the crude. Such situation occurs when the flow rate is 1 m/s or higher, andif the water cut is below 30%. At flow rates lower than 1 m/s and water cut higher than 30%, waterdrops out. It is also assumed that light hydrocarbon condensates (for example, natural gas liquids) donot offer any protection regardless of the water content.

6.3.1f Effect of glycolGlycol is often added to wet gas pipelines to prevent the formation of hydrates. Monoethylene glycol(MEG), diethylene glycol (DEG), and triethylene-glycol (TEG) are used for this purpose. The glycolreduces corrosion rate by absorbing water from the gas phase.

To correct corrosion rate for the effect of glycol, it is multiplied by a glycol factor, Fglyc (Eqn. 6.15):

logFglyc ¼ aglyc:log�Wglyc

�� 2aglyc (Eqn. 6.15)

where Wglyc is the amount of glycol and aglyc is a constant. A value of 1.6 is commonly used for aglycfor most glycols.

6.3.1g Effect of flow velocityIn the earlier versions of the model, the only influences of fluid velocity taken into account were itseffect on crude and water separation, and on protective corrosion product layers. When these effectswere excluded, there was no significant effect of liquid flow velocity on CO2 corrosion rate; i.e., thecorrosion reaction appeared to be activation-controlled (see section 5.2). However, the observedcorrosion rates in some cases were about twice the rate predicted.

With increasingly turbulent flow, more reactive species will reach the surface; consequently, thecorrosion rate will become flow-independent. Under this condition it is assumed that the actualcorrosion rates can be approximately twice as high as that originally predicted.

6.3.1h Effect of microstructureCementite (Fe3C) accelerates corrosion rate (see section 4.5.3); this effect is more pronounced when itforms a coherent network on the surface. In normalized steels, cementite forms a coherent network,whereas in tempered martensite it does not. Therefore cementite affects the corrosion rate ofnormalized steel but not that of tempered steel.

In general, the corrosion rate of carbon steel decreases with increase in chromium content due tothe formation of protective chromium oxide. However, when chromium combines with carbon to formchromium carbide, the beneficial effect of chromium is lost.

It should be noted that the influence of microstructure of various low alloy steels predicted by thismodel only applies to conditions in which protective films do not form. Furthermore, the developmentof a carbide network on the surface of normalized steel is a time-dependent process; hence the pre-dicted rate depends on whether the network has been established or not.

6.3.2 The Srinivasan model22

The de Waard and Milliams model presented in the nomogram form can be used to understand theinfluence of partial pressure of CO2, temperature, and surface layers. But the influence of other factors


cannot be determined in a user friendly way using the nomogram. With the advancement of computertechnology and the application of a user-friendly interface, software products have been developed.The first successful software to predict internal corrosion in oil and gas production environments wasdeveloped by Srinivasan et al. The characteristics of Srinivasan’s model are provided in the followingparagraphs.

The Srinivasan model is derived from principles established by de Waard-Milliams relationship forCO2 corrosion. The Srinivasan model first predicts in situ pH as a function of acid gas partial pressures,bicarbonates and temperature and then superimposes several other effects as shown in Figure 6.10.The effects considered in the first version of the model included the surface layer, H2S partial pressure,temperature, chloride, bicarbonate, gas-to-oil ratio, velocity, oil-gas-water ratio, aeration, and sulfur.

FIGURE 6.10 Flow Chart of Srinivasan’s Model.22



The following sections discuss the effects of these parameters on corrosivity, and provide infor-mation describing why it is critical to examine the parameter interactions prior to capturing thesynergistic effects of these parameters on corrosion.

6.3.2a Effect of pHThe following assumptions have been made with respect to the effects of pH and CO2. The CO2

corrosion mechanism is dissimilar to that of strong acids like HCl, by being much more severe at thesame pH; the presence of higher concentrations of acid gases lowers the pH and consequently in-creases the corrosion rate; the presence of buffering chemicals maintains a higher pH and hence de-creases the corrosion rate, even in the presence of higher concentrations of acid gases; and it is moremeaningful to determine the effective CO2 partial pressure from the system pH.

The in situ pH is predicted usingEqns. 6.16 and 6.17: At 20�C (68�F) in the absence of bicarbonate ion:

pHð1Þ ¼ C1� logðpH2Sþ pCO2Þ (Eqn. 6.16)

and in the presence of bicarbonate ion:

pH�2� ¼ C2� log

�pH2Sþ pCO2

�þ log�HCO3

� (Eqn. 6.17)

where C1 and C2 are constants, pH2S and pCO2 are partial pressures in bars and [HCO3�] is the

concentration of bicarbonate ion. The system pH is given by the larger of pH(1) and pH(2).Once the system pH is determined, the effective CO2 partial pressure is determined using

Eqn. 6.18:

log�pCO2ðeff Þ

�¼ C1� pH

2(Eqn. 6.18)

where pCO2(eff) is the effective partial pressure of CO2 and is used in the deWaard and Milliams modelto determine an initial corrosion rate.

6.3.2b Effect of surface layerThe corrosion rate obtained in section 6.3.2a is modified to account for the formation of a FeCO3 layer(or Fe3O4 at higher temperatures). The correction factor presented in Figure 6.8 is used for thispurpose.

6.3.2c Effect of H2SThe effect of H2S adopted in the model is presented in Figures 6.11 and 6.12.23 It is recognized that theeffect of H2S depends on its partial pressure. At H2S partial pressures less than 0.01 psi (w 0.07 kPa),CO2 is the dominant corrosive species and the presence of H2S has no significant impact; under thiscondition, the corrosion rate and surface layer formation are functions of FeCO3. Above the H2Spartial pressure of 0.01 psia and when pCO2 to pH2S ratio is greater than 200, an iron carbonate layercan form depending on pH and temperature; once formed as intact layer, it can mitigate corrosion.When the ratio of pCO2 to pH2S ratio is less than 200, there is a preferential formation of a iron sulfidelayer when compared to FeCO3. This iron sulfide layer is protective at temperatures between 60 and120�C (140 and 250�F). At temperatures below 60�C (140�F) and above 120�C (250�F), H2S increasesthe corrosion rate because FeS is not stable under these conditions, and, furthermore, the iron sulfidelayer does not allow the formation of FeCO3.

FIGURE 6.11 Effect of Acid Gases on the Corrosion Rate of Carbon Steel.22,23


FIGURE 6.12 Effect of H2S and Temperature on the Corrosion Rate of Pure Iron.22,23




6.3.2d Effect of temperatureIn CO2 dominated systems (pCO2/pH2S is greater than 200) the variation of corrosion rates withtemperature is assumed to follow the trend presented in Figure 6.9. In H2S-dominated systems (pCO2/pH2S is less than 200) it is assumed that the iron sulfide is protective up to 120�C (250�F) and abovethis temperature localized corrosion may occur.

6.3.2e Effect of chloride ionIn de-aerated environments, the corrosion rate increases with increasing chloride ion concentration inthe range 10,000 ppm to 100,000 ppm. The magnitude of this effect increases with increasing tem-perature above 60�C (140�F).

6.3.2f Effect of bicarbonate ionHigh levels of bicarbonates increases the pH and decreases corrosion rates even when the partialpressures of CO2 and H2S are high.

6.3.2g Effect of velocityFluid flow velocities affect both the composition and extent of corrosion products. Typically, velocitieshigher than 4 m/s (13 feet/s) remove surface layers mechanically. Figure 6.13 presents the corrosionrate as a function of flow velocity and temperature.24

In multiphase (that is, gas, water, liquid hydrocarbon) production, the flow rate influences thecorrosion rate of steel in two ways: by flow behavior and flow regime. Figure 6.14 presents the effect offlow rate on the corrosion rate.25

Velocities lower than 1 m/s (3.3 feet/s) are considered static. Under these conditions, corrosionrates can be higher than those observed under moderately flowing conditions. This occurs becauseunder static conditions, there is no natural turbulence to assist the mixing and dispersion of protectiveliquid hydrocarbons or inhibitor species in the aqueous phase. Additionally, corrosion products andother deposits can settle out of the liquid phase to promote crevice attack and underdeposit corrosion.

FIGURE 6.13 Effect of Velocity on Corrosion Rate of Carbon Steel.24


FIGURE 6.14 Effect of Flow on the Corrosion Rate.25



Between 1 and 3 m/s, the flow effect is considered as moderate. It is assumed that stratifiedconditions generally still exist. However, the increased flow promotes a sweeping away ofsome deposits and increasing agitation and mixing. As can be seen from Figure 6.14, thecorrosion rates of steel in chemically inhibited fluids increase only slightly between flow ratesbetween 3 and 10 m/s (10 and 33 feet/s). This is attributed to the mixing of the hydrocarbon andaqueous phases.

At 5 m/s (16 feet/s), the corrosion rates increase with increasing velocity. Above about (10 m/s or33 feet/s) (Figure 6.14), the corrosion rates of carbon steel even in inhibited solutions start to increasedue to the removal of protective surface films by the high velocity flow.

6.3.2h Effect of oil-gas-water ratioThe model classifies the systems as oil-dominated or gas-dominated on the basis of the gas/oil ratio(GOR) of the production environment.

FIGURE 6.15 Effect of Acid Number on Crude Oil Wettability.26,27

(y Secondary Axis is Interfacial Tension).



If the GOR is less than 890 m3/m3 (5000 scf/bbl), the corrosion rate is low due to the inhibitingeffect of oil. However, this effect depends on the oil phase being persistent and acting as a barrierbetween the metal and the corrosive environment. The persistence of the oil phase is a strong factor inproviding protection, even in systems with high water cuts. A persistent oil phase on steel surface mayprotect it from corrosion even in the presence of 45 percent water cut.

The relative wettability of the oil phase versus the water phase has a significant effect on corrosion.Metal surfaces that are oil-wet show significantly lower corrosion. The model classifies the oils aspersistent, mildly persistent, and not persistent, and corrects the corrosion rate, up to a factor of 4,based on the type of oil phase.

The degree of protection, however, can be quantified only as a function of water cut and velocity.The persistence determination is a more complex task and requires knowledge of the kerogen type andhydrocarbon density. It is also important to understand the type of crude oil in terms of the organiccompounds. Figure 6.15 correlates the acid number of the crude and oil wettability26 and Figure 6.16correlates corrosion rate and type of crude oils.27

While the effect of the persistence of the oil medium is significant on corrosion rates, it is evenmore difficult to quantify precise compositional elements of oil that contribute to wettability andpersistent oil film formation. Such quantification may be possible by rigorous laboratory testing ofdifferent actual, uncontaminated (that is, de-aerated) production water samples.

In oil systems, the water cut determines the level of protection from the hydrocarbon phase.However, at very low water cuts (less than 5%), corrosive severity is low due to the absence of anadequate aqueous medium required to promote the corrosion reaction.

FIGURE 6.16 Effect of Crude Oil Type on Corrosion Rate.27



In gas-dominated systems, there are two measures to evaluate the availability of the aqueousmedium. If the operating temperature is higher than the dew point of the environment, no condensationis possible, and this will give low corrosion rates. Corrosion under condensing conditions (that is,operating temperature less than the dew point) is a function of the rate of condensation and transport ofcorrosion products from the metal surface. If the water to gas ratio is lower than 11.3 m3 water/Mm3

gas (2 bbl water/MSCF gas), the corrosivity is low.

6.3.2i Effect of aerationThe presence of oxygen significantly increases the corrosivity of the environment in productionsystems. Figure 6.17 presents corrosion rate as a function of oxygen concentration at different tem-peratures28 and Figure 6.18 presents the effect of flow rate on the corrosion rate of carbon steel in thepresence of oxygen.29

6.3.2j Effect of sulfurThe influence of elemental sulfur on corrosion rate is similar to that of oxygen since elemental sulfuralso acts as a strong oxidizing agent.

6.3.3 The crolet model29,30

Both de Waard and Milliams and Srinivasan developed their models based solely on laboratory data.On the other hand, Crolet et al. developed their model based mainly on operational data and experience

FIGURE 6.17 Effect of Oxygen Concentration on the Corrosion Rate of Carbon Steel.22,28


FIGURE 6.18 Effect of Flow Rate on Oxygen Corrosion of Carbon Steel.22,29

(Maximum economic corrosion rate is the rate up to which the use of carbon steel is considered economical).



from production wells in France. The Crolet model uses CO2 partial pressure, in situ pH, Ca2þ/HCO3�

ratio and the free acetic acid as influencing factors for downhole tubular corrosion. It categoriesdownhole tubulars into three classes: low, medium or high, based on the likelihood of perforationwithin 10 years.

Crolet considers three parameters for localized corrosion to occur: contact of water with themetal; potential corrosivity (PC), and favorable conditions for localized corrosion, i.e., active, stable


anodic sites surrounded by cathodic areas. Based on these parameters, the model categorizes thewells into three types: corrosive (significant corrosion, whose lifetime is under two to three yearsunder current operating conditions), possibly corrosive (in these wells, minor corrosion was expe-rienced in the past and currently the water cut exceeds 20–30%) and non-corrosive (wells for whichno corrosion problems have been experienced for the past eight years, in spite of the water cutexceeding 20–30%).

6.3.3a Contact of water with metalAt low pressure, contact of water with metal, and hence the probability of corrosion, is low as long asthe water cut is lower than 25 to 40%. At high pressure, water contacts the metal surface to initiatecorrosion at a water cut of between 0.5 and 5%.

6.3.3b Potential corrosivityPotential corrosivity is the corrosivity of the water, i.e., it is the maximum rate at which uniformcorrosion occurs in the medium in the absence of any protective effect. The corrosion rate is low if thePC is low and high if the PC is high. It is recognized that PC can be readily measured in the laboratoryusing simple techniques, and that a considerable amount of data is already available in the literature.The Crolet model considers the cathodic reaction as the rate determining step, and calculates thecorrosion rate using pH, H2CO3, CO2, acetic acid, temperature,and flow rate.

Detailed equations are not publicly available, but the PC predicted by the model compares wellwith the corrosion rate predicted by the de Waard and Milliams model (Figure 6.19).30

0.1 1

1

10 mm/yCorrosion rate

mm/y

PC

0.1

0.1 1 10

1

10

0.1

1

10

FIGURE 6.19 Comparison of Crolet Model’s ‘Potential Corrosivity’.30

(Y Axis) and de Waard and Milliams Model’s Corrosion Rate (X Axis).



6.3.3c Favorable conditions for localized corrosionIt is recognized that the nature and physical chemistry of CO2-containing production waters influencethe localized corrosion. The following field data are considered as relevant for predicting favorableconditions for localized corrosion to occur: reservoir data (pressures and temperatures, GOR, andbubble point of the oil); production water (concentrations of all major inorganic cations and anions aswell as organic cations (acetates and propionates); gases (partial pressures of CO2 and H2S); pro-duction data (well-head temperatures, pressures, and flow rates); type of gas lift; nature of wellequipment (diameters and specification of the equipment); corrosion experience of field operators interms of operating problems attributable to corrosion; damage observed (for example, results of caliperinspections); and time elapsed since attainment of a water cut above 25–30%. Based on the input data,the following parameters are calculated: CO2 partial pressure; in situ pH of the production water; insitu acetic concentration; and potential corrosivity.

The Crolet model only predicts a general corrosion rate and only conditions favorable for localizedcorrosion. Further detailed calculations are not available in the public domain.

6.3.4 The Nesic model31–33

Nesic developed a mechanistic model using electrochemical reactions at the metal surface and thetransport process of the species towards that surface. The electrochemical reactions considered in themodel involve Hþ, CO2, H2CO3 and Fe2þ. The transport process is based on the mass transfer co-efficients of diffusing species. It is assumed that the species are diffusing independently of thediffusion rates of other species. Glass cell experiments involving a rotating cylinder electrode wereused to determine the constants required for the model. The model proposes the CO2 corrosionmechanism by the following equations.

Dissolution of CO2 in water produces carbonic acid (Eqn. 6.19):

CO2 þ H2O4H2CO3 (Eqn. 6.19)

The carbonic acid (H2CO3) dissociates in two steps, producing a reservoir of hydrogen ions:

H2CO34Hþ þ HCO3� (Eqn. 6.20)

HCO3�4Hþ þ CO3

2� (Eqn. 6.21)

Thus, under acidic pH conditions (pH less than 4), the hydrogen ions produced from the dissociation ofcarbonic acid undergo cathodic reduction:

Hþ þ e�/H (Eqn. 6.22)

In the intermediate pH range between 4 and 6, in addition to hydrogen ion reduction, direct reductionof carbonic acid takes place (Eqn. 6.23):

H2CO3 þ e�/H þ HCO3� (Eqn. 6.23)

This additional cathodic reaction is the reason that carbonic acid is more corrosive than a completelydissociated acid at the same pH.

When the concentrations of Fe2þ and CO32� ions exceed the solubility limit, they combine to form a

solid iron carbonate surface layer (Eqn. 6.24):

Fe2þ þ CO32�/FeCO3

�s�

(Eqn. 6.24)


It is recognized that the nucleation of a crystalline surface layer is a very difficult process to modelmathematically, and that the rate of precipitation is controlled by the crystal growth rate rather than thenucleation rate. Two different equations (Eqns. 6.25 and 6.26) are used to predict the rate of ironcarbonate surface layer growth.

RFeCO3¼ A:e

54:8�123RT :Ksp

�S0:5FeCO3

� 1�2

(Eqn. 6.25)

RFeCO ¼ A:e52:4�119

RT :KspðSFeCO � 1Þ�1� S�1

�(Eqn. 6.26)

3 3 FeCO3

where RFeCO3is the rate of growth of the iron carbonate surface layer, A is the surface area available for

precipitation per unit volume, R is the gas constant, T is the temperature, Ksp is the precipitation rateconstant, and SFeCO3 is supersaturation rate of iron carbonate.

Accordingly, iron carbonate precipitation can occur on the steel surface or within the pores of aporous surface layer. In the porous layer, A is equal to the surface area of the pores per unit volume. Inthe presence of an iron carbonate layer, the value of surface area ‘A’ is assumed to be 105 m�1.The solubility product, Ksp, for iron carbonate is modeled as a function of temperature, and the ionicstrength is based on thermodynamic calculation.

When the precipitation rate is much smaller than the corrosion rate (expressed in the same units) aporous and unprotective surface layer forms, and when the precipitation rate is much higher than thecorrosion rate, it is very likely that a dense protective iron carbonate surface will form.

The model requires the following inputs: temperature, pH, CO2 partial pressure, oxygen concen-tration, steel composition, and flow geometry (rotating cylinder electrode or pipe).

The model prediction agrees with the de Waard and Milliams model. Compared with previousmodels, it is claimed by Nesic et al. that the present theoretical model gives a much clearer picture ofthe corrosion mechanisms and of the effect of key parameters. Most of the constants in the model canbe determined experimentally and are physically meaningful.

6.3.5 The Mishra model34

The Misha model considers corrosion of steel in CO2 solutions as a chemical reaction-controlledprocess and derives the corrosion rate equation on the basis of fundamental reaction rate theory.According to the model, the corrosion rate depends on the pH, partial pressure of CO2, and temper-ature. The model is best represented by Eqn. 6.27:

Corrosion:rate ¼ a�Hþ1:33:pCO2

0:67:e�nRT (Eqn. 6.27)

where a is a constant and can be modified based on material, environment, and flow velocity, Hþ ishydrogen ion concentration, pCO2 is the partial pressure of CO2, n is the number of electrons, R is thegas constant, and T is the temperature. Equation 6.27 is similar in form to empirically developed rateequations and, similar to other such equations, it cannot be used when surface layers form.

6.3.6 The Dayalan model35

The Dayalan model is a computational model for predicting the corrosion rates of carbon steel insolutions containing CO2. The model first predicts a uniform corrosion rate in the absence of surfacelayers on metal, and then extends to conditions where a FeCO3 surface layer forms.

Table 6.3 Factors Considered in Dayalan’s Computational Corrosion Model in the Absence of aq FeCO3

Surface Layer35

Step I: Formation of Reactants (Chemical Species in the Bulk)

CO2 ¼ H2O ¼ H2CO3

H2CO3 ¼Hþ þ HCO3�

HCO3 ¼ Hþ þ CO32�

Step II: Transportation of Reactants (Bulk to Surface)

H2CO3 (bulk) 0 H2CO3 (surface)

HCO3� (bulk) 0 HCO3

� (surface)

Hþ (bulk) 0 Hþ (surface)

Step III: Electrochemical Reactions at the Surface

Cathodic Reactions

2H2CO3 þ 2e ¼ H2 þ 2HCO3�

2HCO3� þ 2e ¼ H2 þ 2CO2

3�

2Hþ þ 2e ¼ H2

Anodic Reaction

Fe ¼ Fe2þ þ 2e

Step IV: Transportation of Products (Surface to Bulk)

Fe2þ (Surface) 0 Fe2þ (bulk)

CO32� (Surface) 0 CO3

2� (bulk)


The overall CO2 corrosion process is divided into four steps (Table 6.3): dissolution of CO2 in theaqueous solution to form the various reactive species; transportation of these species to the surface ofthe metal; cathodic and anodic electrochemical reactions on the metal surface; and transportationof the products of the corrosion reaction into the bulk of the solution.

At steady state, equilibrium is established between these steps so that the rate of mass transfer ofreactants, sum of the rates of cathodic reactions, rate of anodic reaction, and the rate of mass transferof products are all equal.

The inputs required to a calculate corrosion rate are the bulk concentrations and equilibriumconstants of the various species taking part in the corrosion reaction; the mass transfer rates (masstransfer coefficients) for transportation of reactants/products and the rates of cathodic and anodicelectrochemical reactions (electrochemical reaction rate constants) at the metal surface.

The general corrosion rate (without the effect FeCO3 layer) is computed in three steps: computationof the concentrations of various chemical species in the bulk of the solution under the given conditions;computation of the mass transfer coefficients for the required chemical species; and prediction ofcorrosion rates using the information from the two previous steps.

In the presence of a FeCO3 layer it is assumed that only a fraction of the metal surface is availablefor the anodic reaction, and that the cathodic reactions take place on the surface layer. Consequentially,the factors considered include (Table 6.4): the concentrations of various species at the scale surface, in

TABLE 6.4 Factors considered in Dayalan’s computational model in the presence of a FeCO3 surface

layer35

Step I: Formation of reactants (chemical species in the bulk)

CO2 þ H2O ¼ H2CO3

H2CO3 ¼ Hþ þ HCO3�

HCO3� ¼ Hþ þ CO3

2�

Step II: Transportation of reactants (bulk to scale surface)

H2CO3 (bulk) / H2CO3 (scale surface)

HCO3� (bulk) / HCO3

� (scale surface)

Hþ (bulk) / Hþ (scale surface)

Step III: Transportation of reactants (scale surface to metal surface)

H2CO3 (scale surface) / H2CO3 (metal surface)

HCO3� (scale surface) / HCO3

� (metal surface)

Hþ (scale surface) / Hþ (metal surface)

Step IV: Electrochemical reactions at the scale surface


2HCO3� þ 2e ¼ H2 þ 2CO3

2�

2Hþ þ 2e ¼ H2

Step V: Electrochemical reactions at the metal surface

Cathodic reactions


2HCO3� þ 2e ¼ H2 þ 2CO32�

2Hþ þ 2e ¼ H2

Anodic reactions

Fe ¼ Fe2þ þ 2e

Step VI: Transportation of products (metal surface of scale surface)

Fe2þ (metal surface) / Fe2þ (scale surface)

CO32� (metal surface) / CO3

2� (scale surface)

Step VII: Transportation of products(scale surface to bulk)

Fe2þ (scale surface) Fe2þ (bulk)

CO32� (scale surface) CO3

2� (bulk)



l

l

;

tt

.

,

.l

addition to their concentrations at the metal surface; mass transfer processes for reactants and productsbetween the metal surface and the layer surface; additional mass transfer processes through scale; areaof the surface available for anodic and cathodic reactions; cathodic electrochemical reactions on thescale surface; kinetics of cathodic reactions on the surface of the scale; and anodic reactions on themetal surface.

In a steady state, equilibrium is established between these steps, so that the rate of mass transfer ofthe reactants (from bulk to scale surface), the sum of the rates of cathodic reactions (on metal surfaceand scale surface), the rate of anodic reaction, and the rate of mass transfer of products (from metasurface to bulk) are all equal.

The equations are computationally solved by an iterative approach in two steps. First the corrosionrate is calculated assuming that there is no surface layer on the metal; i.e., the fraction of the metasurface that is covered by surface layer is set to zero. Then the surface concentrations of the productsFe2þ and CO3

2� are calculated from saturation factor (Fsat) and corrosion rate. If the Fsat value is lessthan 1, the conditions are not right for scale formation. If the Fsat value is greater than 1, then apredetermined value greater than zero is assumed for ASc (fraction of surface covered with layer)which consequently decreases the value for AMe from unity (fraction of surface bare) and the value forthe mass transfer coefficients through the layers. The computation then provides new values for Fsat athe metal surface and scale surface, the value at the scale surface being equal to or smaller than thaat the metal surface. If the Fsat value at the metal is greater than 1, the calculation is repeated byincrementing the value for ASc (and AMe and mass transfer coefficients through layer). The iteration isrepeated until the value of Fsat at the metal surface becomes equal to or slightly less than 1, whichcorresponds to an equilibrium condition for which further layer growth ceases.

6.3.7 The Anderko model36–38

Anderko computed the corrosion rates of carbon steel in the presence of CO2, H2S, and aqueous brinesThe model combines a thermodynamic model (which provides speciation of aqueous system) and anelectrochemical model (which provides partial cathodic and anodic processes on the metal surface). Thereactions considered in the model include the oxidation of iron and the reduction of hydrogen ionswater, carbonic acid and hydrogen sulfide. The model also includes the formation of iron carbonate andiron sulfide surface layers and their effect on the rate of general corrosion as a function of temperatureand solution chemistry. The model has been verified by comparing calculated corrosion rates withlaboratory data under conditions that may or may not be conducive to the formation of protective scalesGood agreement between the calculated and experimental corrosion rates has been obtained. The modehas been incorporated into a program that makes it possible to analyze the effects of various conditionssuch as temperature, pressure, solution composition or flow velocity on corrosion rates.

6.3.8 The Oddo model39

The Oddo model accounts for the effects of both iron carbonate surface layer as well as inorganicsurface layer (e.g., calcium carbonate scale). It first uses Eqn. 6.28 to account for the influence ofFeCO3 layer:

Log10Fscale ¼�2; 400

T

�� 0:6log10ðPFCO2

Þ � 6:7 (Eqn. 6.28)


where Fscale is a scale correction factor with a value between 0 and 1, P is the total pressure, and FCO2is

the fugacity of carbon dioxide.Formation of calcium carbonate (calcite) scales further reduces corrosion. To account for this,

another correction factor, the calcite correction factor, Fcalcite, is used. The value of Fcalcite may varybetween 0 and 1. It is zero when the saturation index for calcium carbonate deposition (SIc) is greaterthan 0.4; under this condition, field experience indicates that calcium carbonate scales deposit in a non-turbulent flow condition. The value of Fcalcite is unity when the SIc is less than �0.4. Finally when thevalue of SIc is between �0.4 and 0.4, Fcalcite is calculated using Eqn. 6.29:

Fcalcite ¼ 1�SIc þ 0:4

0:8

�(Eqn. 6.29)

It should be recognized that local turbulence (e.g., presence of a choke, constriction in the pipe, or anelbow), may alter the scaling tendency. Such effects are not included in Eqn.6.29.

6.3.9 The Pots model40–45

The Pots model includes the effect of flow on the CO2 corrosion rate. The model first calculates alimiting corrosion rate (LCR) based on mass transport. The LCR is the theoretical upper limit of thecorrosion rate when the rate determining step is the transport of protons as well as carbonic acid in thediffusion and reaction boundary layers. The model then links LCR with corrosion rate when chargetransfer is the rate determining step; the corrosion rate under charge transfer conditions is numericallycalculated based on chemical and electrochemical reactions. Further it considers the effect of flow andoil in sweeping the water out using Eqn. 6.30:

Fr ¼ Doil

DDo�wgdpipeVLz 0:65 (Eqn. 6.30)

where Fr is the Froude number, Doil is the density of oil, DDo-w is the density difference between oiland water, g is the acceleration due to gravity, dpipe is the hydraulic diameter of pipe, and VL is thevelocity of the liquid.

Several other factors are also considered in the Pots model (Table 6.5). The corrosion rates predictedby this model agree with the de Waard and Milliams model at flow velocities below 1 m/s and non-scaling conditions. At higher flow velocities (typically above 3 m/s), the corrosion rates predicted bythis model are lower than those predicted by the de Waard and Milliam model. It is rationalized thatunder higher flow conditions, the charge transfer kinetics determine the corrosion rate; therefore the ratespredicted by this model are more realistic than those predicted only on the basis of the diffusion process.

The Pots model further predicts the corrosion rate in a sour environment based on that in the sweetenvironment (Eqn. 6.31):

Ccorr:sour ¼ PF:Ccorr:sweet (Eqn. 6.31)

where PF is the pitting factor (Table 6.6), Ccorr.sour is the corrosion rate in the sour environment andCcorr.sweet is the corrosion rate in the sweet environment.

6.3.10 The Garber model46

The Garber model is based on the analysis and optimization of operating conditions in gas condensatewells. This model predicts corrosion rates based on field operating conditions, temperature and flow rates.

Table 6.5 Factors Considered in the Pots Model41

Factors Effect Corrections Remarks

Oil May inhibitcorrosion byentraining water

• Critical velocity water toentrained into oil is between1 to 1.5 m/s

• At these flow rates, below 40%water cut oil may protect thesurface

• Water may drop out fromcondensates even at 2.5 m/s

Based on main oil linein Oman

Water-sweep rate Oil may sweepwater out from lowareas

• At and above a Froude numberof 2, water will be entrainedfully in oil

See Eqn. 6.30

Alcohol Glycol or methanole when used ashydrate inhibitors ealso reducescorrosion

• Corrosion rate may decreaseby an order of magnitude

Similar to de Waardand Milliam’s model

Sour May increase pittingsusceptibility

• Pitting factor is a function ofchloride ion and elementalsulfur

Oxygen in sourenvironment

Increases corrosionrate

• When elemental sulfur forms, itis assumed that oxygen ispresent in the system becauseH2S reacts with oxygenproducing sulfur

Oxygen in sweetenvironment

• Treated as a separatemechanism.

• Oxygen dissolved in glycol andmethanol increases corrosionrate

Organic acids Increase corrosion • In the field in the presence of150 to 700 ppm of organicacids, corrosion rates as highas 5 mm/y were observed

Table 6.6 Values of Pitting Factor (PF) used in Pots Model to Convert Sweet

Corrosion Rate into Sour Corrosion Rate

Chloride, ppm

FP

Scale e No Scale e Yes

Less than 500 0.73 1.7

500 to 5,000 1.1 3.8

Greater than 25,000 2.6 6.1


6.4 Pitting corrosion of CRAS 329

6.4 Pitting corrosion of CRASCRAs normally do not suffer from general corrosion, but they may be susceptible to pittingcorrosion (see section 5.5 for the mechanism of pitting corrosion). Pitting corrosion is thefundamental form of localized corrosion; therefore predicting it provides a method for predictingother forms of localized corrosion. For these reasons, several approaches are used to evaluate thesusceptibility of CRAs to pitting corrosion, and these may be broadly classified into: PittingResistance Equivalent Number (PREN), standard laboratory methodologies, and electrochemicalmodels.

6.4.1 PRENThe Pitting Resistance Equivalent Number (PREN) provides a qualitative method for predicting thesusceptibility of CRAs to pitting corrosion. It is calculated on the basis of the chromium (Cr),molybdenum (Mo), tungsten (W), and nitrogen (N) content of an alloy. PREN is commonly used torank the susceptibility of stainless steel and nickel-base alloys, and is defined as (Eqn. 6.32):47

PREN ¼ Crþ 3:3ðMoþ 0:5WÞ þ 16N (Eqn. 6.32)

Larger numbers indicate higher resistance to pitting corrosion. PREN is only considered as a goodstarting indication of susceptibility to pitting corrosion. The PREN values does not take into accountthe variation of the surface metallurgy (e.g., duplex stainless steel with varying ferrite and austeniteratios). Other definitions of PREN that are slightly different from Eqn. 6.32 are also available.

6.4.2 Laboratory evaluationIn this approach, it is assumed that values of certain parameters can be used to predict the susceptibilityof CRAs to pitting corrosion. Laboratory experiments need to be carried out under simulated fieldconditions. Commonly used such parameters include critical pitting potential (CPP) and critical pittingtemperature (CPT). The CPP is the minimum anodic potential at which stable propagating pittingoccurs; the more noble this potential is, the less susceptible is the CRA to pitting corrosion. Similarlythe CPT is the minimum temperature at which stable propagating pitting occurs.

Standards providing procedures to evaluate the susceptibility of CRAs to pitting corrosion and todetermine values of CPP and CPT include:

• ASTM G150, ‘Standard Test Method for Electrochemical Critical Pitting Temperature Testing ofStainless Steel’

• ASTM F746, ‘Standard Test Method for Pitting or Crevice Corrosion of Metallic SurgicalImplant Materials’

• ASTM G61, ‘Standard Test Method for Conducting Cyclic Potentiodynamic PolarizationMeasurements for Localized Corrosion Susceptibility of Iron-, Nickel-, or Cobalt-based Alloys’

• ASTM G100, ‘Standard Test Method for Conducting Cyclic Galvanostaircase Polarization’• ASTM G59, ‘Standard Test Method for Conducting Potentiodynamic Polarization Resistance

Measurements’


6.4.3 Electrochemical modelsIn this approach the pitting corrosion process is sequenced and theoretical equations are derived fromelectrochemical principles to predict the susceptibility of ametal or alloy to pitting corrosion.Asdiscussedin section 5.5, the surface layer is inherent for CRAs. This air-formed surface layer is usually the oxide ofthe metal or alloy that is compact, adherent to the metal surface, and protects it from further corrosion.When this surface layer breaks down, themetal or alloy is susceptible to pitting corrosion. This takes placein three distinguishable stages: formation of a passive film on the metal surface; initiation of pits atlocalized regions on the metal surface where film breakdown occurs; and propagation of pits.

It should be noted that very sophisticated and detailed analysis of electrochemical parameters hasbeen carried out, in order to understand pitting corrosion and to develop complex models. Onlyselected features and basic principles of certain models are presented in this section. Readers areencouraged to review the original papers for detailed calculations, and to view other equally importantelectrochemical models.48

6.4.3a Passivity modelsPassive films generally form as bi-layers, with a compact layer adjacent to the metal and an outer layercomprised of a precipitated phase. Since passivity is still observed in the absence of the outer salt layer,passivity is attributed to the compact inner layer. The outer layer may incorporate anions and cationsfrom the solution. The outer layers are often unstable. Although these layers can have a dramatic effecton the inner layers, their influence is not considered in the passivity models. Only selected passivitymodels are presented in this chapter.

i The Griffin model49

The Griffin model relates the features measured during electrochemical test (i.e., cyclic voltammetry –see section 8.2.2) to the passivation process. The assumptions made in this model are that the oxidativehydrolysis of surface metal atoms produces adsorbed cations, and these cations dissolve away from theelectrode surface; passivation occurs when the rate of cation dissolution decreases as the cationcoverage increases; and the only feature that distinguishes an isolated adsorbed cation from a cation inthe oxide layer is the presence of a full complement of nearest-neighbor cations. The Griffin modeldefines equations to determine the factors that stabilize the passive layer.

ii The Fleischmann model50

Defining passivation as the consequence of an ordered monomolecular two-dimensional film of a def-inite chemical phase, Fleischmann proposed that measurement of current at high frequencies at constantpotential will yield valuable information about the initial stages of passivation; the passivating filmgrows two-dimensionally on surfaces; the passivating centers are cylindrical in shape; and the nuclei ofthe passivating film are distributed over the surface in a completely random manner (i.e., the predictionof passivation should involve a statistical approach) (see Fig. 5.9). The Fleischmann model presents rateconstants to determine the probability for the nucleation of the passivation films and their growth.

iii The Sato model51

The Sato model assumes that the surface oxide is an ordered structure and that the rate of itsformation can be calculated using concentrations of chemical species in solution, activationenergies for passive film formation, and potential gradients across the passivating films. The Sato


model defines equations to calculate thermodynamic parameters for passive film formation usingpotentiostatic and galvanostatic tests.

iv The Sarosala model52

The Sarosala model assumes that a solid insulating passive film spreads over the surface from randomnuclei, and that the rate of reaction depends on the resistance of the electrolyte in the defects or pores inthe passive film. This model provides equations to calculate the thickness of the passivating film andthe degree of surface coverage.

v The Macdonald model53

Macdonald advanced a ‘point defect’ model (PDM) to explain the growth and breakdown charac-teristics of passive films. The assumptions of this model are that whenever the external potential ismore noble than the passivation potential, a continuous passive film will form on the surface of a metal;a passive film is an oxide of the metal; the passivation potential is the lowest potential at which themetal can be covered by an oxide film; the passive film contains a high concentration of point defects,metal vacancies, electrons, and holes; the passive films have high electrical fields (106 V/cm); theelectric field strength depends on the chemical and electrical characteristics of the film but not on filmthickness; and the rate-controlling step is the transport of the vacancies across the film. The Macdonaldmodel provides equations to calculate the passive film thickness.

vi The Ambrose model54

The Ambrose model describes events that take place following rupture of passive film. According to themodel, the repassivation process involves film coverage as well as anodic metal dissolution; the rate ofrepassivation determines the corrosion morphology; and low rates of repassivation lead to considerableactive metal dissolution, leading in turn to pit initiation. This model provides equations to calculate thepenetration rate of localized corrosion immediately after mechanical passive film rupture.

6.4.3b Initiation modelsThere is much current debate concerning the initiation of pitting corrosion. One approach puts anemphasis on the inherent microscopic defects on the metal surface including grain boundaries, in-clusions, and scratches (a priori), whereas the other acknowledges that a non-uniformity on the metalsurface and its development to visible dimensions occur after a passive metal is placed in a corrosivemedium (a posteriori). The priori approach assumes that heterogeneities are present on the passivemetal, whereas the posteriori approach assumes an induced heterogeneity after the metal has beenplaced into the corrosive medium. In connection with a posteriori assumptions, the significance ofstochastic or fluctuation processes have been stressed as the initial step in pitting. Experimental pitinitiation results show significantly more scatter than those for other types of corrosion, although theorigin of this scatter is still open to discussion.55

i The Okada model56

The Okada model describes a passive metal in a corrosive environment where aggressive ions aretransported and adsorbed through the passive film with spatial fluctuations. According to this model,the metal ions initially dissolve uniformly through the passive film, and the resultant passage ofcurrent transports halide ions. If this transport is perturbed for some reason, the metal dissolution


rate changes locally causing variations in the ion flux; if the perturbation increases with time, thepassive film is destroyed locally and pits initiate; however, the passive metal dissolves uniformly andno pits initiate; if the perturbation decreases. Increase or decrease in the disturbance determineswhether pits initiate or not. Thus, the pit initiation process is a probability event. This model pro-vides equations to calculate the transfer rate of solution species (e.g., chloride) initiating corrosionacross the passive film.

ii The Shibata model57

The Shibata model considers pit initiation as a stochastic, i.e., random, process. This model presentsseveral equations to calculate the probability of pit initiation. The model application requires labo-ratory tests to determine the time needed for a pit to initiate. The data must be fitted to Shibata modelequations by numerical or graphic simulation. From the best fit, the influence of film properties, suchas thickness, on pit initiation may be inferred.

iii The Baroux model58

The Baroux model analyzes pit initiation in two stages: pit nucleation and pit initiation. Nucleationof pits leads to local breakdown of the passive layer, resulting in direct contact between the basemetal and the corrosive solution. The current increases markedly, and some of the dissolved metalliccations are hydrolyzed, resulting in local acidification. If this dissolution current is high enough tomaintain sufficient acidity despite the cation dissolution into the bulk solution, the nucleated pitscannot repassivate. Thus, the pit nucleation process is described as a deterministic process char-acterized by an incubation time. The second stage is known as the stabilization process, and isconsidered to be probabilistic in nature. The Baroux model provides equations to determine the rateof pit generation.

iv The Salvarezza model59

According to the Salvarezza model, the system becomes irreversible, and stable pits are formed whenthe current exceeds a given value. On the other hand, if the current does not exceed this value, then thepits will be repassivated. The rate of pit nucleation strongly depends on the properties of thepassivation layer and the presence of inclusions at that point. The Salvarezza model provides equationsto determine the frequency of nucleation and the probability of pit death.

v The Williams model60

The Williams model is based on the assumptions that the initiation of pitting corrosion requires theproduction and persistence of gradients of acidity and electrode potential on the surface of the metal;fluctuations in the gradients, leading to the birth and death of events, could arise because of fluctuationsin the boundary layer in the liquid at the metal surface; a pit becomes stable when its depth signifi-cantly exceeds the thickness of the solution boundary layer; the solution boundary layer has two parts,one part being defined by the roughness of the surface, and the other by the hydrodynamic boundarylayer; local acidification would arise as a result of the hydrolysis of metal ions in the solution due toslow dissolution of the passive metal; this passive metal dissolution current varies over the surfacebecause of inhomogeneities in the alloy composition or because of inclusions; a critical local pH isrequired for the initiation of pitting corrosion; and the nucleation rate depends on the time required toestablish this critical local pH. The Williams model provides equations to determine the frequency ofnucleation and the probability of pit death.


vi The Bertocci model61

The Bertocci model explains that the current transients are due to breakdown. This breakdown causesanodic oxidation of the exposed metal, part of which forms a new passive film. The passive film isweak and more susceptible to further breakdown at least in the initial stage, and for a limited time. Thismodel provides equations to determine the probability of passive layer breakdown.

vii The Oldfield-Sutton model62

Oldfield-Sutton developed a deterministic approach to characterizing localized corrosion. This modelwas originally developed for the crevice corrosion of stainless steels, but the theoretical treatment canbe applied to pitting corrosion and to other metals.

According to this model, pitting corrosion occurs in four stages: the environment becomes de-oxygenated due to restrictions on the transport of oxygen or other corrosive species; the cathodicreaction switches to the outside of the crevice; the solution inside the crevice becomes sufficientlyaggressive for the permanent breakdown of the passive film and the onset of rapid corrosion; and thecrevice begins to propagate. This model provides equations to calculate the critical pH at which thecorrosion progression is continuous.

viii The Pickering model63,64

The Pickering model assumes that the electric field varies through the surface layer and across itsthickness. The electric field within the film may be calculated by solving a boundary value problem.The surface layer breaks down to initiate pits when the electrostatic pressure at the film/solutioninterface exceeds the compressive film strength. This model provides equations to determine the depthof the pit that is active, and hence is susceptible to propagation.

ix The MacDonald model65,66

MacDonald further developed the deterministic model (see section 6.4.3a.v) to describe the statisticalnature of passive film breakdown and pit nucleation. According to the model, a solute-vacancyinteraction is needed to account for the effects of minor alloying elements, such as Mo and W, onthe pitting resistance of iron-based alloys; the passivity breakdown occurs because of an enhanced fluxof cation vacancies from the film/solution interface to the metal/film interface. The excess vacanciesarriving at the interface between the metal and the film cannot be absorbed into the metal at a suffi-ciently high rate; accordingly, the vacancies accumulate to form a vacancy condensate at the metal/filminterface, which then grows to a critical size; and the film then collapses locally to form a pit that willcontinue to grow if conditions are not conducive for repassivation. The MacDonald model presentsequations to calculate the surface layer breakdown.

6.4.3c Propagation modelsThe final stage of pitting corrosion is the formation and continuous propagation of stable pits. Inpropagation models, the growth of pits is treated as deterministic. When the pit becomes sufficientlylarge, the conditions and processes taking place are closely related to those occurring in crevicecorrosion. Several pitting corrosion models are available to predict pit propagation,67–76 but only fivepit growth rate models are presented here as illustration. Also detailed equations of these models arenot presented; the reader can obtain the information from the references. Characteristics of five pitgrowth rate models are described in the following paragraphs.


i The Tester model77

The Tester model distinguishes two distinct phases of growth of pits in nickel and stainless steel inconcentrated chloride solutions. According to this model, the electrode surface is flush with the surfaceof the cavity in the early period of dissolution, and a semi-infinite approach has been used to model thediffusion process that causes the electrolyte concentration to increase at the metal surface. After theinitial period of saturation of the dissolving cation species at the metal-solution interface, a quasi-steady state period of diffusion control commences. The time-dependent depletion of corrosive spe-cies can be obtained from this model.

ii The Beck model78

The Beck model proposes that the geometry and migration effects exert opposite and nearly equalinfluences on the dissolution rate, and that these cancel one another out. Using this model, the depth ofpit can be calculated as function of time.

iii The Ateya model79

The Ateya model analyses the effects of ohmic, mass transfer, and concentration polarizations on thecurrent, concentration, and potential profiles inside a pit or crack in iron, nickel, and copper. The modelassumes that negative potentials are maintained within the pits so that cathodic reactions occur only atthe outer surface; the depth of the pit is greater than its width; and mass transfer in the electrolytewithin the pit takes place by molecular diffusion and ionic migration. Using the model the depth orwidth of pits can be callused as function of time.

iv The Ben Rais model80

The Ben Rais model calculates the current as a function of time for aluminum undergoing pittingcorrosion. The model assumes that the bulk solution is very dilute and saturated at the bottom of thepit. However, this model does not consider the effects of supersaturation, pH variation, ionic strength,or metal hydrolysis. From the current, the model predicts pit depth as a function of time.

v The Galvele model81–83

The Galvele mdoel analyzes the transport processes for pitting, and proposes that surface layerbreakdown is associated with depletion of hydroxyl ions at the metal-solution interface. The modelcalculates the change of pH, and from the change in pH predicts pit depth as a function of time.

6.5 Localized pitting corrosion of carbon steelSection 6.3 discusses sweet and sour corrosion of carbon steel models developed to primarily addressgeneral corrosion of carbon steel under oil and gas industry operating conditions. Failures of carbonsteel in oil and gas industry operational conditions rarely occur due to general corrosion; almost allfailures are localized pitting corrosion. Although some models presented in section 6.3 recognize theimportance of localized corrosion, they do not adequately describe localized pitting corrosion pro-cesses, nor do they present equations to predict them. More importantly these models do not explainwhy certain locations suffer from pitting corrosion whereas the neighboring areas are intact. Themorphology of corrosion features may include circular depressions, usually with tapered and smooth

6.5 Localized pitting corrosion of carbon steel 335

sides (often described as pits); stepped depressions with a flat bottoms and vertical sides (often referredto as mesa corrosion); and formation of silts (sometimes referred to as knife line); and parallel groovesextending in the flow direction (commonly known as flow-induced localized corrosion (FILC)).84

Some characteristics of localized pitting corrosion of carbon steel corrosion include the fact that nointact passive or surface layer is present when the carbon steel material is first placed into service(unlike CRAs in which the passive layer is an integral part of the metal (see section 6.4)); initiallycarbon steel undergoes uniform or general corrosion, and when sufficient amounts of iron carbonate oriron sulfide surface layer form on the carbon steel, the corrosion rate decreases. (The kinetics of FeSformation is higher than that of FeCO3 formation). The types, morphology, and kinetics of layerformation depend on several operating variables, including flow rates and compositions of oil, gas, andbrine, partial pressures of acid gases, total pressure, temperature, microbial activities, and duration ofoperation (typically the incidences of failure are higher in the initial years of operation(Figure 6.20));85 and the failures are localized and are characterized by loss of metal over discreteareas of the surface, with surrounding areas essentially unaffected or subject to general corrosion.

All these observations may be explained by assuming that the corrosion of carbon steel may proceedthrough a non-classical, localized, pitting corrosion (NCLPC) mechanism. The localized pittingoccurring in carbon steel could be non-classical because the primary passive layer that is traditionallyassociated with pitting corrosion of CRAs (see section 6.4) is not present. But, intact surface layers mayform during operation. This layer may be considered as the precipitated outer layer as defined in theclassical pitting corrosion mechanism. More importantly, carbon steel undergoes uniform corrosion until

FIGURE 6.20 Failure Statistics of 150 Sour Gas Pipeline Failures between 1998 and 2002 in Alberta, Canada.85

(Note: 55% of failures happened within first three years of operation).


the surface layer is formed. No corrosion occurs if the surface layer completely and permanently coversthe entire surface. The addition of corrosion inhibitors and the presence of an oil layer on the metalsurface may offer additional protective layer or may reinforce the surface layer. More importantly carbonsteel may suffer localized pitting corrosion when the surface layer is incomplete.

Table 6.7 presents a general overview of various models used to predict the general and localizedcorrosion of carbon steel, and a model used to predict the localized pitting corrosion of carbon steel ispresented in the following section.

6.5.1 The Papavinasam model86–99

The Papavinasam model first predicts locations which are susceptible to localized pitting corrosion. Asdiscussed in section 5.2, wet corrosion occurring under electrochemical principles requires the pres-ence of water (i.e., a conductive, electrolytic phase). Therefore the locations where water accumulatesare those susceptible to corrosion. Flow establishes locations where water accumulates. Standardsproviding guidelines for determining such locations:

• NACE SP0206, ‘Internal Corrosion Direct Assessment Methodology for Pipeline CarryingNormally Dry Natural Gas (DG-ICDA)’

• NACE SP0208, ‘Internal Corrosion Direct Assessment Methodology for Liquid Pipelines(Liquids-ICDA)’

• NACE SP0110, ‘Internal Corrosion Direct Assessment Methodology for Pipeline Carrying WetGas (WG-ICDA)’

The calculations presented in these standards are used in the model under the following conditions: theequation presented in DG-ICDA standard is used when the gas-to-liquid production rate ratio is higherthan 5,000, i.e., when: �

P:R:gasP:R:oil þ P:R:water

�> 5000 (Eqn. 6.33)

where P.R.gas, P.R.oil, and P.R.water are the production rates of gas, oil, and water respectively.The equations presented in Liquids-ICDA standard are used when 95% of the fluid is oil, i.e., when:�

P:R:oilP:R:oil þ P:R:water þ P:R:gas

�> 0:95 (Eqn. 6.34)

Multiphase flow is assumed under conditions where Eqn. 6.33 and 6.34 do not apply. Section 4.2describes multiphase flow, types of flow regimes, and their characteristics. The information presentedin section 4.2 is used to establish locations where water accumulates in multiphase flow.

The localized pitting corrosion rate is predicted once the locations of water accumulations areestablished. The model assumes that the carbon steel surface is susceptible to corrosion onlywhen in contact with water. If the products of corrosion dissolve in the environment then thecorrosion rate will be uniform, i.e., general corrosion will occur. If the corrosion product layersdeposit and form intact layers that cover the surface (commonly known as surface layers), thenthe corrosion rate will decrease to a minimum or negligible value. Either of the two extremeconditions, i.e., uniform corrosion or the formation of an intact, compact, and protective layerseldom occurs.

Table 6.7 An Overview Models to Predict Sweet and Sour Corrosion)

Parameters

de Waard

and Milliam Srinivasan Crolet Nesic Mishra Dayalan Anderko Oddo Pots Garber Papavinasam

CO2 Partial

pressure

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

H2S Partial

pressure

No Yes Yes No No No Yes No No No Yes

Total

pressure

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Temperature Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Ph Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Flow rate Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Scale factor Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes

Water

wetting

Yes Yes Yes Yes No No No No No No Yes

Ca2þ No Yes Yes No No No No Yes Yes No No

Bicarbonate Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Acetic acid No No Yes Yes No No No No No Yes Indirect

Field data No Yes Yes No No No No Yes Yes Yes Yes

Localized

pitting

corrosion

rate

No No Indirect No No No No No Indirect No Yes

Unique

characteristics

Nomogram User-friendly

Software

Field

experience

Mechanistic

model

Chemical

model

Simulation Simulation Effect of

inorganic

scale

Effect

of flow

Field

data

Field

validation

)Based on the version presented in the reference cited; later version of the models may have additional features

6.5

Localize

dpittin

gcorro

sionofcarbonste

el

337


In practice, the carbon steel becomes susceptible to localized pitting corrosion when the followingsequence of events occurs:

• Surface layers (single or multi) are formed due to corrosion of carbon steel exposure to theenvironment;

• Under the operating conditions of most oil and gas infrastructure either one or the combinationsof all of the following three surface layers are possible: iron oxide, iron carbonate, and ironsulfide;

• Surface layers are removed at localized areas of the steel surface, but the surface layers are left inthe rest of the surface;

• The areas where the protective layer are removed become anodic with respect to the rest of thesurface and the surrounding areas become cathodic; and

• The corrosion reaction taking place at the localized anodic areas is insufficient for completereformation of the surface layer.

If this sequence of events occurs, then pits initiate, propagate, and can lead to premature failures. Theprobability and the rate of the localized pitting corrosion depend on the stability of local anode andbulk cathode and their relative areas. Complete removal of the surface layer may be beneficial, becauseunder that condition the anode and cathode ratio are uniformly distributed, resulting in uniformcorrosion (as opposed to localized corrosion). Parameters influencing the above sequence, as includedin the model, are discussed in the following paragraphs.

6.5.1a Effect of carbon steel gradeMinor alloying elements can have a profound effect on the susceptibility of a metal or alloy tolocalized corrosion, including pitting (see section 4.5.3). In general, if a micro-alloying element that isanodic to carbon steel is present on the steel surface, then there is a greater possibility that the passivelayer formed right on top of the micro-alloying element is less stable, producing an area susceptible topit initiation, i.e., susceptible to the initiation of anodic site, leading to stabilization of smaller localanodic areas surrounded by larger cathodic areas. Although carbon steels differ in composition, theircorrosion performance is considered to be similar in the model.100

6.5.1b Effect of oil-water emulsionThe probability of corrosion is low if water forms an emulsion with oil. There are two types of emulsion:oil-in-water (O/W) and water-in-oil (W/O). In W/O emulsions, the oil is the continuous phase; thereforeits conductivity is low, so it does not sustain corrosion. On the other hand, the probability of corrosion ishigh in O/W emulsions, because in this type of emulsion, the conductive water is the continuous phase.Procedures discussed in section 4.3 are used to predict the occurrence of this type of emulsion.

6.5.1c Effect of oil wettabilityThree categories of surface can be established, depending on the affinity of the oil for the carbon steel:an oil-wet surface, a water-wet surface and a mixed-wet surface. Mixed-wet and water-wet surfacesare susceptible to corrosion, but an oil-wet surface is not. The procedures discussed in section 4.3 areused to predict the type of wettability.

6.5.1d Effect of wall shear stressThe production rates of oil, water, and gas affect the flow rate. The effect of flow rate is considered inthe model using wall shear stress (WSS). The calculation of WSS described in section 4.2 is used for thispurpose.


6.5.1e Effect of solidsTwo detrimental effects of solids are considered in the model. In low flow regimes, deposits may formon the surface and produce conditions for underdeposit corrosion. Under moderate flow conditions,solids may abrade the surface leading to a surface profile conducive for pitting. In both situations, thepresence of solids increases the probability of localized pitting corrosion. The locations where solidsdeposit considered in the model are based on the solid deposition model discussed in section 4.2.4. Inhigher flow regimes, the presence of solids leads to erosion-corrosion. However, the effect of erosion isnot included in this model.

6.5.1f Effect of temperatureThe effect of temperature is manifold. Higher temperatures generally increase the corrosion ratebecause of the accelerated electrochemical and chemical reactions. However, the rate of precipitationincreases with temperature; hence, elevated temperatures reduce the corrosion rate at which protectivelayers are formed. The influence of temperature on protective layer formation depends on whether theprotective layer is physically or chemically adsorbed. For layers that physically adsorb onto the metalsurface, protection decreases with increasing temperature, because elevated temperature facilitatesdesorption. For those layers that chemically adsorb onto the metal surface, the chemical bond strengthincreases with temperature, and hence, protection increases with temperature up to the point afterwhich thermal degradation of the layer occurs. In addition, increasing temperature increases thediffusivity of both pitting (e.g., chloride ions) and inhibitive species (e.g., corrosion inhibitors, sulfateions) across the surface layer.

6.5.1g Effect of pressurePressure exerts two opposing effects. It may increase the corrosion rate if it increases the dissolution ofmetal, and it may decrease the corrosion rate if it facilitates the formation of intact surface layers.

6.5.1h Effect of H2S partial pressureThe acid formed by the dissolution of H2S is about three times weaker than that formed by thedissolution of CO2 (i.e., carbonic acid), but H2S is about three times more soluble than CO2 gas.As a result, the contributions of CO2 and H2S partial pressures in lowering the pH are basically similar.The effect of H2S on pitting corrosion rate depends on the formation and stability of iron sulfides.

6.5.1i Effect of sulfate ionThe sulfate ion effect is predominant only in the presence of H2S. Sulfate ions may inhibit localizedpitting corrosion by developing a sulfate layer.

6.5.1j Effect of CO2 partial pressureDissolution of gaseous CO2 leads to the formation of carbonic acid. This weak acid reacts with iron toform iron carbonate. The effect of CO2 on the localized pitting corrosion rate depends on the formationand stability of iron carbonates. With increasing partial pressure of CO2 the surface layers becomecompact; as a result the localized pitting corrosion rate decreases with CO2 partial pressure.

6.5.1k Effect of bicarbonate ionThe bicarbonate ion effect is prominent only in the presence of CO2. Carbon dioxide dissolved in watercombines with it to form carbonic acid. The carbonic acid then dissociates into hydrogen and bicar-bonate ions. The excess bicarbonate shifts the equilibrium to the left; i.e., it decreases the formation ofcarbonic acid and thereby decreases the rate of CO2 corrosion. Another effect of bicarbonate is its

Table 6.8 Effect of Individual Parameter on Localized Pitting Corrosion Rate as per Papavinasam Model

Effect of Localized Pitting Corrosion Rate Equation

Oil PCRoil ¼ �0:33qCAw þ 55

Water PCRwater ¼ 0:51:W%þ 12:13

Gas PCRgas ¼ 0:19Wss þ 64

Solid PCRsolid ¼ 50þ 25Rsolid

Temperature PCRtemperature: ¼ 0:57T þ 20

Pressure PCRpressure: ¼ �0:081P þ 88

Partial pressure of H2S PCRH2S ¼ �0:54pH2Sþ 67

Partial pressure of CO2 PCRCO2¼ �0:63pCO2 þ 74

Concentration of sulfate ion PCRsulphate: ¼ �0:013½SO2�4 � þ 57

Concentration of bicarbonate ion PCRbicarbonate ¼ �0:014½HCO�3 � þ 81

Concentration of chloride ion PCRchloride ¼ �0:0007½Cl�� þ 9:2


t

tt

.

buffering action. The bicarbonate buffer can absorb Hþ ions and neutralize the acids produced by CO2

dissolution.

6.5.1l Effect of chloride ionPitting is most commonly induced by chloride ions. Like other halides, chloride ion is a very potenagent for destroying the surface layers. Therefore an increase in chloride ion normally increases theprobability of localized pitting corrosion.

6.5.1m Combined effectsEach one of the parameters discussed in sections 6.5.1a through 6.5.1l can individually influence thelocalized pitting corrosion rates. Extensive laboratory and field tests have been carried out to quantifythe individual effect of each of the parameters. Based on the test results, individual rate equations havebeen developed for each parameter. Table 6.8 presents these equations. The ultimate rate at which thepits will propagate depends on the combined effect of all of the operational parameters. Whereas theindividual effect of each of the parameters can be predicted deterministically, determining the com-bined effect of these variables requires the application of statistical principles, because the drivingforce for the pitting corrosion is a ‘distributed parameter’.

It is assumed that each operational variable produces an individual pit growth rate (resulting in 11different pitting corrosion rates, Table 6.8) and that the pit growth rate that results from variables thahave not been considered (e.g., acetic acid effect) is assumed to be the mean value of these 11 pigrowth rates, i.e., PCRadditional. The PCRadditional parameter does not have any effect on the predictedpitting corrosion rate; however, it does increase the uncertainty of the prediction. The feature of thismodel enables one to include the effects of additional parameters without vastly modifying the modelThe actual localized pit growth rate taking place is the ‘distributed function’ that is the mean value ofthe 12 pit growth rates. The resulting pit growth rate is the rate at which the pits will start to grow in thelocalized anodic region where the surface layers are removed.


.

.

Using the 11 calculated pitting corrosion rates (Table 6.8) and PCRadditional, the mean initial pittingcorrosion rate, PCRmean, is calculated using: Eqn.6.35

PCRmean ¼

PCRoil þ PCRwaterPCRgas þ PCRsolid þ PCRtemperature þ PCRPressure þ PCRH2S

þ PCRsulphate þ PCRCO2þ PCRBicarbonate þ PCRChloride þ PCRAddition

12

(Eqn. 6.35)

The standard deviation obtained based on Eqn. 6.35 presents uncertainty of the prediction. Themagnitude of standard deviation represents the uncertainty in predicting the formation of smalleranodic area surrounded by large cathodic areas (which in turn represents the uncertainty in predictinglocalized pitting corrosion).

6.5.1n Effect of durationPitting corrosion does not progress at a constant rate for various reasons, including reformation of thesurface layers, local solution saturation, change of corrosion potential, and local increases in pH. Nor-mally the pit growth rate diminishes parabolically as a function of time. If the operating conditions areconstant over the years, the average pitting corrosion rate for multiple years is calculated using Eqn.6.36

PCRaverage ¼PCRmean

1 þ PCRmean

2 þ PCRmean

3 þ.::þ PCRmean

t

t(Eqn. 6.36)

where 1, 2, 3 etc. are years 1, 2, 3 etc., respectively and t is the number of years for which the localizedpitting corrosion rates is predicted.

If the operating conditions change for a particular year the ‘value of ‘t’ is set to unity for that yearand the ‘t’ values for subsequent years increase as per Eqn.6.36. Table 6.9 provides the boundaryconditions to determine whether the operating conditions change or not.

6.5.1o Effect of flow regimeIn multiphase flow, the flow regimes may change the corrosion rate based on the duration in which thewater phase is in contact with the carbon steel surface. Therefore a correction factor is requiredTable 6.10 provides the correction factors to account for flow regimes (see section 4.2.2) on corrosionrate. These correction factors have been established based on the analysis of field data. The averagelocalized pitting corrosion rate predicted by Eqn. 6.36 is further adjusted using the correction factorpresented in Table 6.10, to produce PCRnon-mic. This rate includes all non-microbiological activitieswhich influence the localized pitting corrosion rate.

6.5.1p Effect of microbesSection 6.7.6 describes the Sooknah model as used to predict the risk of MIC. To account for MIC, thePCRnon-mic is modified according to Eqn. 6.37:

PCRfinal ¼ PCRnon-mic ��RMIC

50

�(Eqn. 6.37)

where PCRfinal is the localized pitting corrosion rate combining the effects of both non-MIC and MICactivities and RMIC is the risk factor due to MIC (see Table 6.11).101–103

Table 6.9 Boundaries to Determine Operating Conditions Changes in the

Papavinasam Model

Parameter Boundaries

Temperature (�C) Less than 25

Between 25 and 50

Greater than 50

Pressure (psi) Less than 100

Between 100 and 500

Greater than 500

H2S (psi) Less than 2.5

Between 2.5 and 10

Between 10 and 50

Greater than 50

CO2 (psi) Less than 2.5

Between 2.5 and 10

Between 10 and 30

Between 30 and 100

Greater than 100

SO42� (ppm) Less than 750

Between 750 and 1000



Greater than 2500

HCO3� (ppm) Less than 500




Greater than 4000

Cl� (ppm) Less than 10000

Between 10 000 and 20 000




Between 80 000 and 100 000

Between 100 000 and 120 000

Greater than 120 000


Table 6.10 Variation of Localized Pitting Corrosion Rate as a

Function of Flow Regimes According to the Papavinasam Model

Flow Regime Type PCRnon-mic

Slug Flow No Change

Plug Flow PCRAverage x 0.98

Bubble Flow PCRAverage x 0.96

Dispersed Flow PCRAverage x 0.94

Oscillatory Flow PCRAverage x 0.92

Annular Flow PCRAverage x 0.90

Churn Flow PCRAverage x 0.88

Wave Flow PCRAverage x 0.86

Stratified Flow PCRAverage x 0.84

Table 6.11 Risk Scores for MIC According to the Sooknah Model101,102

Influence ofParameter

Range ofParameter

Unit ofParameter

MIC RiskScore Remarks

Flow rate Above 3 m/s 1

2e3 2e12

1e2 12e18

0e1 18e20

Temperature Less than �10 �C 0

�10e15 1

15e45 7e10

45e70 7e4

70e120 4e1

Above 120 0

Acid gas partialpressure

Greater than 20 pCO2/pH2S 10 Only if the H2S content isgreater than 10 mol/kmol(1%) by volume.

Less than 20 2

pH Less than 1 0

1e4 5

4e9 10

9e14 1

Above 14 0

LangelierSaturationIndex (LSI)

Less than �6 10

�6 e �1 10e5 MIC tendency decreasesas the LSI valueincreases in the negativedirection because thetendency of non-MICincreases.

�1e1 0

Continued


Table 6.11 Risk Scores for MIC According to the Sooknah Model101,102 Continued

Influence ofParameter

Range ofParameter

Unit ofParameter

MIC RiskScore Remarks

1e8 1e8 MIC tendencyincreases as the LSIvalue increases asmore scales areformed.

Greater than 8 8

Total SuspendedSolids (TSS)

Present 10 If the flow rate is between0 to 3 m/s.

Present 0 If the flow rate is above 3m/s.

Absent 0

Total DissolvedSolids (TDS)

Less than 15,000 ppm 1

15,000e150,000 1e10

Greater than150,000

10

Redoxpotential (Eh)

Less than -15 mV 1

�15eþ150 1e10

Greater than 150 10

Sulfur Present 10

Absent 1

)The sum of all the MIC risks is 100


6.6 Erosion-corrosionA common guideline used in industry is API RP 14E, which defines the velocity below which erosiondoes not occur. Although the relevance of the API RP 14E erosion equation has been questioned, it isstill extensively used, as no other better tool is currently available. According to API RP 14E, above acertain velocity the gas flow may cause erosion to the pipe wall. The erosion velocity of a compressiblefluid is given as Eqn. 6.38:104

Ue ¼ affiffiffiffiffiffiffiffiffiDGL

p (Eqn. 6.38)

where Ue is the erosion velocity, ft/sec, a is a constant with values varying between 75 and 150 (for gastransmission pipeline it is assumed to be 100), and DGL is the gas and liquid mixture density. Thesevalues are reduced when solid particles like sand are present. For corrosive fluids, erosion-corrosionmay take place at velocities lower than those indicated by the formula. This formula is widely usedas a guideline in the oil and gas industry for setting limiting production velocities.

In addition to this standard, certain models, based on laboratory testing results, may be used topredict erosion-corrosion; some of which are presented in this section.

6.7 Microbiologically influenced corrosion 345

6.6.1 The Zhou model105

The Zhou model considers erosion as a mechanical process caused by the impact of solid particles on ametal surface, and corrosion as an electrochemical process caused by the environment. This modelconsiders synergistic interactions between erosion and corrosion. The synergism is defined as theexcess mass loss caused by erosion-corrosion over the sum of the masses lost by erosion and corrosionwhen the two processes act separately. It provides an erosion-corrosion enhancement factor (EFEC) forthe combined effects of erosion and corrosion:

ECEF ¼ 1þ 0:11U0:22 (Eqn. 6.39)

where U is the flow rate.

6.6.2 The Nesic model106

The Nesic model considers the effect of erosion when pipes of different diameters join. The effects oferosion-corrosion for a flow through an expanding pipe section are given in Eqns. 6.40 and 6.41:

For angle � 18:5�: Qerosion ¼mp

�upsina� Ucrit

�2r

(Eqn. 6.40)

� mp

�uPsina� Ucrit

�2cos2

For angle � 18:5 : Qerosion ¼12r

:sin2a

(Eqn. 6.41)

where Qerosion is the metal loss rate, m3; mp is the mass of particles; UP is the velocity of particles;Ucrit,E is the critical velocity constant (for erosion to occur) and normally assumed to be 0.668 m/s;rparticle is the density of particles; and a is the impact angle.

6.6.3 The Shadley model107

The Shadley model analyses erosion-corrosion of carbon steel elbows in CO2-containing solutions,and identifies three zones of erosion-corrosion: at low velocities the surface layer is intact and hencethe corrosion rate is low; at intermediate velocities, localized points where sand particles impinge deeppits form but the surface layer on the remaining areas of the elbow is intact; and at high velocities sandparticles impinge and prevent the formation of a surface layer on the entire surface of the elbow, andthe corrosion rate is high but uniform. Table 6.12 presents the boundaries between these three zones.

6.7 Microbiologically influenced corrosionSections 4.9 and 8.2.4 discuss mechanism and monitoring of MIC respectively. This section presentsspecific models to predict the occurrence of MIC.

6.7.1 The Checkworks model108,109

The Checkworks model serves as a guide to define and compare the potential for MIC at differentlocations. This model provides a susceptibility ranking for components or specific sites (e.g.,piping, elbows, welds, heat exchanger tubes, tube sheets, pumps, and valves). This model ranks the

Table 6.12 Boundary Conditions between Different Erosion-Corrosion Zones as

Predicted by the Shaldley Model107

pH

Boundary between LowCorrosion and Pitting CorrosionZones

Boundary between PittingCorrosion and Higher CorrosionZones

< 5.50:9�4:8dpipe

�W%2

0:5375P:R:Sand

2:5�4:8dpipe

�W%2

0:5375P:R:Sand

> 5.57�4:8dpipe

�W%2

9:5P:R:Sand

8:5�4:8dpipe

�W%2

9:5P:R:Sand

where dpipe is the pipe diameter, W% is percentage of water; and P.R.Sand is the production rate of sand


susceptibility or extent of the potential for MIC to occur. The ranking ranges from 0 (no susceptibilityor very low potential) to 10 (highest susceptibility or greatest potential for occurrence of MIC). Themodel is mathematically expressed as Eqn. 6.42:

RMIC ¼ ðMWF� TF� OF� BF� BD� DiFÞ1=a (Eqn. 6.42)

where MWF is the materials-water factor accounting for interaction of material and aqueous phase; TFis the temperature factor accounting for the change with temperature; OF is the operation factor ac-counting for the component being exposed to either continuously, intermittent, or stagnant flowconditions; BF is the biocide factor accounting for the use of biocides to control microbiologicalgrowth; BD is the biocide decay factor accounting for loss of effectiveness of biocide with distancefrom the point of application; DiF is the discontinuity factor accounting for the increased susceptibilityat discontinuities in construction such as at welds and crevices; and a is a constant.

This model provides a broad framework for field operators to rank and evaluate MIC susceptibility,without quantitatively providing any range of values for various factors.

6.7.2 The union electric model110,111

The Union Electric model provides a relative ranking for the likelihood of the occurrence of MIC. Thismodel evaluates the degree of severity of MIC under the operating conditions of the infrastructure.This model places great significance on the impact of data collection, and emphasizes that the inputdata are collected from the field. The model is expressed mathematically as Eqn. 6.43:

RMIC ¼ 4:5446

29� D½SRB�

4:83

�þ 4:86þ

�6� D½Clos�

�þ�9� D½Gall�

�1:5

þ SF þ VF (Eqn. 6.43)

where RMIC is the MIC risk (varies between 0–100); D[SRB] is the number of days for sulfate reducingbacteria cultures isolated from samples to turn positive; D[Clos] is the number of days for Clostridiacultures isolated from samples to turn positive; D[Gall] is the number of days for Gallionella cultures


t

.

.

t

,

isolated from samples to turn positive; SF is the silt factor representing the amount of sludge and silpresent at the sampling site (0 ¼ no silt; 1 ¼ moderate amount of silt and 3 ¼ high amount of silt); VFis the visual factor representing a visual estimate of the amount of deposit or tubercle formation at thesampling site (0¼ few or none, 1¼moderate soft tubercles/deposit and 3¼many large hard tuberclesand deposits); SRB is the sulfate reducing bacteria; CLOS is the Clostridia cultures and GALL is theGallionella cultures.

Based on the RMIC score, the MIC susceptibility is ranked as: low (0 to 25); moderate (26 to 50), high(51 to 75), and very high (76 to 100). However, the rationale for various constants used is not described

6.7.3 The Lutey model112–115

TheLuteymodel places significant emphasis on thepresenceof specific bacteria associatedwith corrosionThese bacteria are SRB; slime-forming bacteria; metal oxidizing bacteria, such as Gallionella sp. andmanganese oxidizing bacteria; and acid producing bacteria, such as Clostridium sp. Although otherbacteria may be present and contribute to MIC, only the four bacteria listed are considered as importanfor MIC. If there are specific data indicating that the MIC potential is related to other types of micro-organisms, such as nitrite oxidizing/ammonia producing bacteria or sulfur oxidizing bacteria, such asThiobacillus sp., these could be added to the input data. Other criteria included in the model are thedeposit and fouling factor, sedimentation factor, and materials of construction factor. These factors areconsidered as important because they influence the environment where the microorganisms exist.

According to this model:

RMIC ¼ MiFþ DFFþ FFþMF (Eqn. 6.44)

where MiF is the microbiological factor; DFF is the deposit or fouling factor; FF is the flow factor; andMF is the material factor.

Table 6.13 presents the scores for various factors as defined by this model. Based on the RMIC scorethe MIC susceptibility is ranked as: highly likely or severe (above 150), moderate (75 and 150), andlow (below 75).

6.7.4 The Pots model41

The Pots model predicts the occurrence of MIC by considering environmental conditions supportingmicrobial activity and biofilm formation, and operational parameters influencing microbiology. ThePots model calculates the MIC rate using Eqns. 6.45 and 6.46:

CRMIC ¼ a:f 0:57mic (Eqn. 6.45)

f 0:57mic ¼ fmic:1:fmic:2.:fmic:n (Eqn. 6.46)

where CRMIC is the corrosion rate due to microbiological activity, a is a constant (assumed to be 2mm/y), and fmic.1, fmic. etc. are factors influencing MIC. Table 6.14 presents values of fmic.

6.7.5 The Maxwell model116

Maxwell and Campbell modified the Pots model to include the influence of SRB cell numbers, SRBgrowth rates, sulfide production, water content, lesser efficiency of pigging in controlling MIC

Table 6.13 Scores for Various Factors as Defined by the Lutey Model112e115

Factor Value Score Remarks

Microbiological Absent 0 To be scored individually for fourtypes of microbes identified in thismodel. This model does not quantifythe amount of bacteria for differentcategories of scores.

Large amount 5

Depositor biofouling

Absent 0 The deposits or biofouling aremeasured or visually noted.Large amount 5

Flow Stagnant 0

Greater than 10 ft/sec 1

Between 4 and 10 ft./sec 3

Less than 4 ft./sec 5

Material Very resistant 1 This model does not quantify theamount of bacteria for differentcategories of scores. Both basemetal and welds are scoredseparately.

Very susceptible 7


formation of corrosive biofilm, lag period before biofilm develops, and non-MIC corrosion (i.e.,corrosion takes place due to non-microbial activity). The Maxwell model presents a correction factorto be included in the Pots model to account for biofilm development and sulfide production activity.Until a threshold value of the correction factor is reached, it is assumed that MIC does not occur. Oncethis threshold is reached, the MIC rate can be determined by the Pots model. Details of how todetermine the correction factor are described in the model.

6.7.6 The Sooknah model101,102

The Sooknah model establishes boundary conditions in which MIC occurs, and then provides quan-titative scores for the risk of MIC under those conditions.

All bacteria require a water phase to proliferate. According to this model, in the absence of waterMIC does not occur. If any minute amount of water wets the metal surface or deposits attached to themetal surface, then MIC is likely. The likelihood for the occurrence of water-wet surface is predictedaccording to the Papavinasam model (see section 6.5.1).

On a water-wet surface, MIC is likely when the temperature is between 14 and 250�F (-10 and120�C), organic and inorganic nutrients are available in the medium, and no antifouling procedures areregularly applied.

The quantitative MIC risk score is based on nine parameters: temperature, flow rate, pressure, pH,Langemuir Saturation Index (LSI) (see section 6.8.1), total suspended solids (TSS), total dissolvedsolids (TDS), redox potential (Eh), and sulfur content in solids. Each parameter is assigned a numericalvalue between 0 and 10, except for the flow rate, which is assigned a numerical value between0 and 20. Values approaching 0 signify low MIC risk, and values approaching the maximum signifyhigher MIC risk. The total MIC risk factor is calculated as the sum of scores of all nine contributing

Table 6.14 Values of fmic in Pots MIC Model41

ParametersValue of the FactorWhen True

Value of the FactorWhen False

pH between 5 and 9.5 1 0.001

Total dissolved solids (TDS) less than 60 g/l 1 0.2

If TDS greater than 60 g/l, do SRB grow 0.2 0.0001

Temperature (T) between 10 and 45�C? 1 0.2

Sulfate greater than 10 mg/l 1 0.2

Total carbon from fatty acid greater than 20 mg/L 1 0.2

Nitrogen (as utilizable N) greater than 5 mg/l 1 0.2

C:N ratio less than 10 1 0.4

Flow velocity less than 1 m/s 1

Flow velocity ¼ 2 m/s 0.6

Flow velocity ¼ 2.5 m/s 0.1

Flow velocity ¼ 3 m/s 0.01

Debris on bottom of pipeline 2 1

Pigging frequency, never 1

Pigging frequency, once 13 weeks 0.3

Pigging frequency, once 4 weeks 0.001

Pigging frequency, once a week 0.0001

Prolonged oxygen ingress, greater than 50 ppb 5 1

Biocide routinely used 0.2 1

Age of pipeline, less than half year 1

Age of pipeline, greater than half year,downtime 1 week

1

Age of pipeline, greater than half year, downtime50 weeks

2


parameters (Table 6.11). The rationale for the numerical risk of individual parameters is described inthe following sections.

6.7.6a Effect of flow rateThe flow rate influences the nature of biofilm formation and the rate of nutrient delivery. As flow rateincreases, layers of less adherent films are removed, so ultimately only strongly adherent films remainon the metal surface. As a consequence, biofilms become compact as flow increases. However, above acertain flow rate, most biofilms are stripped off and their reformation is limited. Many studies indicatethat biofilms are stripped off at flow rates between 2 and 3 m/s (6.5 and 9.8 ft/s). At the other extreme,stagnant flow promotes attachment and colonization of microorganisms onto the metal surface, fa-cilitates biofilm formation and growth, and creates conditions for the formation of tubercles underwhich MIC occurs. For this reason, MIC occurs often in stratified water, during downtime of opera-tions, and in dead legs.


6.7.6b Effect of temperatureTemperature influences microbial growth in a several ways: low temperatures (typically less than 14�F(�10�C)) may inhibit cellular metabolic processes; high temperatures (typically more than 250�F(120�C)) may thermally denature the protein constituents of the cell causing them to die; microbessurvive between temperatures 14 and 250�F (�10 and 100�C), but individual microbial species survivein narrower temperature ranges. Depending on the temperature range over which the microbes areactive, they can be classified as follows:

• Psychrotroph: Grow below 60�F (w15�C). MIC has been found in extremely cold environments(below 13�F (�25�C)) in pipes in Alaska.

• Mesophile: Grow between 60 and 115�F (15 and 45�C). Mesophile bacteria are the predominantspecies causing corrosion.

• Thermophile: Grow between 115 and 140�F (45 and 60�C). A few thermophilic types of SRBgrow more efficiently at more than 140�F (60�C), and one type is capable of growing at more than210�F (100�C). Shifting operating temperatures within the pipeline or seasonal temperaturevariations may be expected to cause changes in the dominant microbial strains within the biofilm.

• Hyperthermophile: Grow above 140�F (60�C). Hyperthermophilic microbial activity can occur, butgenerally they thrive in specialized habitats where the temperature remains relatively constant orfluctuates in a harmonic manner. The highest temperature at which microbial activity can occur is inpressurized water systems at higher pressures and temperatures than boiling water at sea level.

While microorganisms grow more favorably in their typical temperature ranges, they can also grow inother temperature ranges.

6.7.6c Effect of acid gas partial pressureMicroorganisms are relatively simple forms of life, and can adapt to radical changes in pressure. Ex-periments have proved that microbial species have survived for up to 18 hours at a depth of 12,500 feet(3,800 m) below sea water. MIC is considered as a factor only in sweet systems, but not in sour systems.Studies using molecular biological techniques have identified at least 15 different microorganisms fromsamples collected from a sour gas pipeline. But there is no consensus regarding the overall contributionof MIC in sour systems, though the sour system itself may be created by SRB activity.

6.7.6d Effect of pHMicroorganisms grow over the full spectrum of pH, but individual microorganisms normally adapt tospecific pH ranges. Depending on pH range in which they are active, microorganisms are classified into:

1) extreme acidophiles pH between 1.0 and 4.02) acidophiles pH between 4.0 and 6.03) neutrophiles pH between 6.0 and 9.04) alkalophiles pH between 9.0 and 10.05) extreme alkalophiles pH between 10.0 and 14.0

The extracellular polymeric outer layer buffers the pH within a biofilm, thereby reducing the influenceof bulk water pH to some extent. In general, maximum microbial activity occurs within the biofilmwhen the pH of the bulk is between 4.0 and 9.0. The biofilms are capable of buffering the pH within inthis range in solution within them even when the bulk pH strays out of this range.

6.8 Scaling 351

6.7.6e Langelier saturation index (LSI)The Langelier Saturation Index (LSI) is an indication of the extent of saturation of water with respectto formation of calcium carbonate (see section 6.8.1 for more details on LSI). Scales provide an avenuefor microbes to establish a biofilm, and to cause underdeposit corrosion.

6.7.6f Total suspended solids (TSS)Water often contains suspended solids which settle when the flow rate is low. The settled solids provideenvironments for microbes to thrive. Therefore, if the flow rate is less than 10 feet/s (w3 m/s) and ifTSS is high, the tendency for MIC to occur is high.

6.7.6g Total dissolved solids (TDS)TDS indicates the mineral content of the water. The main dissolved cations in oil field water are sodium,ferrous, potassium, magnesium, and calcium, and the major dissolved anions are chloride, nitrate, bi-carbonate, and sulfate. The TDS values of oil and gas industry water may typically range between 15,000and 150,000 ppm. Higher TDS values indicate greater potential for microbes to survive.

6.7.6h Redox potential (Eh)Redox potential (Eh) is a measure of the potential of a reversible reduction-oxidation electrode(typically platinum) using a stable reference electrode. If the oxidizing or reducing species contacts theelectrode it undergoes oxidation or reduction, causing the potential of the reversible electrode tochange. Generally, an oxidative state (þEh value) will support aerobic microbial activities and areductive state (�Eh) will encourage anaerobic functions. Microbial activities occur extensively whenthe redox potential is between �50 and þ150 mV.

6.7.6i SulfurThe presence of sulfur in solids may be an indication of the presence of SRB. Sulfur may be present assulfide, H2S, mercaptans, and polysulphides.

6.8 Scaling117

The formation of scales may lead to corrosion. Therefore several models, commonly known as indices,are used to predict scale formation. These indices predict whether or not scales will form. Prediction ofscale formation does not necessarily mean that the scales form on metals. For example, in watercontaining colloidal silica or other colloidal matter, scales may form on the suspended colloidalparticles rather than on the metal surface. Further, the formation of scales on metals may lead tolocalized corrosion or decreased corrosion. The effect of scale on corrosion depends on whether thescale forms continuously or not, and whether or not it is protective. Several indices are available topredict scale formation, but only a few of them are discussed in the following paragraphs.

6.8.1 The Langelier saturation index (LSI)The Langelier Saturation Index (LSI) is the most popular index for predicting the formation of calciumcarbonate scale. It predicts conditions for the formation of calcium carbonate, but does not predict thequantity that would actually precipitate. According to this index:

LSI ¼ pH � pHs (Eqn. 6.47)


where pH is bulk pH (see also section 4.12) and pHs is the pH at saturation in calcite or calciumcarbonate. The pHs is calculated using Eqn. 6.48:

pHs ¼ ð9:3þ ALSI þ BLSIÞ � ðCLSIþ DLSIÞ (Eqn. 6.48)

where ALSI is a measure of the total solids effect, BLSI is a measure of the temperature effect, CLSI is ameasure of calcium carbonate content, and DLSI is a measure of alkalinity. ALSI, BLSI, CLSI, and DLSI

are determined using Eqns. 6.49, 6.50, 6.51, and 6.52 respectively.

ALSI ¼ log10½TDS� � 1

10(Eqn. 6.49)

BLSI ¼ �13:12:log10ðTþ 273Þ þ 34:55 (Eqn. 6.50)

CLSI ¼ log10�Ca2þ

� 0:4 (Eqn. 6.51)

DLSI ¼ log10½alkalinity� (Eqn. 6.52)

where TDS is the total dissolved solids, T is temperature in centigrade, and [Ca2þ] and alkalinity aremeasured as calcium carbonate (CaCO3). The TDS may be estimated from the conductivity of solutionand approximately 1 s/cm of conductivity is assumed to be approximately 0.41 mg/L of calciumcarbonate.

Table 6.15 presents further details of scale formation as predicted by LSI.

Table 6.15 Tendency to Form Scale117

Langelier SaturationIndex (LSI) Ryznar Stability Index (RSI) Tendency to Scale

Above 3.0 Above 3.0 Extremely severe

2.0e3.0 3.0e4.0 Very severe

1.0e2.0 4.0e5.0 Severe

1.0e0.5 5.0e5.5 Moderate

0.5e0.2 5.5e5.8 Slight

0.2e0.0 5.8e6.0 Stable water, no tendency to form ordissolve scale

0 e �0.2 6.0e6.5 No scaling, very slight tendency todissolve scale

�0.2 e �0.5 6.5e7.0 No scaling, slight tendency to dissolvescale

�0.5 e �1.0 7.0e8.0 No scaling, moderate tendency todissolve scale

�1.0 e �2.0 8.0e9.0 No scaling, strong tendency to dissolvescale

�2.0 e �3.0 and above 9.0e10.0 and above No scaling, very strong tendency todissolve scale

Table 6.16 Indices to Predict Scale Formation117

Index Characteristics

Puckorius Scaling Index Puckorius Scaling Index quantifies the relationship between saturationstate and scale formation by incorporating the effect of buffering capacityof water

Stiff-Davis Index Stiff-Davis Index modifies the LSI by including the influence of ’commonions’ effect

Oddo-Tomson Index Oddo-Tomson Index modifies the LSI by including the influence ofpressure and partial pressure of CO2

Larson-Skold Index Larson-Skold Index correlates water chemistry and corrosion of mild steel

6.9 High-temperature corrosion 353

.

,

6.8.2 The Ryznar stability index (RSI)Ryznar stability index (RSI) quantifies the relationship between calcium carbonate saturation state andscale formation as:

RSI ¼ 2pHs � pH (Eqn. 6.53)

Table 6.15 presents the tendency to form scale, as predicted by LSI and RSI.

6.8.3 Other indicesSeveral other indices are available. Table 6.16 presents some common indices and their characteristics

6.9 High-temperature corrosion118,119

Prediction of high-temperature corrosion in gas environment depends on predicting the formation andstability of oxides (see section 5.15). The Pilling and Bedworth Ratio (PBR) provides a quick methodto predict high-temperature corrosion. PBR is defined as:

PBR ¼ MWscale:rscalenAwtrmetal

(Eqn. 6.54)

where MWscale is the molecular weight of scale (corrosion product), rscale is the density of scale, n isthe number of atoms in a molecule of oxide (for example for Al2O3, n¼ 2), Awt is the atomic weight ofmetal, rmetal is the density of metal. Table 6.17 lists the PBR of selective metals. The PBR is anindication of relative volumes of metal and its scale.

If the PBR is greater than unity, then the volume of scale is higher than that of the metal from whichit is formed; such a scale covers the entire surface and is protective. For example the PBRs of chro-mium and aluminum are higher than unity and their scales are protective. If the PBR is less than unitythen the volume of scale is less than that of metal from which it is formed; such a scale does not coverthe entire surface and is relatively non-protective. For example the PBRs of magnesium and calciumare less than unity and these scales are non-protective.

Even when PBR is greater than unity, scales may become non-protective above certain tempera-tures and above certain thicknesses. For example, a chromium oxide scale on chromium with PBR 2.0is protective up to 1,100�C; above this temperature the scale spalls off exposing the underlying metal

Table 6.17 Pilling-bedworth Ratio119

Metal Oxide Pilling-Bedworth Ratio

Chromium Cr2O3 2.0

Cobalt CoO 1.9

Titanium TiO2 1.8

Iron FeO 1.7

Copper CuO 1.7

Nickel NiO 1.7

Aluminum Al2O3 1.3

Magnesium MgO 0.8

Calcium CaO 0.6

Lithium Li2O 0.6


surface. The protectiveness of a scale also depends on the adherence of the scale to the substrate.However, the PBR does not consider the influence of adherence.

6.10 Top-of-the-line corrosion (TLC)Two key parameters of practical importance with respect to TLC are the prediction of locations wherewater condenses and the rate at which it does so (see section 5.24). None of the current modelsadequately address these two key parameters. In fact, some models require the water condensation rateas an input. Within these constrains, some models which have been developed to predict TLC arepresented in this section.

6.10.1 The DeWaard model120

DeWaard proposed the first model to predict TLC as:

Ccorr ¼ Fcond:10:

�5:8� 1710

Tkþ 0:67:logðpCO2Þ

�(Eqn. 6.55)

where Ccorr is the corrosion rate (mm/y), Fcond is the correction factor for water condensation and isassumed to be a constant 0.1, T is the temperature (K), and pCO2 is the partial pressure of CO2 (bar).

6.10.2 The Pots model121

Pots developed another model for TLC by considering both iron dissolution and condensation rates.According to the Pots model:

Ccorr ¼ MWFe � 106 � 24� 3600� 365

rcarbonsteel:hFe2þ

i:WCR

rw(Eqn. 6.56)

where Ccorr is the corrosion rate (mm/y), MWFe is the iron molecular weight (55.847 g/mol),rCarbonsteel is the density of carbon steel (7860000 g/m

3), [Fe2þ] is the iron concentration (mol/l), WCRis the water condensation rate (g/m2/s), and rw is the water density (g/m3).

References 355

,’

--

.

f

-

-

6.10.3 The Gunaltum model122,123

Gunaltum developed a model based on the assumption that TLC occurs by continuous formation of awater film on the surface. According to this model, the concentration of ferrous ion in the film as afunction of time is given by:

d�Fe2þ

dt

¼ 1

d�K:Ccorr � ð1� KÞ:PR�WCR:

Fe2þ

�(Eqn. 6.57)

where [Fe2þ] is the concentration of iron ion (mol/m3), t is the time in sec, d is the liquid film thickness(m), K is the covering factor or proportionality constant, Ccorr is the corrosion rate (mol/m3/s), PR isthe precipitation rate (mol/m3/s), and WCR is the water condensation rate (m3/m2/s).

6.10.4 The Nyborg model124

Nyborg developed a model which assumes that TLC is limited by the amount of iron dissolving in thecondensing water. According to the model:

Ccorr ¼ 0:004:WCR:�Fe2þ

�12:5� 0:09� T

�(Eqn. 6.58)

where Ccorr is the corrosion rate, WCR is the water condensation rate, [Fe2þ] is concentration offerrous ion (ppm), and T is the temperature (�C).

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