Think Simulation! Adventures in Electrolytes

OLI Simulation Conference 2012

Corrosion Simulation

October 17, 2012

Scope

• Review of corrosion simulation models

• Progress in modeling general corrosion: Cu-Ni alloys

• Progress in modeling localized corrosion

• Progress in extreme value statistics

• Plans for future development

OLI corrosion

technology

Chemistry / corrosion

thermodynamics

Electrochemistry of metal –

solution interface

Fluid flow

effects

Probabilistic aspects of

propagation

Alloy microstructure

effects

AQ and MSE thermodynamic models

+ stability diagrams

General and localized corrosion

models

Cr / Mo grain boundary depletion

model

Extreme value

statistics

Single-phase flow and integration with multiphase flow

data

Reactive

transport /

propagation of

localized

phenomena

Standalone models

for crevice

corrosion, SCC,

corrosion fatigue

(not in CA)

Corrosion Simulation: Structure

Electrochemical model of general corrosion

• Synthesis of electrochemical phenomena using mixed-potential theory

• Generation of model polarization curves to simulate

• Partial electrochemical processes

Cathodic reactions – reduction of solution species

Anodic reactions - oxidation of metals

Effect of complexation

• Adsorption phenomena

• Passive dissolution and active-passive transition

• Effect of solution species on passive dissolution

• Effect of flow conditions on cathodic and anodic processes

Transport of reactive species to the interface

Transport of corrosion products away from the interface

Behavior of recently added alloys: Corrosion rate of Cu-Ni alloys

• Availability of oxygen controls corrosivity

• Rates are low but flow effects are substantial

• Thermodynamic analysis yields insights into corrosion behavior

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10

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000

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002

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004

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005

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006

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007

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008

0.0

009

m O2

Co

rr. R

ate

(m

m/y

)

Efird and Anderson (1975) 279-302 K, pH 7.8-8.1, quiescent, 5-14

years

Efird and Anderson (1975) 279-302 K, pH 7.8-8.1, f low ing 0.6 m/s,

5-14 years

Efird and Anderson (1975) 279-302 K, pH 7.8-8.1, tidal, 5-14

years

Int. Nickel Co. 298 K, tidal 0.3 m/s

Mansfeld et al. (1994) 298 K, 30-90 days, aerated

Efird (1977) 298 K, pH 8, quiescent, 2 years

Efird (1977) 298 K, pH 8, f low ing 0.5 m/s, 2 years

Todd (1986) 298 K, f low ing 0-0.6 m/s

Todd (1986) 298 K, f low ing 30-40 m/s

Gudas and Hack (1979) 298 K, pH 8, f low ing 1.2-2.4 m/s, 15 days

Syrett and Macdonald (1979) 299 K, f low ing 1.62 m/s

Schleich (2004), static

Todd (1986) 378 K, f low ing 8 ft/s

Calc, 298K, static

Calc, 298K, pipe f low , 2 cm, 0.6 m/s

Calc, 298K, pipe f low , 2 cm, 1.6 m/s

CuNi9010 in seawater

Thermodynamic interpretation of

corrosion behavior of Cu-Ni alloys

Anodic behavior in the active state in a wide range of pH

Passivity is dominated by Cu oxides; Ni does not extend the passivity range in acidic solutions. However, presence of Ni influences the stability of passive film

Hydrogen reduction lies in the immunity zone: oxidants are necessary to cause corrosion

sea water

pH

CuNi9010 in seawater: corrosion potential

• Ecorr depends strongly on oxygen concentration

• As with corrosion rates, flow effects are substantial

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00

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00

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9

m O2

Eco

rr, V

/ S

HE

Efird (1975) 279-302 K, pH 7.8-8.1, stirred

Efird (1975) 294 K, pH 10.1, stirred

Macdonald et al. (1978) 295 K, pH 8-8.4, f low ing

1.62 m/s, deoxygenated

Little and Mansfeld (1991) 298 K, static, aerated, 19

w eeks

Beccaria and Crousier (1989) 298 K, pH 8, unstirred

Efird (1975) 298 K, pH 4.5, stirred

Efird (1975) 298 K, pH 3-8.7, stirred

Effird (1977) 298 K, pH 8, f low ing 0.5 m/s, 2 months

Gudas and Hack (1979) 298 K, pH 8, f low ing 1.2-2.4

m/s, 2 months

Macdonald et al. (1978) 299 K, pH 7.9-8.1, 1.62 m/s

flow

Calc, 298K, static

Calc, 298K, pipe f low , 2 cm, 0.6 m/s

Calc, 298K, pipe f low , 2 cm, 1.6 m/s

Effect of dissolved oxygen: Polarization curve illustrates mechanism

• Oxygen is the dominant cathodic process

• O2 concentration increases corrosion rate and potential

• Effect of oxygen will plateau once passive current density limit is reached

10-6m O2

10-4m O2

static

CuNi9010 in seawater: corrosion rate as a function of flow rate

• Strong effect of flow at low dissolved oxygen

• Corrosion at conditions related to desalination

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0 1 2 3 4 5 6

Flow rate (m/s)

Co

rr. R

ate

(m

m/y

)

Syrett and Wing (1980) 293-302 K, pH 8-8.22,

9-11 days, pipe 1.35cm diameter, 6.6 ppm O2,

9-11 days

Cohen and George (1974) 394 K, natural

treated, 0 ppm O2, 54 months

Cohen and Whitted (1971) 394 K, natural

treated, < 0.005 ppm O2, 697 days

Cohen and Rice (1970) 394 K, natural treated,

0.072 ppm O2, 90-170 days

Cohen and Rice (1970) 394 K, natural treated,

0.072 ppm O2, 365 days, butt w elded

Calc, 298 K, pipe 1.35 cm, 6.6 ppm O2

Calc, 394 K, pipe 1.905 cm, pH=7.4, 0.005 ppm

O2

Calc, 394 K, pipe 1.905 cm, pH=7.4, 0.072 ppm

O2

Effect of velocity: Interpretation using polarization curves

• Anodic current increases with flow velocity due to the Cl-mediated dissolution mechanism

• This increases corrosion rate and reduces corrosion potential

0 m/s

0.1 m/s

6 m/s

Effect of sulfides on CuNi9010

• Thermodynamic aspects

• Formation of sulfides at potentials much lower than Me/Me2+ potentials

• This has a profound effect on anodic dissolution

10-4 m H2S

CuNi7030 and CuNi9010 in seawater with sulfides

• Strong decrease of corrosion potential as a function of sulfides

• Data are scattered because multiple steady states are possible in the transition region

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1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02

m S(-2)

V / S

HE

Syrett and Wing (1980) 293-297 K,

pH 8-8.22, 230 h, pipes, 1.35cm

diameter, 3-5 m/s, deaerated Macdonald et al. (1978) 295 K, pH 8-

8.4, flowing 1.62 m/s, 160-235 h,

deoxygenatedEiselstein et al. (1983) 296 K, pH

7.8-8.3, tubes 3 m/s, aerated, 16

daysEiselstein et al. (1983) 296 K, pH

7.8-8.3, tubes 3 m/s, 0.1-0.3 ppm

O2, 4 daysEiselstein et al. (1983) 296 K, pH

7.8-8.3, tubes 3 m/s, deaerated, 4

daysGudas and Hack (1979) 298 K, pH 8,

flowing 1.2-2.4 m/s, aerated, 1-60

days daysSyrett et al. (1979) 298 K, natural

seawater, 0.045-3.3 ppm O2,

aeratedCalc, pipe 1.35cm, 3 m/s, 0.05ppm

O2

Calc, pipe 1.35cm, 1.62 m/s,

0.05ppm O2

Calc, static, 0.2ppm O2

Calc, static, aerated

CuNi9010

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1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01

V / S

HE

m S(-2)

Alhajji and Reda (1993b) 293 K,aerated, quiescent

Alhajji and Reda (1993b) 293 K,aerated, stirred

Alhajji and Reda (1993a) 293 K,aerated, quiescent

Alhajji and Reda (1993a) 293 K,aerated, stirred

Alhajji and Reda (1995) 293 K,deaerated, quiescent

Reda and Alhajji (1993) 293 K,aerated, quiescent

Alhajji and Reda (1994) 293 K,aerated, jet impingement

298 K, seawater, aerated, static

298 K, seawater, aerated, RDE1000 rpm

298 K, seawater, 0.1 ppm O2,static

CuNi7030

Effect of ammonia: CuNi7030 and CuNi9010

• Complexation of Cu with NH3 leads to enhanced anodic dissolution

• Role of dissolved O2 is important

• CuNi7030 is more resistant to ammonia corrosion

• Higher Ni content mitigates dissolution

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7 8 9 10 11 12

Co

rr.

Ra

te (

mm

/y)

pH

Caruso and Michels (1981) 294 K,0.12 m NH3, spray test, air

Polan et al. (1981) 298 K, 0-0.0094 mNH3, aerated, 8-12 ppm O2

Polan et al. (1981) 298 K, 0-0.0094 mNH3, deaerated, 0.1-0.2 ppm O2

Sheldon and Polan (1985) 298 K, 0-0.0012 m NH3, lab. data, deaerated

Caruso and Michels (1981) 303 K,0.059 m NH3, fog test, air

Todd (2005), 500 ppm NH3, 1400ppm NH4CO3

298K, NH3, 10 ppm O2, static

298K, NH3, 0.2 ppm O2, static

298 K, 1400ppm NH42CO3+NH3, air,static

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7 8 9 10 11 12

Co

rr. R

ate

(m

m/y

)

pH

Caruso and Michels (1981) 294 K, 0.12

m NH3, spray test, air

Polan et al. (1981) 298 K, 0-0.0094 m

NH3, aerated, 8-12 ppm O2

Polan et al. (1981) 298 K, 0-0.0094 m

NH3, deaerated, 0.1-0.2 ppm O2

Sheldon and Polan (1985) 298 K, 0-

0.0012 m NH3, lab. data, deaerated

Caruso and Michels (1981) 303 K, 0.059

m NH3, fog test, air

Todd (2005), 500 ppm NH3, 1400 ppm

NH4CO3

Calc, 298K, NH3, 10 ppm O2, static

Calc, 298K, NH3, 0.2 ppm O2, static

Calc, 298 K, 1400ppm NH42CO3+NH3,

air, static

CuNi9010

CuNi7030

Assessment of corrosion resistance

Example: Alloy 2205 in H2SO4

Isocorrosion Curve (0.1 mm/y)

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40.0

60.0

80.0

100.0

0 1 2 3 4 5 6 7

H2SO4, m

T,

0C

Hummel (1982)

Nicolio andCourtis (2002)

Calculations

General Corrosion

No General Corrosion

Prediction of localized corrosion

• Criterion: Corrosion potential vs. repassivation potential

• Repassivation potential model

• Interfaces: Metal – metal halide – occluded solution

• Formation of metal oxide in the limit of repassivation

• Competitive adsorption at the interface

• Aggressive ions promoting metal dissolution

• Inhibitive ions promoting oxide formation

Chloride

Pote

ntial

Erp

Ecorr

Localized corrosion

Previous work: Generalized correlation for predicting Erp of Fe-Ni-Cr-Mo-W-N alloys

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aCl

Erp

(SH

E)

22, exp

22, generalized

276, exp

276, generalized

625, exp

625, generalized

825, exp

825, generalized

690, generalized

600, exp

600, generalized

800, generalized

254SMO, exp

254SMO, generalized

AL6XN, exp

AL6XN, generalized

2205, generalized

316L, exp

316L, generalized

304L, generalized

s-13Cr, exp

s-13Cr, generalized

Example: T = 368 K

• Reproduces Erp for 15 metals (13 stainless steels and nickel-base alloys, Ni, and Fe)

• Predictions have been verified from 296 K to 423 K

Alloy 2507 in chloride solutions at 85 C: Blind test

• The generalized correlation predicts Erp that is very close to the most recent experimental data

• Further improvement is obtained by a slight adjustment of the Gibbs energy of activation for metal dissolution mediated by adsorption of Cl- ions

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E rp, S

HE

m Cl-

No H2S, Sept 2012

No H2S, Feb 2012

Calc - 2507 (correlation)

Calc - 2507 (one

parameter adjusted)

no localized corrosion observed - pointsignored

Localized corrosion: Current work

• Localized corrosion in Cl- - H2S environments

• Stress corrosion cracking: Initiation above Erp

• Extension of the model to include H2S effects

• Experimental program at DNV

Generalization to multiphase flow

• Electrochemical reactions depend on the concentrations of species near the surface

• Mass transfer of species to and from the interface depends on flow conditions

• Numerical characterization through mass transfer coefficient km

• Models for calculating km for single-phase flow have been available in the Corrosion Analyzer

• In multiphase flow, there is a great variability of flow patterns and a generalized approach is necessary

Generalization to multiphase flow: Shear stress

• Shear stress yields mass transfer coefficient km:

• Alternative ways of calculating the shear stress

• From fluid flow software

Preferred approach because it can account in detail for various flow patterns

Integration with OLGA

• From an approximate correlation for water – oil – gas flow

3/2

00608.0

Dkm

shear stress

Electrochemical models for general and localized corrosion: Parameterization

• Metals

• Carbon steel

• Stainless steels: 13Cr, 304, 316, 254SMO

• Nickel-base alloys: 22, 276, 625, 825, 600, 690, and Ni

• Duplex alloy: 2205

• Copper-nickel alloys: Cu, CuNi9010, CuNi7030

• Aluminum

Progress in Extreme Value Statistics

• Objective: Predict the propagation of localized corrosion as a function of time on the basis of short-term data

From current EVS Analyzer

Progress in Extreme Value Statistics

• New developments

• Improved statistics: Calculating the upper bound for localized corrosion

• Prediction of the number of perforations, their area and leak rate

• Extension to r-largest order statistics

Time, years

0 2 4 6 8 10 12 14

Num

be

r of ho

les p

er

sq. ft.

0

50

100

150

200

250

300

1/2 in.

1/4 in.

Predicting the number of penetrations as a function of time for varying wall thickness

Plans for Future Development

• Short and medium-term objectives

• Implementation of improvements to Extreme Value Statistics in Corrosion Analyzer

• Corrosion-resistant alloys in oil and gas environments (in collaboration with DNV)

• Long-term objective

• Mixed-solvent electrolyte electrochemical model

Opening new chemistries and providing improved predictions by taking advantage of the MSE thermodynamic model