Paper No.

11

CORROSIONOLThe NACE International Annual Conference and Exposition

PREDICTION OF CORROSIVITY OF C02/H2SPRODUCTION ENVIRONMENTS

Sridhar SrinivasanCLI International, Inc.

14503 Bammel N. Houston # 300Houston, Texas 77014, USA.

Russell D. KaneCLI International, Inc.

14503 Bammel N. Houston # 300Houston, Texas 77014, USA.

ABSTRACT

One of the most fundamental issues in current day corrosion research is assessment of corrosion ra~,m in

steels and determination of corrosivity of typical operating environments in oil and gas production. Suchan assessment requires an understanding of the role of primary environmental and metallurgical variablesand underlying mechanisms of corrosion. This paper presents a novel hierarchical approach to assess

system corrosivity and prediction of corrosion rates in carbon steels in production environmentscontaining COZ and/or HZS. Critical environmental parameters that influence system corrosivity are

identifixl and the effects of these parameters on corrosion are examined. Modeling for synergisticassessment of system corrosivity as a function of relevant operating parameters is presented and isaccompanied by a description of a computer program to capture the model so described.

Keywords: COZ HzS,

COz/H2S corrosion in

Corrosivity, prediction, corrosion rates, computer model

INTRODUCTION

oil and gas production environments represents one of the most important areas ofcorrosion research. It is so because of the criticality of the need to assess corrosive severity as a means toensure safe utilization of steels, which have wide application in just about every sphere of oiJ and gasproduction and refining. Even though C02/HDS corrosion and concomitant mechanisms have been areasof significant work over the last thirty years, there still exists a need to accurately predict corrosivity ofCOJH2S environments from a stand point of defiiing limits of use for carbon steels. Even thoughnumerous predictive models have been developed and are being developedl’2 , most of the available

Copyright@X 996 by NACE International. Requests for permission to publish this manuscript in any form, in part or in whole must be made in writing to NACEInternational, Conferences Division, P.O. Box 218340, Houston, Texas 77218-8340. The material presented and the views expressed in thispaper are solely those of the author(s) and are not necessarily endorsed by the Association. Printed in the U.S.A.

predictive models tend to be either very conservative in their interpretation of results or focus on anarrow range of parametric effects, thereby limiting the scope of the model’s application in realisticassessment of corrosivity and corrosion rates. Often times, data required by the models are often not

easily accessible or available to the operators who need to employ the model, thereby limiting theapplicability of the models to situations of reduced practical importance4’5. In this context, the issue ofcorrosivity assessment for carbon steels can be re-stated in terms of the following critical requirements:

. Develop a predictive model that utilizes commonly available operational parameters

. Utilize existing lab/field data and theoretical models to obtain realistic assessments of corrosivity andcorrosion rates

. Develop a computational approach that integrates both numerical (read “lab trends”) and heuristic(field data and experience) information and knowledge about corrosivity prediction.

In this paper, a methodology to determine system corrosivity and predict corrosion rates in steels isdescribecl, consistent with objectives stated above. The method adopted here attempts to capture bo~h

the effect of critical parameters on corrosion rates as well as that of parameter interactions. The model

described in this paper has been encoded into a WindowsTM-based computer program, PREJ~ICTTM, that

would allow the end user to predict corrosion rates.

The primary variables in corrosivity prediction in the model are the acid gases COZ and HZS thatcontribute 10 the typically acidic pH found in production environments. The model uses the widelyaccepted de Ward - Milliams2 relationship for COZ corrosion for an initial determination of C02-basedcorrosion rates. However, the effective C02 partial pressure in the system is not based on the operatingpartial pressure but one obtained from the system pH. This rate is further refined to account for thepresence of HZS, corrosion products, temperature effects etc. A technical description of differentcorrosivity modeling parameters and their effects is given in ensuing sections of this paper. Theunderlying idea here has been to develop a prediction model that accurately represents the state-of-the-artin theoretical analyses as well as parametric correlations based on lab and field data. The model has alsobeen compared with actual field conditions in an effort to compare system predictions with fieldobservations.

In developing any corrosivity model, it is important to recognize the role of superposition of differentparameters. Such a recognition requires a clear understanding of independent parameter effects but alsoon how corrosion rate progresses when subjected to the effects of two or more variables. While thecurrent prediction model is primarily concerned with environmental constituents and their effects ofcorrosion, it is also important to recognize the signflcant role of metallurgy in fashioning appropriatecorrosion behavior. Influence of compositional and alloying elements has been chronicled but hashitherto not been rigorously studied in assessing resistance to system corrosivity and a brief discussion ofmetallurgical factors in corrosivity determination is provided elsewhere in this paper.

The rest of the paper is organized as follows: A brief overview of C02-based corrosion mechanism and

the de Waard - Milliams model is presented. A review of corrosivity modeling efforts available inliterature is followed by a description of this model for corrosivity determination and the effects ofdifferent relevant parameters in predicting corrosion rates. The computer implementation of the mocleland the software interface are also discussed. This is followed by directions for future work andconclusions.

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COJH2S-BASED CORROSION: TECHNICAL BACKGROUND AND LITERATURE REVIEW

C02-based corrosion has been one of the most active areas of research, with several predictive models forcarbon steel corrosion assessment. These efforts range from a predictive model that begins with C02corrosion 2’sto models that focus on specific aspects of the corrosion phenomena (such as flow-inducedcorrosion or erosion corrosion)4’5 to models that empirically relate corrosion rates to gas production andwater production rates7. Crolet et al.s use the physical chemistry of the corrosive medium as the keynotion and take into account ionic strength, pH and specific ionic species as relevant factors. Otherrelevant efforts include those by Ikeda et al. 9 that look at the influence of H2S and 02 on C02-basedcorrosion as well as those by Adams et al.10. Many of these efforts suffer from significant drawbacks in

that,

● they focus on a narrow range of parametric effects, for e.g., there is relatively little publishedinformation on the effects of HJ3 in production systems and on how su~lde sca~g can affect the Cozcorrosion process

. Some models focus just on one component of corrosivity, such as erosional effects, wall shear stresseffects or flow effects and have opted to ignore effects of chemical species (factors such as pH, HZS,C02 etc.)

. Other models totally rely on lab data for predictive modeling, with the consequence that thesimplifying assumptions made in developing laboratory models often lead to results that can be Pmremoved from what is observed in the field.

The current model attempts to integrate lab data and field experience within the framework of relevantcontrolling parameters that are most prominent in oil and gas production. It is important to realize thatwhile arc ane theoretical models are interesting from an academic stand point, the controlling parametersin a modlel must also represent data easily available to oil and gas production personnel. The currentmodel attempts to integrate principles hitherto delineated in developing the predictive model.

While there have been several studies focusing on the exact mechanism of metal dissolution in COZcontaining waters, the efforts of De Ward and Milliams and others2’~’9present a commonly acceptedrepresentation wherein anodic dissolution of iron is a pH dependent mechanism as given by Bockris2, thecathodic process is driven by the direct reduction of undissociated carbonic acid. These reactions can berepresemed as3,

++Fe .---------> Fe + 2e-

H2COS + e-----> HC03- + H

(Anodic reaction)

(Cathodic reaction)

The overall corrosion reaction can be represented as,

Fe + 2H2COS ---> Fe++ + 2 HCOS- + Hz

The build up of the bicarbonate ion can lead to an increase in the pH of the solution till conditionspromoting precipitation of iron carbonate are reached, leading to reaction given below:

Fe + 2HCOS- ---> FeCO~+ H20+COZ

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Iron carbonate volubility, which decreases with increasing temperature, and the consequent precipitationof iron carbonate is a signitlcant factor in assessing corrosivity. The charge transfer controlled reaction

involving carbonic acid and carbon steel (or Fe) can be represented in terms of the concentration orpartial pressure of dissolved COZ in the medium to arrive at a corrosion rate equation that incorporatesthe order of the reaction and an exponential function that approximates for Henry’s reaction constant’stemperature dependence. This corrosion rate equation is given as2,

log (vCO,)= 5.8-17 10/T+ 0.67 log (pC02) ------ (1)

whereV.O,= corrosion rate in mm/yrT = operating temperature in “KpCOz = partial pressure of COZ in bar

The corrosion rate obtained by equation (1) has typically been often seen as the maximum pomiblecorrosion rate without accounting for iron carbonate scaling. A nomogram representing eq. ( 1) is givenin Figure 1‘, which also includes a scale factor to account for the formation of protective carbonate ftilsthat lead to a reduced corrosion rate at higher temperatures.

The above correlation describes COz-based corrosion. There have been other significant efforts todemonstrate the effects of other environmental variables such as pH, HzS, chlorides, bicarbonates,water/gas/oil ratios, velocity etc. Effects of HzS on corrosion rates in the laboratory have been studiedand presented by Videml ] et al., and Ikeda9 et al. Ikeda’s work indicates that the preferential formation

of an Iron suKlde fti can decelerate the corrosion rate, especially at temperatures above 20 ‘C and

extending up to 60 “C. Above 150 ‘C, the corrosion reaction falls back to the standard C02-based

corrosion with an FeCOs film that is more stable than the FeS film. Videm’s work supports the theorythat even small amounts of HZS can provide instantaneous protection at temperatures in the range 70-80”

‘c.

Lotz et al. 12have chronicled the role of the hydrocarbon condensate in providing corrosion mitigation inspecific production systems. The role of the type of oil or gas condensate is important from the standpoint of accurate assessment as reported by Choi et ails’14. Other studies evaluating effects of criticalparameters such as pH and velocity on COZ corrosion include those by Dugstad’5 as well as Lotz’b.Other predictive models also include those by Gunatlun17 and Bonis et a118wherein a combination of theparameters discussed herein along with electro-chernical considerations have been utilized to arrive at thecorrosion rate

The primury objective of the corrosivity prediction model described in this paper is to address the needof developing a predictive method that would synthesize different parametric relationships based oninformation from literature, lab research/data and practical experience/expertise. It has often beenobservecl that lab data and the ensuing models represent poor and often inadequate simulation of fieldconditions]y. It is also necessary to understand that field data is typically sparse and can be negated byother production data. The need to integrate field data/experience and laborato~ models stems from

the fact that the lab data can provide significant pointers and trends that can be used in conjunctionwith field data and experience. The idea is to develop a methodology that can integrate analytical andheuristic models. To this end, this predictive system mirrors other successful development effortsundertaken by the authors in the areas of evaluation of CRAS and cracking in steels20’21. The central

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theme is to develop a computer program that can bring together different types of modeling knowledgeto provide a realistic solution to the significant question of predicting corrosion rates in typicalproducticm environments.

CORROSIVITY PREDICTION MODEL DESCRIPTION

A flow chart delineating the hierarchical reasoning structure of the predictive model is given in Figure 2.The first step in corrosivity determination is computation of the system pH, since it is the hydrogen ionconcentriltion that drives the anodic dissolution. Further, the role of pH in promoting or mitigating COz-based corrosion has been extensively chronicled22’19. For production environments, where it is the

dissolved COZ or H*S that contribute signflcantly to a suppressed pH, the pH can be determined as afunction of acid gas partial pressures, bicarbonates and temperature, as shown in Figure 323. From apractical stand point, the contribution of HzS or HCO~ or temperature to pH determination is anotherway of representing effective levels of COZ that would have produced a given level of pH.

This type of pH determination has been found to be quite accurately applicable in other modeling effortsinvolving vertilcation of the relationship given in ref. 23. While it has been documented that the C02

corrosion mechanism is dissimilar to that of strong acids like HC1 (where as CO~ corrosion is nowunderstood to progress through direct reduction of HzCOq to HCOS- rather than reduction of H+ ions),and that carbonic acid corrosion is much more corrosive than that obtained from a strong acid su,;h asHC1 at the same pH1g, there is also significant agreement that lower pH levels obtained from higher acidgas presence leads to higher corrosion rates. Conversely, higher levels of pH obtained through bufferingin simulated production formation water solutions have been shown to produce sign~lcantly lowercorrosion rates even at higher levels of CO* and/or HzS24. Data about the effects of pH from anotherstudy is shown in Figure 415. Hence, it is more meaningful to determine the effective COZ partial pressurefrom the system pH. Data in Figure 3 can be represented as equations for straight lines in terms of pHand acid gas partial pressures for a given level of HCO~ and temperature. A numerical computer modelhas also been developed to compute pH for different values of HCOS and temperature25. Consequently,pH determination can be represented as,

pHl = C 1- log (pH2S + pCOz) (tempemture = 20 “C) ------------------ (2)11”HC03 >0,

pH2 = C2 - log (pH2S + pC02) + log (HC03) (temperature =20 “C)--(3)

where C 1 and C2 are constants, pHzS and pCOz are partial pressures in bars and HCOS conccntrati ~n isrepresented in mcq/1 (61 mg/1).

The system pH is given by the larger number between pH 1 and pH2. Correspondingly, if the temperature

is higher than 100 “C, there is a slight reduction in the hydrogen ion concentration as shown in Figure 3,but the change in pH can be accounted for by a change in the value of the constants in equations 2 and 3above. Once the system pH is determined, the effective COZ partial pressure can be determined from (2)as.

Log(pCOZ..~) = (Cl - pH) / 2 ---------------- (4)

where pCOz.eff is the effective partial pressure of COZ in a production system that can produce theprevalent level of hydrogen ion concentration.

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The effective COZ partial pressure from (4) can be used in eqn. (1) to determine an initial corrosion ratefor C02-based corrosion. The corrosion rate so obtained is modified to account for the formation of aFeCOS film (Fe~Od at higher temperatures) Whose Stabfity varies as a function Of the operat~g

tcrnpcrature. The scale correction factor shown in Figure 1 is used to determine the initial corrosion ratefrom the nomogram in Figure 12. It is generally estimated that this corrosion rate presents a maximumcorrosion rate even though it has been reported that the rate computed by the nomogram are reached orexceeded in systems with high flow rates. It is important to recognize that this corrosion rate has to be

modilled to account for the effect of other critical variables in the system. Further, this rate does notindicate modality (general or localized) but rather, represents the maximum rate of attack.

As mentioned earlier, it is necessary to superposition the effects of other critical system parameters. Theflow chart in Figure 2 provides the lists sequential effects that are important from a stand point ofcorrosivity determination. In addition to the system pEl, these include,

●

●

●

●

●

●

●

●

●

●

HzS partial pressure

Maximum operating temperature

Dissolved chlorides

Gas to oil ratiowater to gas ratio/water cut

Oil type and its persistence

elemental sulfur/aerationfluid velocityType of flow

Inhibition type and efficiency

The following sections discuss the effects of these parameters on corrosivity and provide information asto how it is critical to examine the parameter interactions prior to capturing the synergistic effects ofthese parameters on corrosion.

Role of 132S

Oilfkld production environments, in recent years, have been characterized by increasing presence of H:lSand related corrosion considerations. Even though H2S is probably the most signtilcant concern incurrent day corrosion and cracking evaluation, the role of HzS in corrosion in steels has received muchless attention when compared to the widely studied COZ corrosion.2G However, H2S related corrosionand cracking has remained one of the biggest concerns for operators involved in production because ofthe significance of HZS related damage.27.

The current modeling effort, in addition to its contribution in pH reduction, HzS has a three fold role:

(1) At very low levels of HzS (< 0.01 psia), CO~ is the dominant corrosive species, and at temperatures

above 60 ‘C, corrosion and any passivity is a function of FeCO~ formation related phenomenon andthe presence of HzS has no realistic significance.

(2) In C~OZdominated systems26’2g, presence of even small amounts of H2S (ratio of pC02/pH2S > ‘200),”

can lead to the formation of an iron sultlde scale called mackinawite at temperatures below 120 ‘C.However, this particular form of scaling, which is produced on the metal surFace directly as a functionof a reaction between Few and S- is influenced by pH and temperature27. This surface reaction can

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lead to the formation of a thin surface fti that can mitigate corrosion. The authors are cur)entlypursuing laboratory studies to characterize the stability and formation of mackinawite in sour systems.

(3) In H2S dominated systems (ratio of pCOZ/pHzS < 200), there is a preferential formation of a meta-stable sulildc fdm in preference to the FeCO~ scale; hence, there is protection avaifiable due to thepresence of the sulfide fti in the range of temperatures 60 to 240 “C. Here, initially it is themackinawite form of H2S that is formed as a surface adsorption phenomenon. At higher

concentrations and temperatures, mackinawite becomes the more stable pyrhotite. However, at

temperatures below 60 “C or above 240 “C, presence of HzS exacerbates corrosion in steels since thepresence of H2S prevents the formation of a stable FeCOs scale.g’29 Further, it has been observed that

FcS i-h itself becomes unstable and porous and does not provide protection. Also, the scale factorapplicable for COZ corrosion with no HzS (shown in Figure 1) becomes inapplicable. Even thoughthere is agreement amongst different workers that there is a beneficial effect of adding small amountsof HzS at about 60 “C, Ikeda et al.9 and Videm et all] present divergent results at higher

concentrations and higher temperatures.

The effect of HzS adopted in the predictive model reflects work published by T. Murata et al.29 for COZdominated systems. Figure 529 shows the combined effects of temperature and gas composition oncorrosion rate of carbon steels. Figure 69 shows the effect of varying degrees of HzS contamination onCOZ corrosion. It is to bc noted that the role of HzS in COZ corrosion is a complex issue governed byfdm stability of FeS and FeCOS at varying temperatures and is an area further active research by theauthors.

Temperature Effects

Temperature has a significant impact on corrosivity in COz/HzS systems. Corrosion rate as a function ofdifferent levels of COZ and temperature are given in Figure 72. It has to be noted that once the corrosionproducts are formed, there is a significant mitigation in corrosivity. It is also apparent that the carbonatefti is more stable at higher temperatures and affords greater protection at higher temperatures. Figure 7

also shows that at temperatures beyond 120 “C, corrosion rate is almost independent of the COZ partialpressure of the system. The carbonate fti may, however, be weakened by high chloride concentrationsor can be broken by high velocity. In HzS dominated systems, because of the fact that no carbonate scale

may be formed and that the FeS fti becomes porous and unstable at temperatures beyond 120 “C,significant localized corrosion may be observed.

Chlorides

Produced water from hydrocarbon formations typically contains varying amounts of chloride saltsdissolved in solution. The chloride concentration in this water can vary considerably, from zero to fewppm for condensed water to saturation in formation waters having high total dissolved salts/solids (TDS).In naturally deaerated production environments, corrosion rate increases with increasing chloride ioncontent over the range 10,000 ppm to 100,000 ppm30. The magnitude of this effect increases withincreasing temperature over 60 “C (150 “F). This combined effect results from the fact that chloride ionsin solution can be incorporated into and penetrate surface corrosion films which can lead todestabilization of the corrosion fti and lead to increased corrosion. This phenomenon of penetration ofsurface corrosion films increases in occurrence with both chloride ion concentration and temperature.

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Bicarbonates

Bicarbonates in the operating environment have a signflcant impact on corrosion rates. On one hand,high levels of bicarbonates can provide higher pH numbers leading to corrosion mitigation even when thepartial pressures of COZ and HzS are fairly high. There is a natural inhibitive effect of presence of

bicarbonates which can be present in substantial quantities in formation waters (up to 20 mcq/1)31.Condensed water in production smeams typically contains no bicarbonates.

Velocity

Next to the corrosive species that instigate corrosion, velocity is probably the most significant parameterin determining corrosivity of production systems. Fluid flow velocities affect both the composition andextent of’ corrosion product ftis. Typically, high velocities (> 4 rds for non-inhibited systems) in theproduction stream leads to mechanical removal of corrosion fhs and the ensuing exposure of the freshmetal surface to the corrosive medium leads to significantly higher corrosion rates. Corrosion rate as afunction of flow velocity and temperature is shown in Figure 815.

In multiphase (i.e. gas, water, liquid hydrocarbon) production, the flow rate influences the corrosion rateof steel in two ways. First, it determines the flow behavior and flow regime. In general terms, this ismanifested as static conditions (i.e. little or no flow) at low velocities, stratified flow at intermediateconditions and turbulent flow at higher flow rates. One measure which can be used to def-ine the flowconditions is the superficial gas velocity. In liquid (oil / water) systems, this is replaced with the liquidvelocity.

Velocities less than 1 m/s are considered static. Under these conditions corrosion rates can be higherthan those observed under moderately flowing conditions. This occurs because under static conditions,there is no natural turbulence to assist the mixing and dispersion of protective liquid hydrocarbons orinhibitor species in the aqueous phase. Additionally, corrosion products and other deposits can settle outof the liquid phase to promote crevice attack and underdeposit corrosion.

Between 1 and 3 rdsec, stratified conditions generally still exist. However, the increased flow promotesa sweeping away of some deposits and increasing agitation and mixing. At 5 n-dsec, corrosion rates innon-inhibited applications start to increase rapidly with increasing velocity .31 Data shown in Figui-e 931demonstrates the effects of velocity on corrosion rate for both inhibited and non-inhibited systems. Forinhibited applications, corrosion rates of steel increase only slightly between 3 to 10 rrdsec, resulting frommixing of the hydrocarbon and aqueous phases. Above about 10 rn/see, corrosion rates in inhibitedsystems start to increase due to the removal of protective surface films by the high velocity flow.

Flow related effects on corrosivity have been linked to the wall shear stress developed and is an area ofintense research in the community32. Flow induced corrosion is a direct consequence of mass andmomentum transfer effects in a dynamic flow system where the interplay of inertial and viscous forces isresponsible for accelerating or decelerating metal loss at the fluid/metal interface. While flow-inducedcorrosion is a signf~cant component of predictive modeling discussed herein, the topic of flow-relatedeffects is being actively researched by the authors and forms the focus of another publication. Anotherrelevant aspect of flow or velocity induced corrosion is erosion corrosion3s and refers to the mechanicalremoval of corrosion product ftis through momentum effects or through impingement and abrasion.Guidelines for velocity limits with respect to erosional considerations are given in API- 14E in terms ofthe dens:{ty of the fluid medium.34

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Importance of Water/Gas/Oil ratios

The predictive model class~les systems as oil dominated or gas dominated on the basis of the gas/oil ratio(GOR) of the production environment. If the environment has a GOR <890 m3/m3 (5000 sctibbl inEnglish units)~s, the tendency for corrosion and environmental cracking is often substantially reduced.This is caused by the possible inhibiting effect of the oil fii on the metal surface, which effec~ivclyreduces the corrosivity of the environment. However, the inhibiting effect is dependent on the oil phase

being persistent and acting as a barrier between the metal and the corrosive environment. The persistenceof the oil phase is a strong factor in providing protection, even in systems with high water cuts. In oilsystems with a persistent oil phase and up to 45 percent water cut, corrosion is fully suppressed,irrespectl ve of the type of hydro-carbon’2. Relative nettability of the oil phase versus the water phase has

a significant effect on corrosionsG. Metal surfaces that are oil wet show significantly lower corrosionmtes17.

The predictive model described in this paper provides for a signitlcant reduction in the corrosion rate (upto a fiactor of 4) based on the type of oil phase being persistent, mi~dly persistent and not persistent.However, the degree of protection 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 important to understand the type of crude oil in terms of the organic

compounds that make up the crude to determine nettability effects. Figure 10 shows data that relates theacid number of the crude to oil nettability and Figure 11 shows corrosion rate as a function of producedwater content for different crude oil/produced water mixtures3G. While the effect of persistence of the oilmedium is signtilcant on corrosion rates, it is even more difficult to quantify precise compositional

elements of an oil medium that contribute to nettability and persistent oil film formation. Suchquantflcation is possible by rigorous laboratory testing of different actual, uncontaminated (readdeaerated) production water samples, so as to determine the extent of protection.

In oil systems the water cut acts in synergy with the oil phase to determine the level of protection fromthe hydrocarbon phase. However, at very low water cuts (less than 5 percent), corrosive severity of theenvironment is lessened due to the absence of an adequate aqueous medium required to promote thecorrosion reaction.

In gas dommated systems, there are two measures to evaluate availability of the aqueous medium. If theoperating temperature is higher than the dew point of the environment, no condensation is going to bcpossible and will lead to highIy reduced corrosion rates. Corrosion under condensing conditions (i.e.,

operating temperature less than the dew point) is a function of the rate of condensation and transpc rt ofcorrosion products from the metal surface.38 If the total water in a condensing system as measured by the

Water to Gas Ratio is less than 11.3m3/Mms (2 BBL water/MSCF gas), corrosivity is substantiallyreduced.

Aeration/Sulfur

Presence. of oxygen signflcantly alters the corrosivity of the environment in production systems.Oldfield~g has chronicled how presence of oxygen can signdlcantly increase corrosion rates due toaccelemtion of anodic oxidation. While corrosion rate increases with oxygen, rate of oxygen reduction asa cathodic reaction is further exacerbated by:

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1. Increase in operating temperature2. Increased fluid flow leading to increased mass flow of oxygen to the metal surface3. Increasing oxygen concentration

Data showing increases in corrosion rate as a function of oxygen concentration for differing temperaturesis shown in Figure 12s9. Corrosion rates for different flow velocities and oxygen levels as a function oftemperature is shown in Figure 1339. Presence of elemental sulfur is similar to that of free oxygen sinceelemental sulfur also acts as a strong oxidizing agent.

Inhibiticmhhibition Effectiveness

Appropriate inhibition is a critical criterion for effective use of carbon steels in corrosive productionsystems. Inhibition has been typically found to be viable in flows with velocity in the range 0.3- 10 rids.Requirements for the type of inhibitor and the method of delivery depend on the type of system(product ion tubing or horizontal flow lines) to be inhibited. Inhibition Efllciency (IE) describes the

efficacy of an inhibitor treatment in mitigating weight loss corrosion and is an important factor inassessing corrosivity. It is based on either laboratory or field data where inhibited and non-inhibited

corrosion rates are compared using the following equation:

IE = 1OO[(CR. - CR,)/CR.]

whereCRn = non-inhibited corrosion rate,CRi = inhibited corrosion rate.

Values of IE near 1.0 represent conditions with maximum efficacy of the inhibitor treatment. Conditionswhich affect IE include:

1. Inhibitor concentration.2. Severity of corrosive environment.3. Service temperature.4. Volubility of inhibitor in aqueous phase.5. Phase behavior of inhibitor and carrier fluid in service environment.6. Persistence of inhibitor on metal surface.

The predictive model evaluates inhibition efficacy on the basis of velocity, hydrocarbons to water ratioand dissolved chloride levels. The method of delivery (batch, continuous, pigging etc.) is also animportant Factor in determining appropriateness of inhibition for a given set of operating conditions.

The corrosion rate predicted in the current model can be represented in terms of three broad rules thatguide the computer model’s decision making:

1. Effect of fundamental system variables such as COZ, HvS, pH, temperature, and velocity on corrosionrate.

2. Effect of parameter interactions on corrosivity, such as, influence of temperature on the carbonate orsulfide fti stability. Or flow effects on corrosion products and the ensuing loss of protective ftis asa function of velocity, temperature, acid gases and pH.

3. Effeets of system modfiers such as oil film persistence (or lack of it) or the crude type, water cut,dew point, aeration and inhibition.

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Corrosion rate, thus predicted, incorporates the synergy of the effects of all the critical system variablesand provides a more realistic estimation of corrosivity than what would be available with conservativetheoretical models that focus on a limited number of parameters. The significance of the reasoni(]g inpredictive model stems from the fact that the decisions made synthesize different types of corrosionknowledge:

. Theoretical models that provide effects of different parameters

. Data from laboratory tests that provide insight on parametric correlations and trends about parametriceffec~~

. Experience-based heuristics that facilitate proper interpretation of data form lab and field

System Overview

The predictive model in this paper has been implemented as a Windows-based computer program with aninterface as shown in Figure 14. Based on data spec~led for different parameters, the system willinstantaneously display the following results:

● System pH

● Predicted Corrosion Rate called Corrosion Index (in mpy or mmpy)

. A textual recommendation in the results box indicating whether the predicted corrosion rate is withinthe specified allowance for the particular system.

. A corrosion index bar that graphically represents the corrosion rate.

The user can specify data for any of the parameters and watch the effect of that parameter on thecorrosion rate in the system instantaneously. The system starts with a set of default values and calculatesa corrosion rate based on any changes to the displayed values. A typical consultation will involve thefollowing five steps:

1. Specification of pH related data: At the outset, the system determines a corrosion rate only if theoperating environment is acidic or has aeration. If the specified environment has no acid gases orthere is sufficient buffering to produce a pH higher than 7.0, the system will predict zero or very 10wcorrosion rates, except under conditions of aeration. So, the f~st step in consulting the systeminvolves specification of the acid gas (HzS and COZ) partial pressures as well as the bicarbonatecontent of the environment.

2. Temperature/Gas-Water ratios: Temperature has a signitlcant impact on corrosion rates as

described in the previous section. Corrosion rates typically increase with increasing temperature. If

the Gas to Oil Ratio indicates gas dominated conditions (as opposed to an oil dominated system) thesystem uses the water to gas ratio and the dew point as means to determine availability of an aqueousmedium to measure corrosion. So, depending on the value entered for the Gas to Oil Ratio, thesystem will let the user specify the relevant water-related parameters. If the Gas to Oil Ratio is lessthan 5MM1scf/bbl (which denotes an oil well), the system uses the water cut and oil persistency todetermine the wetness effect.

3. Chlorides/ sulfur: These parameters typically make corrosion worse if the process has been initiatedby the presence of acid gases. Their role, while not as critical as that of H2S or C02, is signitlcant

because these parameters can significandy increase corrosion rates in mildly corrosive systems.

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4. VeIoeity/Type of flow: Flow parameters are very critical in both determining and controlling

corrosion effects. Erosion corrosion as well as the protection (or the lack of it) from corrosion fti~s1svery much a function of fluid velocity.

5. Inhibitionlcorrosion allowance: Inhibition choices in the system allow the user to select applicablemethods of inhibition for vertical or horizontal flow and determine the extent of corrosion mitigation.In some cases, the system might provide no protection due to inhibition because of high velocities orchloride concentrations. The system’s rules assess the appropriateness of method of inhibitiondelivery for a given set of conditions.

Future Work

Predicting corrosivity of production environments is a complex and challenging task from several standpoints. ‘While the system described in this paper captures the effects and interaction of several criticalparameters, significant opportunity exists for enhancing the system’s analytical and modeling capabilities.Further work in refining the predictive model described herein is governed by the following factors:

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Manv of the fundamental mechanisms driving corrosion have been well understood; however, there isstill a large body of on-going research grappling with providing accurate phenomenological models.Formation of suklde films and their stability as a function of temperature and pH is an area thatrequires quantification and better modeling.

A large number of parameters that influence the corrosion process and the Complex set of parameterinteractions that exacerbate or mitigate corrosion. New data generated in the laboratory or field

performance is critical to refining the existing mode].

The current work does not include mass transfer and momentum transfer effects on surface corr(,sion.Howwer, further work aims at including flow models that capture the effects of inertial and viscousforces in single phase and multi-phase flows. The wall shear stress developed as a function ofprevalent flow regimes has a direct influence on corrosion rate and is the focus of considerableresearch in both industry and academia. s2’40

Metallurgy of steels used in COZ/HzS production environments is critical to determiningenvironmental corrosivity. Metallurgical factors include microstructure, material processing(annealed, quenched and tempered, normalized etc.) and other morphology related factors likehardness of welds and residual stresses. Addition of residual and alloying elements (Cr, Cu, Ni etc.)has been shown to have signflcant impact on corrosion performance41’42. While there is some dataavailable for understanding the effects of metallurgy on corrosivity in COZ environments, very littleinformation is available on metallurgical effects vs. corrosivity in H2S environments and represents anarea of active research. The authors have currently initiated a research program on testing to quantifymetallurgical effects as it relates to corrosion in typical production environments.4q

Conclusions

Predicting corrosivity of COZ/HzS production environments is a complex and challenging task requiring ~clear understanding of the role of several critical parameters from a theoretical and practical stand point.While theoretical models are valuable from a perspective of mechanistic comprehension, it is necessary tointegrate different kinds of datahowledge and experience-based expertise to provide a realistic basis forcorrosion prediction. A hierarchical predictive model has been developed to integrate the effects andinteractions of several critical parameters enroute to determining system corrosivity. The model has beenimplemented as a Windows-based computer program and incorporates a framework that facilitatesfurther refinement.

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The authors would like to recognize and thank the contributions of researchers and pioneers in corrosionmodeling, whose efforts, available in the public domain, provided the fwmament on which the modelpresented in this paper has been built. The authors also acknowledge the contributions of numerousworkers within the authors’ orgamzation whose untiring efforts have contributed significantly to thedevelopment of the corrosivity model.

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(;i

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,1

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Figure 1: C02 Corrosion Nomogram

H2Sco*HCO~Temp.

Figure 2: System Flow Chart

11/16

\

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Figure 3: In-situ pH determinationfor productionenvironments

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Figure 4: Corrosion rate of steel as a function of pH

11/17

Figure 5: Effect of gas composition and temperature on corrosion rate

T~ F)150 200 250 300 350 400 450

I

~..100

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T(”C)

Figure 6: Effect of HZS and temperature on corrosion rate in pure iron

11/18

14

12

<10

8

6

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n’20 40 60 80 100 120 140

Figure 7:

Temperature, “C

Corrosion rate as a function of temperature and COZ pressure

Shear Stress, Pa19Pa 1 50Pa 350Pa

9~- ..-

12 15Flow velocity, m/s

Corrosion rate as a function of velocity and temperature

11/19

0.

Uninhibited

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Figure9: Effectof gas velocityon corrosion rate

-.❑ Acid Number

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Crude Oil Wetabilky

Figure 10: Effect of acid number on crude oil nettability

11/20

0.28 -

0.24 -

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Figure 11: Effect of changing crude oil type on corrosion rate as a function of water content

co

Flow rate: 2 m\s

Oxygen

Maximum EconomicCorrosion Rate

20 40 60 80 100 120Temperature (°C)

Figure 12: Effect of oxygen concentration as a function of temperature on corrosion

11/21

1 I I I I 1

20 40 60 80 100 120Temperature (“C)

Figure 13: Effect of oxygen concentration as a function of temperature on corrosion

11122

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The specifiedcorrosion allowanceis less thanthe predicted *

corrosion rate. Hencethe projectedlife of the steel willbe lessthm the desired seruicelife.

For Help, prss%F1

Figure 14: Predictive Model System Interface

11/23