Paper No.
10371
2010
CO2 CORROSION MODELS FOR OIL AND GAS PRODUCTION SYSTEMS
Rolf Nyborg
Institute for Energy Technology
P.O. Box 40, N-2027 Kjeller, Norway
[email protected]
ABSTRACT
Several CO2 corrosion prediction models have been developed for oil and gas production
systems. It can be difficult to understand the differences between the models and choose which
models to use. This paper gives an overview of the different prediction models used in the oil and gas
industry for evaluation of CO2 corrosion of carbon steel. The differences between the models are
explained. The models differ considerably in how they predict the effect of protective corrosion films
and the effect of oil wetting on CO2 corrosion, and these two factors account for the most pronounced
differences between the various models.
Keywords:
CO2 corrosion, carbon dioxide, prediction models, oil and gas
INTRODUCTION
Oil companies and research institutions have developed a large number of prediction models for
CO2 corrosion of carbon steel. Very different results can be obtained when the models are run for the
same cases due to the different philosophies used in the development of the models. Some of the
models are based little protection by corrosion product films or full water wetting. These models have
a built-in conservatism and can overpredict the corrosion attack significantly for many cases, but there
is little risk that they would predict low corrosion rates for situations where corrosion problems are
actually encountered in the field. Other models assume protection from oil wetting or corrosion films
and predict generally much lower corrosion rates. These models often rely on the company's field
experience of conditions where the corrosion rates have been at an acceptably low level.
©2010 by NACE International. Requests for permission to publish this manuscript in any form, in part or in whole, must be in writing to NACE
International, Publications Division, 1440 South Creek Drive, Houston, Texas 77084. The material presented and the views expressed in this paper are
solely those of the author(s) and are not necessarily endorsed by the Association.
1
Several of the models are mainly based on empirical correlations with laboratory data. Other
models are to a large degree based on field data. A few models are based on mechanistic modeling of
the different chemical and transport reactions involved in CO2 corrosion of carbon steel. The
mechanistic models are usually tuned against lab data to some degree, while the laboratory and field
data models often have some mechanistic equations as a starting point. The differences in predicting
the effects of oil wetting and corrosion product films represent the most important differences between
the various CO2 corrosion models. Some of the models have a very strong effect of oil wetting for
some flow conditions, while other models do not consider oil wetting effects at all. Some models
include strong effects of protective iron carbonate films especially at high pH or high temperature,
some include a qualitative risk for localized corrosion attack and some do not take any account for
protective corrosion films for formation water cases due to risk for localized attack.
All model developers want to be able to validate their models against real field data. For CO2
corrosion prediction models for the oil and gas industry this has proven to be a difficult exercise, as
the amount of reliable corrosion field data is scarce. Much of the available uninhibited field data has
been found to be either unreliable or insufficient for model validation purposes, or biased against
specific field conditions. For field data cases all the necessary input parameters for the corrosion
prediction models are often not known in sufficient detail in order to be able to run the models for
validation purposes. If a model is validated for a set of field data of similar type from one company and
maybe also from just one field, or similar or neighboring fields, a good correlation for the conditions
specific for that field may be obtained, but extrapolation of the model to other fields or conditions
different from the ones used in the validation may become highly uncertain.
As a result of these considerations several operators decided to join forces in an effort to collect
reliable corrosion field data from a number of operators into a database which can be used for sharing
field experience between operators and for model validation purposes. This has been done through
three joint industry projects conducted by the author. In these projects field data with actual corrosion
measurements were gathered from the participating oil companies. The different available CO2
corrosion prediction models were evaluated by performing sensitivity studies for the different models,
running the different corrosion models for a set of the field cases, and comparing predicted corrosion
rates with the actual measured corrosion rates. An overview of the different CO2 corrosion prediction
1
models based on this work has been published previously . This paper presents an update of this
overview, including more recent models and new versions of some of the models, and gives some
examples of how the field data were used for comparing the different corrosion prediction models. The
process of collecting field data and using them for evaluation of corrosion prediction models have
2, 3
been described in detail previously . The operators in these projects have also formulated tentative
guidelines for prediction of CO2 corrosion, emphasizing a methodology for defining the corrosion
4
severity levels rather than corrosion rates .
CO2 CORROSION MODELS USED IN THE OIL AND GAS INDUSTRY
De Waard Model
The model developed by de Waard and coworkers (hereafter denoted Model DW) was for several
years the most widely used CO2 corrosion model. The first version was published in 1975 and was
5
based on dependence of temperature and pCO2 only . The model has been revised several times
6, 7
7
since, when different correction factors have been added to the original equation . The 1995 version
represents a best fit to a large number of corrosion flow loop data generated at the author's
2
2
8
laboratory . The model uses a scale factor to take account for corrosion product scales, but this gives
only a minimum estimate of scale protectiveness. The model takes relatively little account for the
effect of protective corrosion scales, especially at high temperature or high pH. The model was
calibrated against laboratory data up to 80 - 90 °C, and the model does not give much account for
formation of corrosion films with good protective properties above this temperature. The scale factor
was meant to be used only when formation water is not present, due to the risk for breakdown of the
6
corrosion film in the presence of formation water .
The model includes an on/off factor for oil wetting in crude oil systems. Oil wetting and no
corrosion is assumed when the water cut is below 30 % and the liquid velocity is above 1 m/s. This
on/off oil wetting factor is used for crude oil only and not for condensate, as water is considered to
6
separate out much more easily in condensate systems . For oil pipelines the scale and oil wetting
factors result typically in either quite high corrosion rate (water wetting and no effect of protective
films) or no corrosion at all (oil wetting). The model includes pH calculation only for pure condensed
water or condensed water saturated with corrosion products, and requires pH as a separate input
when a formation water chemistry is specified. However, due to the moderate account for protective
films the model has relatively little sensitivity to variation in pH.
Norsok M-506 Model
This model( 1 ) (hereafter denoted Model NO) is an empirical model developed by the Norwegian oil
9, 10
companies Statoil, Norsk Hydro and Saga Petroleum . The model is fitted to much of the same lab
8
11
data as Model DW, but includes in addition more recent experiments at 100 to 150 °C. The model
takes larger account for the effect of protective corrosion films at high temperature and high pH than
DW and several of the other models. The model is considerably more sensitive to variation in pH than
DW. In the second revision of the model the lower temperature limit for the model was extended from
,
20 to 5 °C and a lower limit of 0.1 bar CO2 partial pressure was introduced 10 12. The model has been
issued as a standard for the Norwegian oil industry and is openly available10.
The model contains modules for calculating pH and wall shear stress. Three options for
calculating pH are available. For condensed water without corrosion products the pH is given by the
temperature and CO2 partial pressure. The pH in condensed water saturated with iron carbonate
produced by corrosion can also be calculated. For formation water the pH calculation is based on the
bicarbonate content and the ionic strength. Wall shear stress can be calculated from production rates
and pipe diameter. The model does not account for any effect of oil wetting. The model is not intended
for corrosion prediction in systems where pH stabilization is used for corrosion control. Practical
guidelines for application of this model have been given by Olsen et al12.
Hydrocor
This model( 2 ) (denoted Model HY) was developed by Shell to combine corrosion and fluid flow
modeling. Different CO2 corrosion models are coupled to models for multiphase flow, pH calculation
13, 14, 15
and iron carbonate precipitation
. An oil wetting factor is used for crude oil systems, but not for
gas condensate, which is not regarded as giving any protection by oil wetting, as water separation is
likely to occur. Oil wetting and no corrosion is assumed when the water cut is below 40 % and the
liquid velocity is above 1.5 m/s14. The scale factor is applied for condensed water cases, but not for
(1)
(2)
NORSOK standard No. M-506: Standards Norway, Oslo, Norway
HYDROCOR: Shell Global Solutions, Amsterdam, The Netherlands
3
3
14
formation water cases, as porous mixture scales may form with little protection . The scale factor
gives relatively weak protection from corrosion product films. Prediction of top-of-line corrosion is also
14, 15
included16 as well as simplified models for H2S corrosion and organic acid corrosion
. The program
includes a fluid flow model which calculates pressure, temperature and flow profiles along a pipeline.
This is then used for predictions of corrosion rate along the pipeline. The pH calculation takes account
for production of iron and bicarbonate due to corrosion and to iron carbonate precipitation, giving an
increase in pH along the pipeline.
Corplus
This model( 3 ) (denoted Model CO) is developed by Total and is a result of a merger of the Cormed
17, 18
19
tool (CM) developed by Elf
and the Lipucor model (LI) developed by Total . CM and LI are no
longer used by Total and have been replaced by Model CO. The model is based on detailed analysis
of the water chemistry including effects of CO2, organic acids and calcium, and a large amount of
corrosion field data, particularly for wells. Free acetic acid and pH are identified as key parameters for
corrosion prediction. The pH calculation method is the same as in the previous CM tool. Model CO
gives a potential corrosivity without any protection from corrosion films or oil wetting. This is the same
as in CM. It then uses the fluid flow calculations from LI, and gives a predicted corrosion rate as the
potential corrosivity with no effect of protective corrosion film (from CM) times a water wetting factor
(from LI), typically giving low corrosion rates for liquid velocities above a critical velocity which is often
calculated to around 0.5 m/s. s. A possible reduction in corrosion decrease due to oil wetting is also
calculated In addition to the numerical values for corrosion rate the model gives a CO2 corrosiveness
in categories from Very low to Very high. This includes additional qualitative criteria determined from
an extensive review of Total field experience. A Very High Corrosiveness means that early failures
have been reported in the field in less than 3 to 5 years, corresponding to corrosion rates above about
3 mm/yr. Very Low Corrosiveness means that there is good field experience without any corrosion
indication after more than 30-40 years service life, i.e. corrosion rates < 0.1 mm/yr. The CO2
corrosiveness does not include any possible beneficial effect of oil wetting. The combination of the
predicted corrosion rate and the CO2 corrosiveness category then gives the most complete picture of
the actual case.
Model CO gives recommendations on how to adjust the water chemistry if it is not evaluated as
consistent with respect to calcium carbonate saturation. If the specified water chemistry, temperature
and CO2 partial pressure shows supersaturation of CaCO3 it is recommended to check for the most
likely cause and correct it until the saturation is evaluated as consistent. The most likely cause is often
an over-estimate of the bicarbonate content. If the user ignores this recommendation Model CO will in
the pH calculation reduce the bicarbonate level until CaCO3 saturation is reached, giving a lower
calculated pH for CaCO3 supersaturated waters than most of the other prediction programs which do
not correct for CaCO3 supersaturation.
Cassandra
This model( 4 ) (denoted Model CA) is BP’s implementation of Model DW, including company
20
experience in using this model . In this model a pH calculation module is included, where the pH
value is calculated from the CO2 content, temperature and full water chemistry. The effect of protective
corrosion films can be included or excluded by the user by choosing the scaling temperature. Above
(3)
(4)
CORPLUS: Total, Pau, France
Cassandra: BP Exploration, Sunbury, United Kingdom
4
4
the scaling temperature the corrosion rate is considered constant instead of reduced with increasing
temperature as in Model DW. The model thereby gives less credit for protective films at high
temperature. However, it is not distinguished between effect of corrosion films for cases with and
without formation water. Acetate in the water analysis is assumed to be present as acetic acid, giving
a lower pH value when acetate is present. The presence of acetic acid is pointed out as important both
due to the effect of acetic acid on the corrosion rate and the possibility of overestimation of
bicarbonate content and pH from a water analysis when acetate is present21. Oil wetting effects are
not included in this model. Important aspects in the practical use of this model are the use of corrosion
inhibitor availability rather than inhibitor efficiency and the use of corrosion risk categories as a way of
quantifying the corrosion risk22.
KSC Model
This model( 5 ) (denoted Model KS) is a mechanistic model for CO2 corrosion with protective
23, 24
corrosion films developed at Institute for Energy Technology
. The model is based on an
25
electrochemical model by building it together with a transport model. The model simulates
electrochemical reactions at the steel surface, chemical reactions in the liquid phase, diffusion of
species to and from the bulk phase and diffusion through porous iron carbonate films. The properties
of the protective corrosion films are correlated with a large number of flow loop experiments. The
model calculates the concentration profiles and fluxes of the different species and the resulting
corrosion rate. The model calculates a corrosion rate without protective films, a corrosion rate with
protective films and a risk for mesa attack. The corrosion rate with protective films is the preferred
value, but when the risk for mesa attack is high the corrosion rate without film should be used.
This model includes a relatively strong effect of protective corrosion films which is sensitive to pH
and temperature, and therefore tends to predict low corrosion rates for high temperature and high pH.
The model does not take any effect of oil wetting into account. The strength of Model KS is primarily
as a tool for understanding the different processes taking place during CO2 corrosion of carbon steel
in the presence of protective corrosion films. It is not primarily intended as a design tool.
Multicorp
This model( 6 ) (denoted Model MU) is developed by Ohio University. This model was originally
based on a Model KS using mechanistic modeling of the chemical, electrochemical and transport
23
processes occurring during CO2 corrosion . This has been developed further at Ohio University by
including modeling of multiphase flow, precipitation of iron carbonate films and effects of oil wetting
26, 27
and crude oil chemistry, and further verified against laboratory and field data
. Effects of organic
28
acids and H2S, including iron sulfide film precipitation, has also been included . The model is based
on detailed mechanistic modeling of the kinetics of chemical reactions in the bulk and electrochemical
reactions at the steel surface and transient transport of species between the bulk solution and the
steel surface, through the turbulent boundary layer and a porous corrosion product layer. Kinetics of
iron sulfide and iron carbonate precipitation, growth of corrosion product layers and development of
localized attack is also modeled. Further effects of multiphase flow including water entrainment and
dropout, effects of H2S and organic acids, effects of steel type and effects of inhibition by crude oil
and/or corrosion inhibitors are included to make a comprehensive model well suited for understanding
(5)
(6)
KSC Model: Institute for Energy Technology, Kjeller, Norway
MULTICORP: Ohio University, Athens, Ohio
5
5
all the various mechanisms occurring during CO2 / H2S corrosion of carbon steel. The model is
correlated against a large amount of laboratory data and some field data.
ECE Model
This model( 7 ) (denoted Model EC) developed by Intetech is based on Model DW, but includes a
module for calculation of pH from the water chemistry and bicarbonate produced by corrosion, a new
29, 30
oil wetting correlation and effects of small amounts of H2S and acetic acid
. It is developed for wells
and flowlines. The oil wetting correlation is based on a compilation of tubing corrosion data from a light
crude oil field29. The oil wetting factor is dependent on the oil density, the liquid flow velocity and the
inclination of the flow. An emulsion breakpoint is calculated from the oil density. This can be 40 to 50
% water cut for heavy crude oils and close to zero for light condensate. For water cuts lower than the
emulsion breakpoint the water is believed to be present as a water-in-oil emulsion, and the predicted
corrosion is low, but not zero. The critical flow velocity for water dropout is taken as 1 m/s for
horizontal flow and lower for inclined flow.
The model includes a module for calculation of pH from the water chemistry and bicarbonate
produced by corrosion. The way of accounting for bicarbonate produced by corrosion can result in
markedly higher calculated pH than many other models. However, it is possible to override this an
calculate the pH without bicarbonate produced by corrosion. On the other hand the calculated
corrosion rates are not very sensitive to calculated pH. The model includes H2S effects, effect of acetic
acid and calculation of top of line corrosion. Small amounts of H2S can give a considerable decrease
in the predicted corrosion rate due to protection by iron sulfide films.
Predict
This model( 8 ) (denoted Model PR) was developed by InterCorr International, now a part of
31, 32, 33
Honeywell
. The basic part of the model is based on Model DW, but other correction factors are
used together with an effective CO2 partial pressure calculated from the system pH. The model
includes very strong effects of oil wetting and variation in pH, and this tends to give very low corrosion
rates for many situations. The model includes a flow modeling module for calculation of flow velocity
and flow regime. The model distinguishes between persistent and not persistent oil types in order to
predict oil or water wetting. Low corrosion rates are typically predicted when the water cut is below 50
% for highly persistent oils and 5 % for not persistent oils. The model has a very strong pH
dependence on the corrosion rate, due to both effect of protective corrosion films and particularly
+
effect of H mass transport limitations. This tends to give low corrosion rates when the pH value is
higher than 4.5 to 5.
Tulsa Model
The SSPS CO2 corrosion model( 9 ) developed at the University of Tulsa (denoted Model TU) is a
mechanistic two-phase flow model with detailed modeling of the kinetics of electrochemical reactions
34, 35, 36
and mass transfer
. The model calculates the corrosion rate in presence of iron carbonate scales
and also indicates what the corrosion rate would have been without the formation of iron carbonate
scales. The model puts much emphasis on flow modeling and can be used for straight pipes or
(7)
Electronic Corrosion Engineer (ECE): Intetech, Chester, United Kingdom
PREDICT: Honeywell Corrosion Solutions, Houston, Texas
(9)
SPPS CO2: University of Tulsa, Tulsa, Oklahoma
(8)
6
6
elbows. The model has a very strong effect of protective corrosion films. This effect is highly
dependent of pH, and the model is therefore very sensitive to variation in pH, with low corrosion rates
usually predicted when the pH value is above 5. The model also indicates the corrosion rate in
absence of protective iron carbonate films in the case that the films are destroyed for instance by
erosion or tool scratching. The Tulsa group has worked extensively on sand erosion and erosioncorrosion. The model has a high sensitivity to flow velocity. It does not take any effect of oil wetting
into account.
ULL Model
This corrosion model( 10 ) (denoted Model UL) consists of a package of programs developed
37, 38
primarily for gas condensate wells by the University of Louisiana at Lafayette (ULL)
. The model
calculates temperature and pressure profiles, phase equilibria, flow rates and flow regime and then
calculates the pH profile and predicts the corrosion rate profile along the well. The model puts much
weight on calculating the flow regime and the location for condensation of water and hydrocarbons in
the well. The model has a strong effect of oil wetting when hydrocarbon condensation occurs. It
typically predicts oil wetting and no corrosion for the part of the tubing where hydrocarbon
condensation occurs, and corrosion when only water condenses. The model has later been extended
39, 40
to cover oil and gas pipelines
. This version contains multiphase flow calculations and has not
been evaluated in the work described here.
CorPos
This model( 11 ) (denoted Model CP) is a tool developed by CorrOcean (now part of Force
Technology) where results from multiphase flow calculations are combined with water chemistry
calculation and a point corrosion model in order to calculate pH and corrosion rate along a
41, 42
pipeline
. The model is based on using input from an external fluid flow model combined with
calculation of a probability of water wetting and calculation of pH. Bicarbonate produced by corrosion
is accounted for in the pH calculation, giving an increase in the pH along the pipeline. Model NO is
then used to calculate the corrosion rate in several points along the pipeline. A probability of water
wetting is calculated depending on water cut, flow regime, local phase velocities and emulsion
stability. This gives lower corrosion rates than Model NO for pipelines with very low water cut.
OLI Model
The corrosion model developed by OLI Systems( 12 ) (denoted Model OL) combines a
thermodynamic model for the concentration of molecular and ionic species of aqueous systems with
an electrochemical corrosion model and a model for formation and dissolution of iron carbonate or
43, 44
sulfide scales
. The model is based on detailed mechanistic modeling of the phase behavior and
the various chemical and electrochemical reactions. Much weight is put on thermodynamic calculation
of the phase equilibria and the concentration of the different species in the system. Protectiveness of
corrosion films is modeled by assuming that the electrochemical reactions only occur on the parts of
the steel surface not covered by corrosion films. The scale formation parameters have been calibrated
against selected laboratory data. The model does not include any effect of oil wetting.
(10)
UL Lafayette Corrosion Prediction Model: University of Louisiana at Lafayette, Lafayette, Louisiana
CorPos: Force Technology Norway, Trondheim, Norway
(12)
OLI Corrosion Analyzer: OLI Systems, Morris Plains, New Jersey
(11)
7
7
SweetCor
This prediction tool( 13 ) (denoted Model SW) was developed by Shell for analysis of CO2 corrosion
by managing a large database of corrosion data from laboratory experiments and field data45. The
approach is to group data by ranges of temperature and CO2 partial pressure or by the stable
corrosion product. Statistical analysis of the grouped data is used to make correlations for predicting
corrosion rates for specific conditions. Corrosion prediction can be done by using these prediction
correlations or by filtering the database for conditions close to the conditions of interest in terms of
temperatures, CO2 partial pressure, flow/no flow and inhibitor/no inhibitor. The model includes only a
weak effect of protective corrosion films and does not take any effect of oil wetting into account. The
pH value is calculated assuming a bicarbonate content in the water corresponding to water saturated
with corrosion products, but the predicted corrosion rate is very little dependent on pH.
IMPORTANT FACTORS FOR CO2 CORROSION PREDICTION
Among the most important factors which are needed to understand the differences between the
various CO2 corrosion prediction models are how the pH value is calculated, effect of organic acids,
effects of protective corrosion films, effects of oil wetting, effect of H2S and connection with multiphase
flow properties. The importance of these factors are described below. An overview of how these
factors are treated in the different models is shown in Table 1. Some of the models have been issued
in several versions and the author has not evaluated the latest version of every model, so all details in
the table may not be accurate. Also several of the table entries include interpretations of weak,
moderate and strong effects which by nature are somewhat subjective. Nevertheless, the table should
together with the description in the following give some guidance to understanding the differences
between the models.
Determination of pH
One of the most crucial aspects in corrosion evaluation of oil and gas wells and pipelines is to
obtain a realistic estimate of the actual pH in the water phase. For cases with only condensed water
this should include an evaluation of increase in the pH of the condensed water due to bicarbonate
produced by corrosion. Some of the models include bicarbonate produced by corrosion in the pH
calculation. When formation water is produced it is important to obtain good water analysis data,
especially with respect to bicarbonate and organic acids. In many cases formation water samples are
very scarce during the first well tests. Another uncertainty is when and how much formation water will
actually be produced. It is often very uncertain when formation water breakthrough will occur. The
water chemistry and hence the pH and the resulting corrosion rate can change considerably if the
water composition changes from pure condensed water only to condensed water with small amounts
of formation water, or to a water chemistry dominated by formation water.
The reported pH in a water analysis is most often totally useless for a corrosion prediction, as it is
usually measured at atmospheric conditions after depressurization. This gives no information about
the actual pH in the pipeline, which must be calculated from the CO2 partial pressure, temperature,
bicarbonate content in the water and ionic strength. Several of the models have pH modules which
perform such calculations. On the other hand, some of the models show little dependence of pH on
the corrosion rate, and therefore uncertainties in the pH calculations will not have a large effect on the
predicted corrosion rate.
(13)
SweetCor: Shell Global Solutions, Houston, Texas
8
8
In some cases the specified water chemistry from a water analysis can indicate supersaturation of
calcium carbonate. It may be advisable to check the water analysis for supersaturation of calcium
carbonate at reservoir conditions, which may indicate an erroneous bicarbonate analysis since
supersaturation of calcium carbonate is not possible in the reservoir. In this case it may be advisable
to reduce the bicarbonate value to the bicarbonate solubility at reservoir conditions with the actual
calcium content, as it is more likely that the measured bicarbonate level is erroneous than that the
calcium measurement is too high. Only a few of the models have a built-in check of this effect.
For liquid only systems the CO2 partial pressure is defined by the conditions in the last separator
stage upstream the pipeline, or by the bubble point pressure for oil wells, which gives the actual
dissolved CO2 in the aqueous phase and a corresponding CO2 partial pressure. Using the CO2
mole % in the associated gas and the total pressure in an oil well without free gas can give much too
high estimates for the CO2 partial pressure, and consequently too high corrosion rate predictions.
TABLE 1.
IMPORTANT FACTORS IN CO2 CORROSION PREDICTION MODELS
OL
SW
9
9
UL
N
N
TU
S
N
PR
N
N
EC
N
N
Y
MU
W
W
W
KS
W
M
W
CA
L
CO
M
HY
L
L M F L M M L
L M F L
Lab data, Field data model,
Mechanistic model
Scale effect formation water*
N M N W W M M W S S
M
Scale effect condensed water*
W M W W W M M W S S
M
Effect of pH on corrosion rate*
W M W M W M M W S S S M
Risk for localized attack
Y
Y
Y
Y
Oil wetting effect crude oil*
S N M M N N S S S N S M
Oil wetting effect condensate*
N N N M N N M M M N S M
CaCO3 correction for pH
Y
Y
Effect of organic acid on corrosion
Y Y Y
Y Y Y
Y
Top of line corrosion
Y
Y
Y Y
Y
Effect of H2S on corrosion rate*
N N W N N N M S S N W N
Multiphase flow calculation**
N P M P N N P M P P M M
140 150 150 150 140 150 100 140
115
150
Max. temperature limit °C
10 10 20 20 10 20 20 20 70 17
10
Max. CO2 partial pressure bar
O O P O O O P C C P P P
Open, Commercial, Proprietary
* S - strong effect, M - moderate effect, W - weak effect, N - no effect
** P - point calculation, M - multiphase profile calculation, N - no multiphase flow calculation
NO
CP
DW
Model
120 120
20
C
P
Effect of Organic Acids
In addition to the CO2/bicarbonate buffer system also the H2S/sulfide and the acetic acid/acetate
buffering systems can be important for determining the actual pH value. The presence of acetic acid
and other organic acids can give too high values for bicarbonate and hence too high calculated pH
values if organic acids are not measured in the water analysis, which has been most typical until
21
recently . In addition, the presence of acetic acid can have important effects on the corrosion
process, especially at low CO2 partial pressures. Not all of the models take this effect into account.
Model CO distinguishes between condensed water and formation water cases with respect to
effects of acetic acid. When condensed water is specified, it assumes that acetic acid is
co-condensing with the water and lowers the pH of the condensed water. When formation water is
specified, a given acetate content in the formation water is assumed to be as acetate ions, resulting in
an increase in pH, if not explicitly given as acetic acid. Model CO also lists recommended default
values for total acetate content in produced waters when this is not known, as shown in Table 2.
These values are used within Total when no water analysis is available. The values are developed
4
from general experience on organic species analyses of reservoir and condensed water . Based on
experience the total organic acid content is likely to be closer to this value than to zero when no data
is available. The temperature considered is the bottom-hole temperature.
TABLE 2.
DEFAULT VALUES FOR TOTAL ACETATE IN meq/l IN FORMATION WATER
In meq/L
≤ 1% CO2 in gas
T < 60 °C
0
T < 80 °C
1
T < 100 °C
3
T < 120 °C
5
T < 135 °C
3
T < 150 °C
1
T ≥ 150 °C
0
* 1 meq/l is equivalent to 59 mg/l.
> 1% CO2 in gas
0
1
5
10
5
1
0
Effect of Protective Corrosion Films
One of the major difficulties with prediction of CO2 corrosion of carbon steel is the very important
effect of protective iron carbonate films especially at high temperature or high pH. At low temperature
the iron carbonate solubility is high and the precipitation rate slow, and protective films will not form
unless the pH is artificially increased. At high temperature the iron carbonate solubility is lower and the
precipitation rate much faster, and very dense and protective iron carbonate films can form. This can
lower the corrosion rate from several mm/y for a carbon steel without any corrosion films to less than
0.1 mm/y when protective films are present. The effect of protective corrosion films can almost be
considered as an on/off switch, and the success of a prediction model depends to a large degree on
whether it is able to predict the presence or absence of protective films or localized attack reliably,
rather than the ability to predict the general corrosion rate in the absence of protective films with a
certain accuracy. The picture is further complicated by the tendency to development of localized attack
in the form of pits or mesa attack on steel surfaces with a partially protective film.
10
10
The effect of protective corrosion films on the predicted corrosion rate varies considerably
between the models. Some of the models include very strong effects of protective films, while others
include only moderate effects of protective films or do not take any account for protective corrosion
films for formation water cases due to high risk for localized attack for such cases. Generally the
models with the strongest effects of protective films rely on easy formation of corrosion films with good
protective properties and absence of localized attack, while the models with weak effects of protective
corrosion films assume that the films have only limited protectiveness or that there is a high risk for
localized attack. Some of the models predict a corrosion rate with protective corrosion films together
with the corrosion rate if there were no corrosion films, and some include also a qualitative risk for
localized corrosion attack.
Effect of Oil Wetting
It is important to know whether water or oil wets the steel surface since corrosion takes place only
when water is present at the surface. If the water is transported as a water-in-oil emulsion or
dispersion the corrosion can be substantially reduced. The degree of oil wetting depends heavily on
flow conditions, water cut and the properties of the actual hydrocarbon, and this is difficult to assess
without performing laboratory or field tests with the actual oil-water mixture. Some of the models have
a very strong effect of oil wetting for some flow conditions, while other models do not consider oil
wetting effects at all, either because oil wetting effects are not modeled or because it is believed that
water will wet the steel surface and cause corrosion somewhere in the pipeline anyway. Better
understanding of the parameters controlling the entrainment of oil and the oil wetting mechanism has
a potential to improve the corrosion predictions and increase the confidence in the models.
In the simplest form oil wetting is considered an on/off switch in the models: either the surface is
water wet with full corrosion or oil wet with zero corrosion. In Model DW oil wetting and zero corrosion
was assumed if the liquid flow velocity is larger than 1 m/s and the water cut is less than 30 %. This
effect is used for crude oil only and not for condensate, as water is considered to separate out much
6
more easily in condensate systems . In the more recent Model HY these limits were changed to 40 %
and 1.5 m/s, and partial protection is assumed when the water cut and flow velocity are above these
limits. Also here no protection is assumed for condensate systems14. In Model EC the oil wetting factor
is dependent on the oil density, the liquid flow velocity and the inclination of the flow29. Model PR
includes strong effects of oil wetting, and distinguishes between persistent and not persistent oil
types31. Low corrosion rates are typically predicted when the water cut is below 50 % for highly
persistent oils and 5 % for not persistent oils. Model UL has a strong effect of oil wetting when
hydrocarbon condensation occurs. It typically predicts oil wetting and no corrosion for the part of the
well where hydrocarbon condensation occurs, and corrosion when only water condenses. Another
approach is to model the transition between separated and dispersed water/oil and use this to
46
determine the degree of water wetting .
Connection with Fluid Flow Modeling
CO2 corrosion is dependent on the flow velocity, and the models have varying degree of flow
dependence. Most of the models include a simplified fluid flow calculation based on production rates
and pipe diameter, while some only take flow velocity as input and require that liquid flow velocity in
the well or pipeline have been calculated by a fluid flow model. A corrosion module has been
11
11
developed in the OLGA three-phase fluid flow model 14 , where the NO and DW models have been
46
combined with this fluid flow model . Some of the corrosion models include a full fluid flow model
giving temperature, pressure and flow profiles along the well or pipeline. Other models include options
for point calculations of the flow parameters used in the corrosion prediction. A few of the models take
liquid velocity as input and require that this has been calculated separately.
The variation of temperature along a well or pipeline is often much more important for the
corrosion behavior than variation in flow velocity and flow regime, and there may have been a
tendency to over-emphasize the effects of flow parameters on CO2 corrosion. The most important
effects of the flow parameters are probably the switch between oil and water wetting and the effect of
flow on localized attack on surfaces covered by corrosion films, and not the effect of flow velocity on
the corrosion rate itself, especially for cases where protective corrosion films may form.
Top-of-Line Corrosion
The discussion above concerns primarily corrosion in the bulk water phase. The situation is quite
different for top-of-line corrosion when water condenses out in the upper part of a pipeline. The
condensing water is unbuffered with low pH, but can become rapidly saturated or supersaturated with
corrosion products, giving rise to increased pH and possibility for iron carbonate film formation. The
top-of-line corrosion rate then becomes dependent on the water condensation rate and the amount of
47
iron which can be dissolved in the condensing water . Already an early version of Model DW included
6
a very simplified model for top-of-line corrosion , but little attention was paid to this type of corrosion
48
until top-of-line corrosion failures started to emerge in gas fields in the Far East in the 1990's . Topof-line corrosion is now included several of the corrosion models. In addition to the models discussed
above, Institute for Energy Technology has developed a dedicated top-of-line corrosion model which is
dependent on the water condensation rate and the amount of iron which can be dissolved in the
47
condensing water . A key parameter to prediction of top-of-line corrosion is estimation of the water
condensation rate, which requires a rather detailed fluid flow simulation including heat transfer through
the pipe walls. Top-of-line corrosion is primarily a concern in the first few kilometers of wet gas
pipelines with relatively high inlet temperatures, as the water condensation rate is rapidly reduced
when the temperature decreases. The presence of acetic acid in the gas may increase the top-of-line
corrosion rate considerably, as it increases the amount of iron which can be dissolved in the
condensing water before protective corrosion films are formed.
Effect of H2S
All the models discussed here are primarily CO2 corrosion prediction models and are not
particularly suited for situations with appreciable amounts of H2S. When even small amounts of H2S
are present, the corrosion products are iron sulfide rather than iron carbonate, since iron sulfide is
much less soluble and precipitates much more rapidly than iron carbonate. Some of the models try to
take this into account, but the steps to be taken when moving from sweet corrosion to sour corrosion
prediction are large since the type of corrosion films, corrosion attacks and mechanisms to be
predicted are very different. Prediction models developed on the basis of formation of protective iron
carbonate films can therefore not be used for situations where iron sulfide films are formed instead,
and adding sulfide correction factors to CO2 corrosion models will not give reliable results. Some of
the models discussed above use the H2S content in the pH calculation without actually predicting
14
OLGA: SPT Group, Kjeller, Norway
12
12
sulfide-dominated corrosion, while some of the models give a warning that the results are not valid
when the H2S content is above a certain low level. Several of the models do not take the effect of
sulfide films into account at all. Some of the models show a marked reduction in corrosion rate due to
32, 44
iron sulfide films when the H2S content is higher than about 1 mbar or even lower
. Model HY uses
the sweet corrosion rate multiplied with a pitting factor between 0.7 and 6 for the sulfide dominated
regime defined as pCO2/pH2S < 20. This pitting factor indicates the tendency to pitting in sulfide
14
dominated systems and increases with chloride content and presence of elemental sulfur .
There is a need for H2S corrosion models that take the mechanisms for formation of different
types of iron sulfides and development of localized attack into account. Work is ongoing in this field in
several laboratories, including the author's, but this research area has not yet advanced as far as for
sweet corrosion with iron carbonate film formation. The best advice for the time being is to be very
careful in using any of these models for situations with more than a few mbar H2S, and to be aware of
the limitations of corrosion prediction models in taking H2S effects into account.
EVALUATION OF MODELS AGAINST FIELD DATA
A database of corrosion field data was collected by the operators representatives in three joint
industry projects conducted by the author. The difficulties in obtaining reliable corrosion field data
2, 3
have been reported previously . Most of the field data used for models evaluation were failure cases
where failure reports were available. The amount of available data varied considerably from case to
case. For some cases detailed corrosion data along the pipeline or well were available in the form of
caliper surveys or intelligent pigging data. For other cases corrosion data were only available at single
points or as maximum rates. For some of the cases the full production rate history was available, while
others only stated typical production rates. Typical for many of the cases was that when corrosion
problems are encountered, it is often difficult to trace back to obtain all the relevant information from
earlier stages of the history of the field. Even when the operators representatives in the project were
actively seeking for field data within their own organization it was often difficult to find all the necessary
data to be able to run the different models.
When field data are to be used for corrosion prediction model evaluation or validation it is
necessary to restrict the analyses to uninhibited cases, and this of course limits the number of
available field data that can be used considerably. A selection of available field data was made for use
in the corrosion models evaluation, based on the uninhibited cases where detailed and reliable
information was available for both production characteristics, geometry and water chemistry, and
where CO2 corrosion was identified as the corrosion mechanism. An overview of a few examples of
the field data cases is shown in Table 3. This shows three examples from the selection of field data
cases used in the models evaluation. In all these cases the corrosion rates were relatively high, from 1
to 5 mm/y. Two of these cases resulted in leakages, and for these cases corrosion measurements
were also available at other locations from intelligent pigging or caliper runs. The third case is from an
onshore pipeline with ultrasonic wall thickness measurement.
The case called Oil line 3 is a multiphase oil line which suffered multiple leakages after seven
years of operation. After the leakages this case was extensively investigated with intelligent pigging,
21, 49
inspection and detailed studies of metallurgy and water chemistry
. This pipeline had a low CO2
content but presence of acetic acid. At the locations with the largest corrosion damage the flow regime
was slug flow with high liquid velocity.
13
13
TABLE 3. OVERVIEW OF SELECTED FIELD DATA
Oil line 3
Oil well 5
Gas line 3
58 - 65
70 - 110
55
0.05 - 0.15
1.6
1.2
Bicarbonate mg/l
49
181
0
Acetate mg/l
100
Calcium mg/l
4 800
770
Chloride mg/l
114 000
20 000
59
70 - 80
55
0.07
1.6
1.2
Water cut %
30
5 - 80
10
Liquid flow velocity m/s
11
1.3 - 2.0
2.8
Corrosion rate mm/y
1.1
4.6
4.1
Corrosion measurement
Leakage,
pigging
Leakage,
caliper run
Ultrasonic
testing
Temperature °C
CO2 partial pressure bar
At location with max. corrosion:
Temperature °C
CO2 partial pressure bar
FIGURE 1 - Penetrating Localized Attack in Oil Well 5
The Oil well 5 is an oil well where a leakage occurred after 18 months of service. Contrary to the
belief during design, the well started to produce formation water very soon, with water cut increasing
from 5 % after 2 months to 80 % after 17 months. The CO2 molar fraction in the gaseous phase was
30 %, and the bubble point pressure was 5.2 bar. The wellhead pressure was always higher than this,
so this was a liquid-only oil well with a CO2 partial pressure of 1.6 bar. The calculated pH is between 5
and 5.5. Here both detailed caliper data over the whole depth and detailed inspection of the recovered
50
well string was available . The tubing had penetrating mesa attack close to the top of the well, at
around 100 m depth, corresponding to 4.6 mm/y corrosion rate based on the total production period. A
section of the tubing where penetrating localized attack was found is shown in Figure 1. Further down
in the well the corrosion rate was measured to 0.3 - 0.9 mm/y by caliper data.
14
14
The Gas line 3 case is from a short onshore gas line with condensed water only and 10 % water
cut. Ultrasonic wall thickness measurements were done in different bends just before the slug catcher
after one year of operation. The highest measured corrosion rate was 4.1 mm/y.
The corrosion rates predicted with some of the models for the field cases described in Table 3 are
compared with the actual measured corrosion rates in Table 4. Here the models are anonymized for
commerciality reasons and in order not to rank models based on a few example cases. Comparison
with a large number of field cases showed that none of the models performed markedly better for all
cases, while some of the models are more conservative than others. Here the maximum corrosion rate
measured for each case is compared with the predicted corrosion rates for the location where the
highest corrosion rate was measured. The Oil line 3 case has the lowest measured corrosion rate of
the cases in Table 3. This pipeline had a low CO2 content but presence of acetate. Early water
analyses reported 110 ppm bicarbonate. However, a later water analysis where acetate was looked
for showed 49 ppm bicarbonate and 100 ppm acetate. This is an example of a case where presence
of organic acids can lead to too high values for bicarbonate and hence too high calculated pH
21
values . The total alkalinity measured by standard titration will not give a correct measure for the
bicarbonate content when organic acids are present, since the total alkalinity is the sum of bicarbonate
and acetate, and varying amounts of acetate can be included in the titration depending on the end
point of the titration. This means that some of the acetate may be interpreted as bicarbonate if the
bicarbonate is taken as the total alkalinity and it is not analyzed for organic acids.
TABLE 4. MAXIMUM MEASURED AND PREDICTED CORROSION RATES
Maximum measured and
predicted corrosion rate mm/y
Oil line 3
Oil well 5
Gas line 3
Measured
1.1
4.6
4.1
Model A
0.8
4.1
10
Model B
1.5
4.1
8
Model C
1.1
7.3
5.9
Model D
0.4
4.2
4.3
Model E
0.6
1.7
4.5
Model F
0.9
2.0
4.3
Model G
0.1
0.3
4.9
For the Oil well 5 case the model predictions were done for different times during the production
history and for different depths of the well, and then integrated over the production history. The
resulting accumulated predicted corrosion at different depths in the well is shown in Figure 2 as
average corrosion rate over the tubing life. Some of the models predicted a considerable increase in
3
corrosion rate during the tubing life, as a result of increasing water cut . The predicted corrosion rates
are compared with the actual corrosion damage measured by caliper runs and inspection of selected
parts of the recovered tubing string. The corrosion damage was relatively small with general corrosion
with corrosion rates around 0.5 mm/y in the lower part of the well, and more severe in the upper part
of the well with localized attack, and penetrating mesa attack at around 100 m depth.
15
15
Figure 2 shows marked differences between the various prediction models. Models E, F and G
are quite successful in predicting the low corrosion rate in the lower part of the well, but are not able to
predict the severe corrosion in the top of the well. This is because these model take large effects of
protective corrosion films or oil wetting. Model B, C and D are all quite successful in predicting the
severe corrosion in the top of the well, but are not able to predict the lower corrosion rate deeper in the
well. This is because these models do not account for any effect of protective corrosion films for this
case, and consequently predict higher corrosion rates deeper in the well where the temperature is
higher. Model A is able to predict the high corrosion rate in the top of the well and also predicts lower
corrosion rates in the lower part of the well, since this model takes larger effect of protective corrosion
films at high temperatures.
Corrosion rate / (mm/y)
10
Model A
Model B
8
Model C
6
Model D
Model E
4
Model F
2
Model G
Measured corrosion
0
-1500
-1000
-500
0
Depth / m
FIGURE 2 - Predicted and Measured Corrosion Rates at Different Depths in Oil Well 5
The Gas line 3 is a case with only condensed water and a temperature of 55 °C. Under these
conditions protective films are not expected to form, and a corrosion rate of 4 mm/y was measured by
ultrasonic thickness measurements. Most of the models calculated a pH around 4 for this case, and
the predicted corrosion rates varied from 4 to 10 mm/y. All the models were able to predict that
unacceptably high corrosion rates would occur for these conditions.
It should be noted that these are only three examples from a database with a large number of field
data. Many of the field data cases in the database do not have enough detailed or reliable data to
obtain representative model predictions. However, these examples illustrate that an evaluation of any
model against field data can be strongly dependent on the selection of field data used for the
evaluation or validation and the reliability of these data. The cases described here are cases with high
measured corrosion rates. An evaluation of prediction models could be quite different for cases with
low corrosion rates.
16
16
A larger number of corrosion field data were included in the models evaluation in the project these
examples are taken from. The project also included different sensitivity studies not described in this
paper, showing the sensitivity of the models to variations in temperature, pH, flow velocity etc.
A general conclusion which can be drawn from this work is that it varies considerably from case to
case which models are most successful in their prediction, and it is not possible to declare one or two
models as better than the others. It is however important to understand the differences between the
models in order to interpret the predictions. Especially the effects of protective corrosion films and oil
wetting are modeled quite differently in the various models, and these are the two effects that may
shift between very high and very low predicted corrosion rates. Another observation is that no model is
able to predict the actual corrosion with reasonable accuracy for all different scenarios, and the author
would be skeptical to any model claiming better than ± 50 % accuracy for a wide range of conditions.
The accuracy will very easily become much less than this if the effects of protective films and oil
wetting are not predicted successfully. Even with the considerable uncertainties in the models as
described above, the uncertainty in the required input parameters will for practical field situations often
be even higher. When the prediction models are used in the design phase of a project, the available
input data are often very limited. This suggests that corrosion prediction models should be used with
caution, remembering the limitations in both the models and in the input data. Several operators are
now focusing more on defining the corrosion severity levels rather than numerical values for corrosion
rates, as this may be of more practical use for the fields and prevent people from believing in
4
unrealistic accuracies in the corrosion prediction models .
CONCLUSIONS
Several prediction models for CO2 corrosion of oil and gas pipelines are available. The models
have very different approaches in accounting for oil wetting and the effect of protective corrosion films,
and this accounts for much of the differences in behavior between the models. Some of the models
have a very strong effect of oil wetting for some flow conditions, while other models do not consider oil
wetting effects at all. Some models include strong effects of protective iron carbonate films especially
at high pH or high temperature. These models rely on easy formation of protective corrosion films and
absence of localized attack, while the models with weak effects of protective corrosion films assume
that the films have only limited protectiveness or that there is a high risk for localized attack. All the
models are capable of predicting the high corrosion rates found in systems with low pH and moderate
temperature, while the models can predict quite different results for situations at high temperature and
high pH, where protective corrosion films may form.
Evaluation of the different corrosion prediction models against actual corrosion field data has
shown that it can vary considerably from case to case which models are most successful in their
prediction, and it is not possible to declare one or two models as better than the others. An evaluation
of any model against field data can be strongly dependent on the selection of field data used for the
evaluation or validation and the reliability of these field data. It is however important to understand the
differences between the models in order to interpret the predictions. Especially the effects of protective
corrosion films and oil wetting are modeled quite differently in the various models, and these effects
may shift between very high and very low predicted corrosion rates.
17
17
ACKNOWLEDGMENT
The field data collection and corrosion models evaluation described in this paper was carried out
in three joint industry projects at Institute for Energy Technology. The following companies participated
in these projects: Total, Shell, BP, ConocoPhillips, Statoil, Chevron, Saudi Aramco, ENI, Gaz de
France, Petrobras, Petronas, Aker Solutions, Tenaris, Force Technology, American Bureau of
Shipping. The author wants to thank these companies for their technical and financial support.
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