Article1.4 Hedges

A PROPHETIC CO2 CORROSION TOOL – BUT WHEN IS IT TO BE BELIEVED ?

Bill Hedges BP, P.O. Box 714, Port of Spain, Trinidad.

Richard Chapman, Don Harrop & Imran Mohammed

BP Exploration Technology, Chertsey Rd., Sunbury on Thames, Middlesex, UK.

and

Yuhua Sun BP Exploration Technology, 501 Westlake Park Blvd, Houston, TX 77079, USA

ABSTRACT This paper reviews the development of our company’s CO2 corrosion rate prediction model and highlights some significant differences between it and other models. Topics such as the calculation of pH, the treatment of fugacity, scaling, oil wetting, acetates and hydraulic diameter are discussed in some detail. It discusses the limits, errors and uncertainty associated with the model so that the reader may be able to consider if such models can really be believed. The authors hope that the final conclusion will be that such models can be trusted providing the user understands these limitations.

The further development of such models has been addressed. On this question the authors indicate that model discussed is not perfect and that we would happily change to an alternative providing that clear benefits can be demonstrated.

It is suggested that the future lies in the development of mechanistic models and that the industry

collectively, not individually, should address this issue. Keywords : CO2, Carbon, Dioxide, Corrosion, Rate, Prediction, Cassandra.

Nigel Kimber - Invoice INV-242097-6ZCVBM, downloaded on 9/11/2009 10:25:17 AM - Single-user license only, copying and networking prohibited.

INTRODUCTION This paper discusses the BP CO2 corrosion rate prediction tool known as Cassandra. Throughout the rest of this paper BP will be referred to as “Our Company” and Cassandra as “Our Tool” or “Our Model”. The paper does not explain how to use the tool or other CO2 prediction programs but discusses the development of it and the features that make it different from other models. Limitations of the model as well as areas for future work and collaboration are discussed. Objectives and Accuracy of CO2 Corrosion Prediction Programs

The answer to this question may appear obvious but in practice will depend on the users’ requirements. The majority of applications are associated with the design phase of new oil or gas facilities when decisions related to the materials of construction are made. The most common choice that has to be made is between carbon steel or corrosion resistant alloy (CRA). Typically, this simply requires an estimate of the expected corrosion rate that is compared to the users recommended values for these materials. For example, many companies prefer to use CRA if the predicted corrosion rate is above 5-10 mm/y. In such situations a value to within an accuracy of ± 1 mm/y is sufficient and calculations to decimal places, even if possible, do not add any value.

In the sometimes-heated debates related to the accuracy of corrosion models this last point is often overlooked. Even in the academic area of model development it is possible to loose sight of the primary objective and significant time and effort have been expended to develop models for conditions that would never be used in practice (e.g. the use of uninhibited carbon steel containing corrosive fluids under severe slug flow conditions).

If carbon steel is chosen then it is necessary to predict the corrosion rate as accurately as is reasonably possible so that the correct corrosion allowance can be selected.

Once a system is operating the value of a corrosion model is usually less than that of a good corrosion monitoring and inspection program. However, for systems that do not have such programs, corrosion models can be used to prioritise which parts of the system should be studied first by identifying the highest risk areas.

In some cases corrosion models are used to help identify the mechanism of the corrosion reaction. For example, if the predicted CO2 corrosion rate is 0.5 mm/y but the observed rate is 5 mm/y it is probable that the dominant mechanism is not CO2 related. In these cases other possible mechanisms such as those related to oxygen (O2), mineral acids or bacteria should be investigated.

The question of accuracy is a key driver for the developers of commercial corrosion prediction models. In theory, being able to predict a corrosion rate as accurately as possible is the ultimate objective especially when facilities costing US$100’s of millions are involved. In this context a spend of US$50K / year would appear to be a good investment. In practice this must be balanced against the accuracy really needed. In recent years many oil and gas operators have concluded that the accuracy of models currently ava ilable is sufficient and have become reluctant to buy models that have high licensing costs or invest in R&D programs to further develop models. This is an even bigger concern for many engineering design houses that do not have the same budget resources as oil and gas companies. This was a major consideration in the development of the tool discussed in this paper.


Historical Development

Our model is based on the equations first published by C. de Waard and D.E. Milliams1-4 in the mid 1970’s with significant additions in 1991, 1993 and 1995. These equations, which are widely used in the Oil & Gas industry, became known as the de Waard & Milliams equations and will be referred to as such in this paper.

With time many companies modified the equations to account for their own preferences and /or experiences, which led to an inconsistent use of them. Moreover, the exact method of calculation was obscured by the use of different practices and correction factors. This proved increasingly problematic for us when dealing with external engineering companies who often did the calculations for our Projects. Version 1: The 1998 Tool: Consequently, in 1997 we began a project to formalize the way the calculations were done and provide transparency to them. An Excel™ based spreadsheet was built to generate the corrosion rates using standardized inputs and it was released in May 1998 to both the company and its contractors. It was released with a guidance document5 detailing the logic used together with advice on how to use the results during the design phase of a project.

This model, together with the guidelines, superseded previous company documents covering this topic. They described our approach to Corrosion Prediction and its use during the design of pipelines and facilities. Important features were:

1. A clear, transparent method for calculating corrosion rates. 2. The introduction of probabilistic modeling as an alternative to the traditional deterministic

approach (reflecting the reality that real systems experience a distribution of corrosion rates and not a single, discrete rate).

3. A move away from the use of corrosion inhibitor efficiencies to the use of availabilities. 4. The introduction of Corrosion Risk Categories as a way of classifying systems.

The guidelines were divided into two sections; the first section introduced the new prediction

spreadsheet as our implementation of the CO2 prediction models published by de Waard et al. It built on these models to include our experience of such systems. The second section discussed how the prediction model should be used for design purposes and introduced several improvements over previous guidelines. The new material and concepts originated from many sources including the results of Joint Industry Projects, in-house R&D and practical experience from both ours and other operators. To illustrate the points made, examples were provided from several of the company’s assets worldwide. Version 2: The 2001 Tool. This 2nd version was developed based on feedback from the users of the 1st version and because Microsoft™ had changed the macro structures in the spreadsheet program. It included updated macros and components, which were compatible with Windows 2000™ software. In order to provide the required functionality for the new version it was necessary to write detailed macros which inevitably reduced the ‘transparency’ of the calculations. Due to the loss of transparency of some of the calculations the need for reference to the guidelines was very important for users who wanted to understand how the calculations were made. New Features introduced were:

1. Option to store brine chemistries 2. Option to predict corrosion rates over a range of one parameter (e.g. Temperature). 3. Incorporation of a multiphase model


Version 3. Issued in 2003. This is the current version of our model which was developed to increase the functionality further and to fix a serious error in the multiphase program of version 2. The flexibility has been introduced by the use of array functions. It is targeted at experienced corrosion engineers. The date suffix was removed to avoid any confusion that an ‘out of date’ version may be being used. The rest of this paper will only discuss the latest version of the tool The Name

For the release of the first version in 1998 a competition was run to find a name for the new tool. The winning submission was Cassandra6. Cassandra, Figure 1, is a character from Greek mythology and was the daughter of Priam and Hecuba. She was endowed with the gift of prophecy but fated never to be believed. She is regarded as the prophet of disaster – especially when disregarded!

Figure 1: Cassandra

Corrosion engineers who have had to present corrosion modeling results to project teams or

operations personnel will sympathise with this description. Often our work is regarded as pessimistic and ignored only for it to be proved correct several years later when it is too late to do anything about it. Following the release of the first version it was suggested8 that the name was a mnemonic for:

Corrosion ASSessment AND Risk Analysis We liked this very much and continue to use it as a more “scientific” justification of the name !! Commercial Considerations

There was some discussion related to how the tool should be distributed and if it should be sold. It was agreed that commercially this would never be a core activity of an oil and gas company and so we would not sell it directly. However, to obtain some income from the product was considered desirable to cover the development and maintenance costs of it. Consequently, options to license the product to a 3rd party were, and still are, considered. However, one of the primary goals for developing the tool was to make our experience and requirements available to the multitude of design engineering companies across the world. This is because such companies design almost all new facilities. This consideration over-ruled all others and so it was agreed that to ensure all companies, irrespective of their size or resources, had access to the tool it would be distributed as freeware.


Provision of Updates

One problem with freeware is that it is impossible to know how many copies have been shared and who has them. Consequently providing updates and fixes for problems has been difficult / impossible. For the latest version we have tried to control this by asking recipients not to provide copies to others but ask them to contact us directly for their own copy. In this way their email addresses are captured and used for the provision of updates. Training

To date this has only been provided on an informal basis to in-house engineers. However, as the tool has become more complex there have been an increasing number of requests for training. Commencing in 2005 there are plans to run 1-day workshops in several locations for anyone who wants to attend. Invitations to the workshops will be sent using the email distribution list noted above. The Company’s Requirement for use of The Tool

The tool is our preferred CO2 corrosion rate prediction program although its use is not compulsory for our Projects. We respect the capability of 3rd party design companies and recognise that they may have different models that they prefer to use. However, for assurance purposes The Company’s Corrosion Engineers will tend to check calculations against results generated by our model. If discrepancies are found we reserve the right to insist that our models values are used. Consequently 3rd parties who undertake such calculations for us are strongly recommended to become familiar with and use of our model in addition to their own tools. Format of The Model

The tool is spreadsheet based and has undergone several changes from a basic spreadsheet to the latest version which has been implemented in terms of array functions. For those that have never used these before they can appear complex but a detailed help file has been developed to enable users to become familiar with them. There are two array functions which form the basis of the tool:

• CASS_RATE: Uses relevant input data to generate corrosion rates. • CASS_FLOW: Uses relevant input data to generate a description of the flow regime.

When the model is installed the array functions are made available within the spreadsheet program

itself (as “add- ins”) and so are always available when spreadsheet is run and are not dependant on any specific worksheets. The benefit of using such functions is that a spread-sheet can be set up to do many calculations at one time such as:

• Corrosion rate vs. a range of temperatures, pressures, brine chemistries, etc. • Corrosion rate at different locations along a pipeline.

Tables 1-4 show the arguments (inputs) and property codes (outputs) for each function. It should be

noted that the CASS_FLOW function is a multiphase model that uses well-known correlations to predict the hydrodynamic properties of black oil at the conditions provided. It cannot predict the behaviour of emulsions. Further information on the functions can be obtained from the help file in the program itself or the authors.


Table 1: The Arguments (inputs) for the CASS_RATE Function CASS_RATE (UnitCode, PropertyCodes, LocalTemp, TotalPressure, PercentCO2, phModel, ScalingTemp, PercentH2S, PercentGlycol, LiqVelocity, HydraulicDiam, phManual, Ions , Concentrations , WaterGravity, AcetateAsAcid)

Argument Type Description Units

UnitCode Text, Required

Unit set specifier. Only text values "C" or "F" are acceptable inputs, corresponding to temperatures/ pressures/lengths in °C/bara/m or °F/psia/ft respectively.

None

PropertyCodes Text, Required

Array of text-string property codes specifying the output quantities that are to be returned by the function. None

LocalTemp Numeric, Required Local system temperature. °C or °F

TotalPressure Numeric, Required Total system pressure. bara or psia

PercentCO2 Numeric, Required Molar/volume percentage of carbon dioxide in the gas phase. None

phModel Text, Required

Text string denoting which pH model is to be used. Acceptable options are "BP" for the BP in-house pH model, "OT" for the Oddo and Tompson model, "DW" for the de Waard and Milliams model, and "MANUAL" for direct entry of the pH.

None

ScalingTemp Numeric, Optional Scaling temperature. °C or °F

PercentH2S Numeric, Optional

Molar/volume percentage of hydrogen sulphide in the gas phase. Default value if unspecified is zero. None

PercentGlycol Numeric, Optional

Weight percentage of glycol in the aqueous phase. Default value if unspecified is zero. None

LiqVelocity Numeric, Optional

Actual liquid velocity. Default value if unspecified is zero, but a non-zero value is required in order to obtain the 1995 BP and de Waard model corrosion rates.

m/s or ft/s

HydraulicDiam Numeric, Optional

Hydraulic diameter. Default value if unspecified is zero, but a non-zero value is required in order to obtain the 1995 BP and de Waard model corrosion rates.

m or ft

phManual Text, Optional User-specified pH value. Required only if phModel = “MANUAL” None

Ions Text, Optional

Array of ion specifications defining the water analysis, and used in detail for pH prediction when phModel is set to "BP", and to define ionic strength and bicarbonate concentration when it is set to "OT". Specification of bicarbonate is required for model "OT". Acceptable specifications for ions are: "Na", "K", "Ca", "Mg", "Sr", "Ba", "Fe", "Cl", "HCO3", "SO4", "Ac". If used, this argument must be consistent with the specification of Concentrations, below.

None

Concentrations Numeric, Optional

Array of ion concentrations in the water analysis. Input must be consistent with the specification of Ions, above, and is required input under the same circumstances.

mg/l

WaterGravity Numeric, Optional

Specifies the specific gravity of the water, used in the BP pH model. Default value if unspecified is 1.0. None

AcetateAsAcid Logical, Optional

Specifies whether acetate is included in the charge balancing within the BP pH model. Default value if unspecified is FALSE, corresponding to acetate being regarded as its salt.

None


Table 2: The Property Codes (outputs) for the CASS_RATE Function

Property Code Description Units PPCO2 Partial pressure of CO2 bara or psia PPH2S Partial pressure of H2S bara or psia FCO2A Fugacity of CO2 (actual) bara or psia ASCAT Accepted scaling temperature °C or °F FCO2U Fugacity of CO2 (used) bara or psia CSCAT Calculated scaling temperature °C or °F PHACT pH (actual) None PHUSD pH (used) None PHSAT pH of solution saturated in iron carbonate None CFPHB pH correction factor (BP model) None CFGLB Glycol correction factor (BP model) None CFFUB Fugacity correction factor (BP model) None CFSCB Scaling correction factor (BP model) None CFBP BP total correction factor None

CFPHD pH correction factor (de Waard model) None CFGLD Glycol correction factor (de Waard model) None CFFUD Fugacity correction factor (de Waard model) None CFSCD Scaling correction factor (de Waard model) None CFDW de Waard total correction factor None PHDW de Waard pH None BP93 BP 93 model corrosion rate mm/yr BP95 BP 95 model corrosion rate (requires input of non-

zero liquid velocity and hydraulic diameter) mm/yr

VBP BP overall corrosion rate (requires input of non-zero liquid velocity and hydraulic diameter)

mm/yr

DW93 de Waard 93 corrosion rate mm/yr DW95 de Waard 95 corrosion rate (requires input of non-

zero liquid velocity and hydraulic diameter) mm/yr

VDW de Waard overall corrosion rate (requires input of non-zero liquid velocity and hydraulic diameter)

mm/yr

STATS Return status and error flags None


Table 3: The Arguments (inputs) for the CASS_FLOW Function CASS_FLOW (UnitCode, PropertyCodes, LocalTemp, TotalPressure, GasFlowRate, OilFlowRate, WaterFlowRate, GasGravity, OilGravity, WaterGravity, IntDiam, Inclination, Roughness, PercentCO2, PercentH2S, PercentN2)

Argument Type Description Units UnitCode Text, Required Unit set specifier. Only text values "C" or "F" are

acceptable inputs, corresponding to temperatures/ pressures/lengths in °C/bara/m or °F/psia/ft respectively.

None

PropertyCodes Text, Required Array of text-string property codes specifying the output quantities that are to be returned by the function.

None

LocalTemp Numeric, Required

Local system temperature. °C or °F

TotalPressure Numeric, Required

Total system pressure. bara or psia

GasFlowRate Numeric, Required

Gas flow rate at standard conditions (14.7 psia, 60 °F). mmscf/d

OilFlowRate Numeric, Required

Oil flow rate at stock tank conditions. mb/d

WaterFlowRate Numeric, Required

Water flow rate at stock tank conditions. mb/d

GasGravity Numeric, Required

Specific gravity of gas (air=1). None

OilGravity Numeric, Required

Specific gravity of oil. None

WaterGravity Numeric, Required

Specific gravity of the water. None

IntDiam Numeric, Required

Internal diameter of the tubing or flowline. m or ft

Inclination Numeric, Required

Angle of the tubing or flowline, measured from the horizontal. The value must lie between -20° and +90°.

º

Roughness Numeric, Required

Roughness of the tubing or flowline surface. m or ft

PercentCO2 Numeric, Optional

Molar/volume percentage of carbon dioxide in the gas phase. Default value if unspecified is zero.

None

PercentH2S Numeric, Optional

Molar/volume percentage of hydrogen sulphide in the gas phase. Default value if unspecified is zero.

None

PercentN2 Numeric, Optional

Molar/volume percentage of nitrogen in the gas phase. Default value if unspecified is zero.

None


Table 4: The Property Codes (outputs) for the CASS_FLOW Function

Property Code Description Units GOR Input GOR scf/stb API API gravity of oil °API RS Solution gas-oil ratio scf/stb BO Oil formation volume factor stb/rb

RSW Solution gas-water ratio scf/stbw BW Water formation volume factor stbw/rb

VFW Volume fraction of water None SGDG Specific gravity of dissolved gas (air=1) None SGFG Specific gravity of free gas (air=1) None ZFAC Compressibility factor of gas None

BG Ideal gas equation constant ft3/scf QG In situ volume gas rate mmscf/d QL In situ volume liquid rate stb/d

VSG Superficial gas rate ft/s VSL Superficial liquid rate ft/s

ODENS Oil density lb/ft3 WDENS Water density lb/ft3 GDENS Gas density lb/ft3 LDENS Liquid density lb/ft3 GVIS Gas viscosity cP LVIS Liquid viscosity cP STEN Surface tension dyn/cm

HL Liquid holdup None DONR Depth/radius None DPDL Pressure gradient bar/m FRGR Frictional pressure gradient bar/m ELGR Elevational pressure gradient bar/m

ACCGR Accelerational pressure gradient bar/m HLNS No-slip liquid holdup None

HDIAM Hydraulic diameter m or ft VLIQ Liquid velocity m/s or ft/s FPAT Flow pattern None

STATS Return status and error flags None


Model Limits, Errors and Uncertainty

The original equations were based on correlations with laboratory data and as such have error bands associated with them. To understand these error limits it is important for the user to read the original papers and understand the assumptions and limits of the work in them. It is not unusual for an error band of ± 25% to be applied to the final corrosion rate.

The model is a deterministic tool, that is, for a given set of inputs a specific corrosion rate will be calculated. There are two types of uncertainty with the calculated number that must be considered: Errors in the input data. As with any calculation the accuracy of the input data is a critical factor for getting an accurate corrosion rate. This is often stated using the phrase “garbage in – garbage-out”. Uncertainty in the input data. Even when the data are good it should be reviewed carefully to confirm that it provides a good description of the actual facility. Typically single values of temperature, pressure, brine composition and velocity are used for facilities with a required life span of 20-30 years. It is certain that conditions will change over such time scales. Indeed changes often occur on a daily basis, e.g. the temperature of a pipeline may be much cooler during the night than during the day. Equally, the day-to-day operations may introduce variations, e.g. due to contractual demand, operations limitations, etc. This means that the input data is not in fact constant but is distributed over a range of values.

The uncertainty in the input data may not be a problem if it is only the worst-case conditions that are required. However, if the distribution of some of the inputs can be estimated a more accurate estimation of the conditions may be made. This can be done using a statistical program in conjunction with the spreadsheet. Cassandra allows this and this has been discussed in a previous paper9.

Providing that the accuracy of the input data has been confirmed then any remaining errors are due to the model itself. One attraction of Cassandra is that we are open about the equations used and how they are applied to determine the corrosion rate. Hence the practiced corrosion engineer has a better chance of recognising problems with its use should they arise. Experience in Using The Tool

What makes our model different from the de Waard equations1-4 is the inclusion of our experience and philosophy related to CO2 corrosion. This section highlights the major areas of difference from the published equations1-4 and other CO2 corrosion rate prediction tools. pH Model. The 1993 and 1995 de Waard3, 4 papers focus on wet gas situations and use a simple formula for the pH of pure water containing dissolved CO2 gas. The majority of oil and gas systems also produce formation water, the chemistry of which is much more complex than condensed water. The composition of the brine, especially the anions of other acids such as bicarbonate and acetate10, 14-19, can have a significant effect on the pH of it.

Since the value of the pH is a major input into the equations to determine the corrosion rate it is important that it be as accurate as possible. As a result our model uses a more sophisticated series of calculations for determining the pH of both pure water and brines with no restrictions on salinities or component concentrations. The pH model is based on freely available, well-documented code published by the US Geological Survey known as the PHREEQE model20. The modified code has been embedded


into the model and it has been validated against other pH models, literature sources and laboratory studies.

It should be noted that our model does not take into consideration the effect of solubility limits of the components of the brine. Consequently it allows solutions that are super-saturated to be entered and it calculates the pH based on this. That is, it does not allow precipitation of salts even above their solubility limits. For example, a brine containing calcium (Ca2+) and bicarbonate (HCO3

-) may, in practice, precipitate calcium bicarbonate when CO2 is added but Cassandra does not consider this. There are advantages and disadvantages to this approach and we constantly review our position on this matter. The use of Fugacity. The non-ideality of gases, including CO2, means that at high total pressures the partial pressure is not an accurate description of the activity (or “concentration”) of the gas component. The fugacity represents the true activity of the gas component. The 1991 and 1993 papers use the pressure of CO2 (pCO2) in the main equations and then, at the end, apply a fugacity correction factor (Ffug). In the model the pressure is converted to fugacity before being used in the calculations. Oil Wetting. In many ways this is similar to the phenomena of scaling. It is well known that some oil systems, which, in theory, should be corrosive, do not corrode due to the nature of the hydrocarbons produced. There can be several explanations for this that include the formation of a macro film of oil (or wax) which “wets” the metal surface and provides a physical barrier to water and / or the presence of natural inhibitors in the oil.

There have been several excellent studies that have demonstrated this effect and attempted to understand the exact mechanism11-13. Despite this the effect is not well understood and the ability to predict that the oil from a new reservoir will be protective is not available at this time. Note that all of the demonstrations of protectiveness have been retrospective. Consequently, our current approach is not to take any credit for this phenomenon and so our model does not include this effect. As further understanding becomes available this position may change. Effect of Scaling Temperature. CO2 corrosion leads to the formation of an iron carbonate (FeCO3) scale that has a complex structure. At low temperatures (<60°C) the scale can be described as semi-protective and partially reduces the overall corrosion rate from that expected based on the kinetics of the corrosion reactions. The exact mechanism of this is still not fully understood and remains the subject of research programs. The latest research suggests that the degree of protection is related to the density and uniformity of the scale. Under certain conditions it has been observed that as the temperature increases the scale becomes increasingly protective. It should be noted that, like many carbonate salts, the solubility of FeCO3 decreases with temperature. Thus as the temperature increase two antagonistic processes occur:

1. The kinetics of the corrosion reaction increase which increases the corrosion rate. 2. The protectiveness of the corrosion product scale increases which reduces the corrosion rate.

The temperature at which the protectiveness of the scale begins to reduce the overall corrosion rate is

known as the scaling temperature (Tscale). For many oil & gas applications Tscale is in the range of 70-90°C.

Whilst we recognise this advantageous effect there is a need to be cautious about taking full credit for it in the design phase of a project. This is because laboratory studies, which have shown high levels


of scale protectiveness, were usually undertaken in simulated wet gas conditions with no associated formation water production. Such studies use very simple brines based on condensed water and traces of sodium chloride (e.g.. 0.1% w/v NaCl). When formation water is produced much higher levels of chloride are present and this is known to destabilize the FeCO3 film and reduce its protectiveness. Other studies have suggested that acetate10 may also contribute to destabilization of the FeCO3 film.

Consequently, until this matter is fully resolved, a middle course has been chosen for design purposes. Thus at temperatures above the scaling temperature the corrosion rate is not allowed to increase and forms a plateau. This approach is shown in Figure 2.

Figure 2: The Possible Effects of High Temperature Scaling on the CO2 Corrosion Rate

We recognise that under certain conditions this approach may be overly conservative leading to

unnecessary costs (e.g. at temperatures >120°C). Clearly this is not an exact science and some experience of actual operating systems is required to make the best judgment. It is for this reason that our model has the flexibility to allow the user to compare both our and the de Waard approach Acetates. The current model allows the user to include the concentration of acetates. However, the only impact that this has is to modify the pH and typically it will increase the pH and so reduce the corrosion rate. The model does not incorporate our recent understanding of the effect of acetate to act as a buffer via the production of un-dissociated acetic acid. The un-dissociated acid is a proton donor which can increase the rate of corrosion, even though the pH increases10, 14-19. Currently this effect is included on a manual basis but will be built into the next version of the tool. Hydraulic Diameter and Liquid Velocity. Most aqueous corrosion reactions consist of two consecutive steps that together determine the overall corrosion rate resulting for a given set of conditions. CO2 corrosion is no exception involving a charge transfer step, that is activation controlled, occurring across the metal/solution interface and a mass transfer step involving transport of the corrosive species through the hydrodynamic boundary layer to the metal/solution interface. It is the latter that is affected by the

Temperature / C

Co

rro

sio

n R

ate

/ mm

/y

No Scale Effect Our Approach de Waard Approach

Tscale


flow regime prevailing and in particular the actual velocity of the aqueous phase (not the superficial velocity used to determine flow regime).

The corrosion rate equations developed by de Waard et al address the mass transfer contribution using Sherwood’s power law to compute the mass transfer coefficient and hence the mass transfer contribution to the corrosion rate as follows: Vmass = 0.023 x (D0.7/v0.5) x (U0.8/d0.2) x [H2CO3] ……………………… Equation (1) Where: D = diffusion coefficient for species – in this case H2CO3 v = dynamic viscosity of liquid. U = actual liquid velocity (m/s) d = hydraulic diameter (m) [ ] = concentration of species – in this case H2CO3. The concentration gradient of corrosive species within the boundary layer is assumed to be linear. For a pipe full of moving water the hydraulic diameter will be equal to the actual pipe diameter. However, for other flow regimes such as stratified flow there is a need to determine the equivalent hydraulic diameter that the liquid phase will occupy to enable the actual liquid velocity to be computed. The hydraulic diameter is simply defined as: d = 4 x A/S …………………………………………………………. Equation (2)

where A is the pipe cross-sectional area (pr2) and S the cross-sectional perimeter of the liquid in the pipe, Figure 3.

Figure 3. For a pipe partially filled with liquid the

cross-sectional perimeter, S, is the length of the black line in the second drawing.

Calculating S is not as straightforward as it might first seem and this is computed directly within the model using the liquid hold-up fraction (0 to 1) determined by the integral multiphase program. The cross sectional area of the liquid is calculated by multiplying the pipe cross-sectional area (pr2) by the liquid hold-up fraction.

Liquid

Gas

Liquid

GasCross-sectional perimeter length of the liquid region

Liquid

Gas

Liquid

Gas

Liquid

Gas

Liquid

Gas

Liquid

Gas

Liquid

GasCross-sectional perimeter length of the liquid region


A simplified method of determining the hydraulic diameter is to assume it is equal to the diameter of a circle that contains the cross sectional area of the liquid. This will overestimate the hydraulic diameter, by up to 10% for the condition where a line is 50% full of liquid. Validation

Clearly, the only value of a prediction model is if it can predict the corrosion rates for new situations. Corrosion engineers need to have confidence that the predictions are accurate enough for their needs and understand the errors that are involved. To achieve this level of confidence the models must be able to generate corrosion rates that match those of known systems. The simplest way to do this is to match the predicted rates to those generated in a laboratory where the conditions are carefully controlled. Of course these correlations tend to be very good since many models, including the one discussed here, are based on the results of laboratory experiments.

Actual operating facilities are more complex than laboratory simulations and so it is necessary to correlate with these as well. In theory this is simply done by measuring the corrosion rate in the field and comparing it to the predicted value. In practice this is often difficult because very few systems are operated without corrosion inhibitor. Even when this is done (often by mistake !) it can yield poor correlations for a number of reasons such as:

• The presence of corrosion inhibitors. • Oil wetting, scale or wax effects. • The action of non-CO2 mechanisms (O2, bacteria, galvanic). • Unknown flow regimes. • Unknown operations procedures (shut- ins, well testing).

Despite this our model has been tested against an increasing number of field conditions and

found to be sufficiently accurate. Generally, the predicted rate is observed to be the highest corrosion rate found – usually based on that of the deepest pit. Consequently, the tool is felt to give a “worst-case” corrosion rate and no further correction for pitting corrosion or other localized effects are used.

In addition to in-house testing every opportunity is taken to collaborate with other organizations to test our model. In recent years a detailed comparison of available models was undertaken as a joint research program at IFE21. In this program our model gave similar results to other well-known models (of course this doesn’t mean they are correct!!). Following on from this program, IFE are maintaining a database of known field conditions against which the members of the project can test their models. Ultimately, however, it is the inability to provide absolute validation of the currently available models that makes oil and gas companies reluctant to invest significantly in further model development. Industry Opportunities

Clearly our model and many other CO2 corrosion rate prediction programs meet the requirements of corrosion engineers for the majority of applications. At this time we intend to maintain our model but not invest in further refinement unless new factors (such as acetate) are identified or the industry finds a way to clearly validate models. Ideally, the authors believe that the oil and gas industry collectively and not individual companies should address further work. In particular two major areas for further work are suggested:


Consolidation of Models. Currently there are at least fifteen (15) CO2 corrosion prediction models available to corrosion engineers, several of which are based on the de Waard models. The authors believe that the deve lopment (proliferation) of new models is not required and effort should be focused on consolidating the existing ones Mechanistic Models. The majority of models currently available use mathematical equations derived from laboratory data. These are then corrected using different factors to match them to known operating conditions (e.g.. The de Waard based models). Today, there are numerous correction factors and it can be difficult to know which ones to apply and what value they should have.

Another class of models have been built on an understanding of the mechanism of the various corrosion reactions. These models are often referred to as “mechanistic” models and use the thermodynamic and kinetic equations of the reactions concerned to generate a corrosion rate. The authors believe that these models potentially provide the best way for predicting corrosion rates and that any further effort should be expended on them.

Whilst Cassandra is currently our preferred model we are not protective of it and would happily change to another if the industry could agree on some standardized models. We always remain open to collaboration in this area.

SUMMARY This paper has looked at the development of our company’s CO2 corrosion rate prediction model and highlighted some significant differences between it and other models. It has discussed the limits, errors and uncertainty associated with the model and the reader may well be left wondering if such models can really be believed. The authors hope that the final conclusion will be that such models can be trusted providing the user understand the limits and is clear on the errors involved.

The further development of such models has been addressed. On this question the authors have identified that our model is not perfect and that we would happily change to an alternative providing that clear benefits could be demonstrated. We believe that the future lies in the development of mechanistic models and that the industry collectively, not individually, should address this issue.

We look forward to increased cooperation and collaboration in this area.

ACKNOWLEDGEMENTS The authors would like to thank the following staff from our company for their contribution to the development of our model and this paper.

John Alkire Jim Corbally Laurence Cowie Mike Fielder Sandra Hernandez Ardjan Kopliku John Martin Will McDonald Drew McMahon Dominic Paisley Adam Petersen Phil Sugarman Tracy Smith Simon Webster Richard Woollam

In addition, we are indebted to our many colleagues in other oil and gas companies, research organizations and model development companies who have challenged, corrected and guided us as we have developed our model.


REFERENCES 1. C. de Waard and D.E. Milliams, “Carbonic Acid Corrosion of Steel”, Corrosion, 31 (1975) 177. 2. C. de Waard, U. Lotz and D.E. Milliams, “Predictive Model for CO2 Corrosion Engineering in Wet

Natural Gas Pipelines”, Corrosion, 47 (1991) 976. 3. C. de Waard and U. Lotz, “Prediction of CO2 Corrosion of Carbon Steel”, Paper 69, NACE

Corrosion 93, New Orleans, USA. 4. C. de Waard, U. Lotz and A. Dugstad, “Influence of Liquid Flow Velocity on CO2 corrosion: A

Semi-Empirical Model”, Paper 128, NACE Corrosion 95, Orlando, USA. 5. A.J. McMahon and D.M.E. Paisley, “Corrosion Prediction Modelling: A guide to the use of

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6. Bust of Cassandra by Max Klinger, 1857-1920. Hamburger Kunsthalle. © Maicar-Förlag-GML. From Carlos Parada, Greek Mythology Link, http://homepage.mac.com/cparada/GML/

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14. Y. Garsany, D. Pletcher* and B. Hedges, “The Role of Acetate in CO2 Corrosion of Carbon Steel: Has The Chemistry been Forgotten”, Paper 02273, NACE 2002, Denver, CO, USA, 7-12 April 2002.

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16. Y. Garsany, D. Pletcher and W.M. Hedges, “Speciation and Electrochemistry of Brines containing Acetate Ion and Carbon dioxide”, J. Electroanal. Chem., 538-9 (2002) 285-297.

17. Y. Garsany, D. Pletcher and W.M. Hedges, “Quantifying the Acetate Enhanced Corrosion of Carbon Steel in Oilfield Brines”, Corrosion, in press.

18. D. Pletcher, D. Sidorin and B. Hedges, “A Comparison of the Corrosion of Carbon and 13 % Chromium Steels in Oilfield Brines Containing Acetate”, Paper 05301, NACE 2003, Houston, TX, USA, 3-7 April 2005.

19. D. Pletcher, D. Sidorin and W.M. Hedges, “The Electrochemistry of 13 % Chromium Stainless Steel in Oilfield Brine”, Electrochim Acta, submitted.

20. D.L. Parkhurst, D.C. Thorstenson, and L.N. Plummer, PHREEQE - A computer program for geochemical calculations: U.S. Geological Survey Water-Resources Investigations Report 80-96, 195 p. 1980, (Revised and reprinted August, 1990.)

21. R. Nyborg, "Overview of CO2 Corrosion Models for Wells and Pipelines", Paper No. 02233, NACE 2002, Denver, CO, USA, 7-12 April 2002.


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