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Abstract

This paper describes an integrated approach, based on corrosion modeling and laboratory testing, to optimize the use of carbon steel in corrosive service for applications such as downhole tubulars, pipelines, and facilities. This approach presents economic advantages, such as reducing the use of expensive corrosion resistant alloys, while ensuring the operational integrity of equipment and facilities. A key part of this integrated approach is to apply reliable corrosion models underpinned with laboratory data. To be most effective, the models should account for the relevant chemistry and physics of the corrosion process, including the effects of detailed water chemistry, liquid hydrocarbons, and the degree of protection from iron carbonate or iron sulfide scales. Ideally, models should account for variations in conditions and flow characteristics along the length of a wellbore or pipeline. Case studies are presented that demonstrate how corrosion modeling in conjunction with laboratory testing may be used to the selection of validate carbon steel for challenging applications.

Introduction

Although high cost corrosion resistance alloys (CRAs) were developed to resist internal corrosion, carbon steel is still the most cost effective material used in oil and gas production. It is very important to develop an integrated corrosion prediction approach for optimizing the use of carbon steel in corrosive service while ensuring the operational integrity of equipment and facilities. Both corrosion models and laboratory testing are frequently used in this industry to make lifetime predictions of facilities using carbon steel and further to make decisions on materials selection. Corrosion models, including empirical, semi-empirical, and mechanistic ones, have been developed over the past several decades to predict corrosion of carbon steel1-6. These corrosion models can provide engineers quick and economical corrosion predictions. Most of the models were validated by laboratory data and/or field data. Empirical and semi-empirical models usually provide reasonable predictions inside of their validation range but poor predictions outside of their range. Mechanistic models generally can extrapolate to conditions outside of their validation range and remain accurate to a certain degree. Consequently, one should always understand the validation range and limitations of the models to apply these correctly. Moreover, although part of corrosion mechanisms are well understood in lab investigations, due to the complexity in production operations, it is still challenging to apply lab short-term testing results and corrosion models to predict corrosion of facilities for twenty to thirty years of service. An integrated approach was therefore developed by ExxonMobil1 to apply a reliable corrosion model in conjunction with laboratory testing for predicting corrosion in oil and gas production. 1 ExxonMobil herein refers to capabilities within ExxonMobil Development Co., ExxonMobil Upstream Reseach Co. and ExxonMobil Production Co.

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Corrosion Modeling Within an Integrated Corrosion P rediction Approach Wei Sun, Kevin Geurts, Dylan Pugh, ExxonMobil Upstream Research Company Jorge Pacheco, Craig Monahan, ExxonMobil Development Company Robert Franco, David Norman, ExxonMobil Production Company

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This paper describes ExxonMobil's corrosion prediction model and the application of this model as part of an integrated corrosion prediction approach.

Model Overview

ExxonMobil's corrosion prediction model is one of the key components of ExxonMobil's integrated approach for corrosion prediction. This integrated approach enables the optimal use of carbon steel in corrosive service for downhole, pipeline, and facilities applications. This integrated approach is increasingly important, due to increasing production from corrosive resources, such as highly sour gas fields, and the need to effectively manage the integrity of existing resources. The software implementation of ExxonMobil's corrosion prediction model, CorrCast, provides a window based, user-friendly tool for performing corrosion rate predictions in sweet (CO2) and sour (H2S) services. Furthermore, CorrCast serves as a platform for delivering new corrosion prediction capabilities, such as wet gas top-of-line corrosion prediction, to ExxonMobil operating companies. Model Description

CorrCast is based on a mechanistic corrosion model that accounts for the relevant chemistry and physics of the corrosion process in a mixed aqueous-liquid hydrocarbon environment, which is depicted schematically in Figure 1. The model has a number of advanced capabilities including:

• Predicting corrosion in sweet (CO2) and sour (CO2 + H2S) conditions • Predicting formation and protectiveness of corrosion scales (FeCO3 or FeS) • Accounting for detailed water chemistry, including salts and organic acids • Accounting for liquid hydrocarbon effects • Predicting changes in corrosion over the length of a pipeline / flowline • Predicting corrosion in a simulated autoclave test • Accounting for corrosion inhibitor efficiency

The model has been validated against a wide range of field and laboratory data.

Figure 1. Schematic depicting the features of the c orrosion model CorrCast predicts corrosion rates by modeling the chemistry and physics of the corrosion process, including CO2 and H2S solubility in the aqueous phase, solution equilibrium reactions, electrochemical reactions, mass transfer to and from the steel surface, scale formation, and inhibition. The calculation methodology is illustrated in Figure 2. The model requires users to input test / field conditions, gas composition, water analysis, flow parameters, and hydrocarbon properties. Water chemistry and electrochemistry models are then applied to calculate an unscaled corrosion rate. The effects of iron carbonate and iron sulfide scales are modeled by first calculating scale protectiveness factors, which are then applied to the unscaled corrosion rate. The inhibitive or antagonistic effects of liquid hydrocarbons are also modeled and incorporated as a multiplicative factor, similar to the approach taken

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for an injected corrosion inhibitor. A sensitivity tool is included in the software implementation to test the variation of results with changes to selected input parameters. Corrosion rates can be predicted in CorrCast at selected points in pipes for field application or for a metal coupon in a test condition-controlled vessel for laboratory application. Rates can also be predicted as a function of length along a pipeline for field application, and as a function of time for a metal coupon in an autoclave for laboratory application. A detailed description of the model and calculation methodology is described below.

Figure 2. Schematic depicting the calculation metho dology of CorrCast

Water chemistry The water chemistry model requires test/field conditions (temperature, pressure), gas composition (%H2S / PH2S, and %CO2 / PCO2), and water analysis (salt concentrations, organic acids' concentrations, bicarbonate concentration) as input parameters. Gas solubility is modeled using Henry's Law for CO2 and H2S. Fugacity coefficients are estimated for CO2 and H2S to adjust for real gas pressure using the Peng-Robinson equation of state. The water chemistry model includes equilibrium reactions for: carbonic acid dissociation, hydrogen sulfide dissociation, weak acid (HA) dissociation, and water dissociation. Salinity is represented by the ionic strength. Equilibrium constants are taken from the literature and vary with temperature and salinity. pH and all the species’ concentrations are computed from the water chemistry model and used as inputs for the electrochemistry model which calculates the unscaled corrosion rate. Electrochemistry model CorrCast models the corrosion current by assuming direct attack by H+, H2CO3 HCO3

-, and HA at the surface of the metal. The electrochemical reactions include the cathodic and anodic reactions listed below:

2H2CO3 + 2e- = H2 + 2HCO3- (1)

2HCO3- + 2e- = H2 +2CO3

2- (2) 2H+ + 2e- = H2 (3) 2HA+2e- = H2 + 2A- (4) Fe = Fe2+ + 2e- (5)

The corrosion current is equal to the anodic current which is equal to the sum of the cathodic currents:

HAHCOCOHHFecorr iiiiii +++== −++332

2 (6)

Conditions (temperature, pressure)

Electrochemistry model

(unscaled rate)

Scaled corrosion rate

Flow parameters (pipe geometry,

velocity) Mass transfer effects

Hydrocarbon properties

(water cut, inhibitive)

Scale effects (scale type and protectiveness)

Hydrocarbon effects

Input

Calculated value

Iterative calculation (pipe evolution and autoclave)

Water analysis (salts, organic acids)

Gas composition (pCO2, pH2S)

Water chemistry model

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The unscaled corrosion rate is therefore calculated using the following equation.

corrFe

Fecorr i.nFMi

CR 1551==ρ

(7)

where corri is in A/m2,

FeM is the molecular weight of iron in g/mol, Feρ is the density of iron in kg/m3, n is number

of moles of electrons used in reducing or oxidizing a given species, F is the Faraday’s constant, and CR is in mm/s, which can be conveniently converted to mm/y. When H2S is present in the water solution, it is assumed that the adsorption of sulphur on iron affects the potential. This effect varies with coverage and is fit by a Langmuir isotherm. Mass transfer effect Mass transfer allows the movement of solution species from the bulk fluid to a near-surface region, and vice versa. Mass transfer in the model is described via Fick's First Law. Mass transfer "in" is equal to mass transfer "out", making allowances for net flow. Hydrodynamic models are applied to calculate shear stresses and mass transfer coefficients for both pipe and autoclave simulations. There are no local concentration surpluses. Electric field effects are assumed to be dominated by hydrodynamics. Adsorption and desorption are neglected as a conservative assumption. Scaling methodology In CO2 and CO2/H2S corrosion environments, corrosion products such as iron carbonate (FeCO3) and iron sulfide (FexS), can form and decrease the corrosion rate of carbon steel. Scale formation is therefore one of the key factors affecting corrosion rate. CorrCast applies scaling factors for quantifying the effects of scale on corrosion at the conditions of interest.

FeSorFeCOunscaledscaled SFCRCR3

×= (8)

where

scaledCR is the scaled corrosion rate, unscaledCR is the unscaled corrosion rate, and FeSorFeCOSF

3 is the

scaling factor for FeCO3 or FeS, which is a function of temperature, pH, and local supersaturation. Hydrocarbon liquids CorrCast includes the effects of hydrocarbon liquids (crude oil or condensate) on corrosion in two ways. First, it uses a viscosity based drainage model to estimate how much the liquid hydrocarbon contacts the metal surface versus the aqueous phase. This is a function of the water cut and the relative viscosities of the water and liquid hydrocarbon phases. If no data exists on inhibitive or corrosive properties of the liquid hydrocarbon, it is assumed to be corrosion neutral (simply reduces corrosion by displacing the aqueous phase). Second, it can account for inhibitive or corrosive properties of the liquid hydrocarbon using the empirical equation below

+−×=

HW

b

aCRCR actual 1 (9)

where a and b are constants, and W and H are water and hydrocarbon volumes respectively. This equation can account for species in the liquid hydrocarbon that partition into the water that further inhibit or accelerate corrosion. The empirical constants a and b can only be obtained for a particular hydrocarbon from experimental data. Therefore, laboratory tests are required to establish how field hydrocarbons, if present, may affect the corrosion rate.

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Validation Range Validation ranges of key parameters in CorrCast are given below:

• Temperature: 39-316°F (4-158°C) • CO2 partial pressure: up to 648 psia (44 atm) • H2S partial pressure: up to 100 psia (6.8 atm) • Equivalent salt concentration: up to 32 wt% • Water cut: 3-100%

Obtaining Flow Parameters from a Flow Simulator The current version of CorrCast can import hydraulic results from a flow simulator, such as OLGA, PipePhase, or Prosper. Predicted values for fluid velocity, temperature, pressure, gas composition, water cut, viscosity, flow regime, and liquid holdup along the pipe or tubing are imported. CorrCast predicts the corrosion rate beginning at the entrance to the pipe, and proceeding to the exit, taking account along the way of changes in the fluid properties and hydraulic variables, as well as changes in iron concentration, pH, and scale formation.

Laboratory Testing Methodology

Corrosion models are not ordinarily used alone to determine corrosion rates, especially if the conditions are near the validation and/or validity limits of the model.7 Instead, confirmatory laboratory corrosion tests frequently accompany or follow the modeling. Specialized laboratory test apparatuses, such as large-volume high-pressure, high-temperature autoclave test cells and/or a large-diameter sour multiphase flow loop, are often necessary to ensure proper replication of field conditions in the laboratory. In the course of corrosion research, the ExxonMobil Materials and Corrosion Laboratory (M&CL) has been designed and constructed specifically for this purpose. The M&CL includes a state-of-the art building control system to ensure optimal safety and environmental performance, while also supporting the experimental facilities.

Figure 3 shows a photograph of a corrosion autoclave in the M&CL and includes a schematic depiction of the autoclave internals. These autoclaves can deliver shear stresses to the coupons up to 90 Pa at temperatures as high as 230oC (450oF) and a total pressure up to 34.5 MPa (5,000 psi).

Gas

Liquid

Thermocouple

Baffles forvortex

prevention

Impeller forshear stressproductionTest Material

Impeller forwater/hydrocarbon

mixing

Figure 3: Autoclaves at ExxonMobil's Materials and Corrosion Laboratory are specially designed to replicate field conditions, including fluid chemist ry and mass transfer A large-diameter, multiphase flow loop can simulate flow regimes that are not attainable in a stirred autoclave, primarily slug flow and flow with large upsets. Figure 4 is a photograph of the 102-mm (4-in) diameter multiphase, Hastelloy C276 sour flow loop at the M&CL. This 30-m (100-ft) long flow loop pumps liquid and gas separately

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and provides the ability to replicate all production flow regimes at inclinations between 0o and 90o. The maximum gas and liquid velocities are ~9 m/s (30 f/s) and ~4 m/s (13 f/s) respectively. Liquid temperatures can be varied from 5oC (40oF) to 120oC (250oF) with a maximum total pressure of 6.9 MPa (1,000 psi) and a maximum H2S partial pressure of 0.2 MPa (30 psi). Both coupons and electrochemical probes can be used in the flow loop, which provides the ability to remove coupons under pressure. Results can be extrapolated to other pipe sizes by ensuring equivalent mass transfer between the flow loop and the service line based on the aforementioned mechanistic relations.

Figure 4: 102-mm (4-in.) multiphase sour flow loop used for corrosion testing at the materials and corrosion laboratory

Case Studies Case 1

In this example, carbon steel was evaluated to determine its applicability for tubing in a new well at a mature field. The well would initially be used as an appraisal well, producing oil for approximately three months, and it would then be switched to a water injector for the remainder of its life. At this field, 13 Cr is used for tubing in production wells to mitigate CO2 corrosion and carbon steel is used for tubing in water injection wells. If carbon steel was not feasible in this case, 25 Cr Super Duplex would be required since 13 Cr is susceptible to pitting in water injection service where there may be upsets in oxygen control. Reservoir and subsurface engineers provided the design conditions during the initial production phase, including hydrocarbon liquid, hydrocarbon gas, and formation water analyses for the reservoir (key component bicarbonate 160 ppm), expected range of temperatures (55oC to 85oC) and pressures along the wellbore (PCO2 < 58 kPa, no H2S), and estimates of the flow velocities (~1 m/s) and water cut (~10%). Corrosion modeling was performed with a widely-used empirical model and CorrCast based on the expected upper bound of CO2, the supplied formation water chemistry, and the range of temperatures expected along the wellbore. The empirical model predicted general corrosion rates up to 6.9 mm/y (270 mpy). This model did not, however, account for any beneficial effects from the formation of an iron carbonate scale or from the oil. This prediction indicates that up to 1.7 mm of wall loss could occur during the three month production period, which corresponds to more than 10% of the tubing thickness. In contrast, CorrCast, which includes the effect of a protective iron carbonate scale, predicted general corrosion rates up to 0.5 mm/y (20 mpy) for 100% water cut. Corresponding unscaled corrosion rate predictions were up to 8.0 mm/y (315 mpy), which agrees well with the empirical model, but such high corrosion rates are unlikely because conditions should be favorable for the formation of an iron carbonate scale. With the addition of oil (10% water cut), CorrCast predicted general corrosion rates of <0.1 mm/y (<4 mpy). These predictions indicate that there will be less than 0.2 mm of wall loss during the three month production period and that the prediction from the empirical model represents a conservative upper bound. This example demonstrates how the advanced capabilities of the model enabled it to reduce the conservatism built into the empirical model and provide a more realistic corrosion prediction. Based on this analysis, carbon steel

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was used for the tubing in this well. Since the model was inside its validation range no laboratory testing was conducted. Case 2 In this example, inhibited carbon steel was evaluated for a wet gas flowline in highly corrosive service. The flowline would transport full wellstream fluids with a CO2 partial pressure of 1,800 kPa at a temperature of ~30 oC. The reservoir is predicted to sour over time, giving rise to small amounts of H2S (up to ~0.7 kPa). Reservoir and facilities engineers provided detailed conditions including gas and water analyses, flow rates, and temperature. Corrosion modeling was conducted with CorrCast to confirm the need for inhibition and to compare against uninhibited corrosion tests conducted at M&CL during the corrosion inhibitor qualification program. For the initial conditions (sweet), CorrCast predicted general corrosion rates of 1.6-3.8 mm/y (64-155 mpy), which confirmed the need for inhibition. A range of corrosion rates was reported because a poorly protective iron carbonate scale was predicted to form (1.6 mm/y corresponds to the scaled rate, 3.8 mm/y corresponds to the unscaled rate). For the end of life conditions (sour), CorrCast predicted a general corrosion rate of 0.2 mm/y (8 mpy). The reduction in corrosion rate versus the initial conditions was due to the formation of a protective iron sulfide scale. CorrCast predictions agreed well with uninhibited corrosion tests conducted on X52 and X65 pipeline steels. For the initial conditions (sweet), the measured corrosion rates of the X52 and X65 coupons were 3.9 mm/y (154 mpy) and 1.4 mm/y (54 mpy), respectively. These rates agree well with the range predicted by CorrCast, and the measured difference in rates between the two steels was attributed to differences in microstructure affecting the formation of the iron carbonate scale. For the end of life conditions (sour), the measured corrosion rates of both steels was 0.2 mm/y, which agrees with the CorrCast prediction. Additional corrosion tests were conducted with inhibitor, and the measured corrosion rates were 0.01 mm/y (0.4 mpy), which confirmed the inhibitor would mitigate corrosion at the dosage tested. This example demonstrates how an integrated approach of modeling and testing supported the use of inhibited carbon steel for a challenging application. It also shows the ability of the model to predict the impact of corrosion scales in both sweet and sour conditions. Based on the modeling and corrosion inhibitor qualification testing, inhibited carbon steel was selected for the flowline.

Conclusions

This paper provides an overview of ExxonMobil's corrosion prediction model and its implementation in the CorrCast software. It describes how to apply the model in conjunction with laboratory testing to evaluate the use of carbon steel in oil and gas production environments. An integrated approach to corrosion modeling and laboratory testing is important to ensure long-term reliability of carbon steel production equipment with minimal life-cycle cost.

Acknowledgement

The authors would like to acknowledge R. V. Reddy from ExxonMobil Upstream Research Company, and J. C. Bondos and J. L. Nelson from ExxonMobil Production Company for early-stage corrosion model development.

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NACE International, 1993.

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