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.


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 steel 1–6. 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.

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