Application of Corrosion Prediction Software to Optimize Material Selection in Offshore Pipelines and Tubing - Ali, Abd Azim Aizat Bin (Universiti Teknologi PETRONAS, 2009)
Corrosion prediction in upstream oil & gas industry is a very important process. Many prediction models have been developed for the Exploration & Production (E&P) business, varying from the empirical to mechanistic models. The fact that data are very limited in the design stage compared to operational stage. The uses of default values are common in design stage by which the design of the tubing and pipelines are decided. The question whether the utilization of default values is accurate can be checked with the modeling of operational data. This project will assess the corrosion predictions from two CO2 prediction models mainly the ECE4 and MULTICORP. Comparison on the corrosion rates predicted and other driving features will be analyzed for a set of cases taken from corrosion field database. Evaluation of the models can strongly depends on the selection of field data used and the accuracy of the field data. The task is to perform modeling of field data by comparing the predictions on the design and operation stage of a project by using same field data. The accuracy of ECE4 predictions for the design stage is higher by more than 200% compared to the operation stage predictions. However, MULTICORP did come up with predictions that are within 30% difference of the design and operation stage. CO2 partial pressures, H2S, acetic acid, carbonate content, flow type and flow velocity are the crucial parameters that can highly stimulate corrosion process to occur. Therefore, it is essential that the user have the ability to accurately predict the default values if the data are not available. With less data available, ECE4 can provide satisfactory predictions. MULTICORP would be a better model for higher accuracy predictions if more data were available. Thus, regardless the amount of data available, it is crucial to understand the uncertainties and limitations of the corrosion prediction models and how the input could affect the corrosion prediction.