Modelling CO2 corrosion of pipeline steels

Abstract

Over the years, several attempts have been made by various research institutions and petroleum companies to develop models for the prediction of CO2 corrosion in pipelines, in order to better capture the underlying principles that cause it. Modelling CO2 corrosion is important to the oil and gas and carbon capture and storage (CCS) industries, as it provides the means by which the prevention of the financial costs from lost production, the preservation of the environment as well as the health and safety of human lives can be achieved. In this thesis, existing models have been investigated and compared against newly derived models in terms of their accuracy of prediction, by using an identical test dataset. A neural network (NN) model was developed, in which a detailed sensitivity analysis was carried out on Matlab training functions to determine their degree of suitability in CO2 corrosion prediction. Results showed that the tansig transfer function was the most suitable and that a 2-layer network was sufficient to obtain desirable R2-values of ~0.9 for both low and high pressure CO2 corrosion data. Also, a linear regression model was developed based on predictor variables: temperature (T), CO2 partial pressure (𝑃𝐶𝑂2), fluid velocity (U) and pH, for both low and high pressure CO2 data. The respective R2-values obtained are 0.65 and 0.7. An R2-value of 0.8 can be achieved for the low pressure CO2 data; however the derived regression equation is inelegant and contains a combination of a large number of predictor terms. From Monte Carlo analyses, the exponential and normal distributions were discovered to be the best fits for the low and high pressure CO2 corrosion rate data, respectively. Further, parametric sensitivity analyses revealed the pH and fluid velocity to be the least and most significant variables for low pressure CO2, respectively, while the velocity and temperature were the least and most significant variables for high pressure CO2 corrosion, respectively.