A study on the behavior of CO2 corrosion on pipeline using computational fluid dynamics, experimental and artificial neural network approach

Abstract

Corrosion of the piping system is a genuine problem in the oil and gas industry. Most oil and gas industries used a carbon steel pipeline for the transportation of crude oil, which is affected by CO2 corrosion. Now a day, the computational approach and artificial neural network approach will be used to study the corrosion rate. Therefore, in this work, Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) studies on piping systems were made to determine the corrosion rate induced by CO2 saturated aqueous solutions on carbon steel pipeline. In CFD study, corrosion rates were computed by modeling the electrochemical processes occurring at the metal substrate from cathodic reductions of the carbonic acid and hydrogen ions, and the anodic oxidation of the metal component. Also, an artificial neural network study was made using a multilayer perceptron neural network method; and, computational fluid dynamics and artificial neural network simulations were validated with in-house built experiment set-up. The experimental study had been carried out for more than 200-h to find the corrosion rate on the pipeline, and satisfactory trends were observed between computational fluid dynamics, artificial neural network, and experimental values. In the end, corroded pipes were observed under a scanning electron microscope and x-ray spectroscopy, and the corroded zones were viewed as against the non-corroded pipe.