Evaluation of machine learning algorithms in predicting CO2 internal corrosion in oil and gas pipelines

Over recent years, a lot of money have been spent by the oil and gas industry to maintain pipeline integrity, specifically in handling CO2 internal corrosion. In fact, current solutions in pipeline corrosion maintenance are extremely costly to the companies. The empirical solutions also lack intelligence in adapting to different environment. In the absence of a suitable algorithm, the time taken to determine the corrosion occurrence is lengthy as a lot of testing is needed to choose the right solution. If the corrosion failed to be determined at an early stage, the pipes will burst leading to high catastrophe for the company in terms of costs and environmental effect. This creates a demand of utilizing machine learning in predicting corrosion occurrence. This paper discusses on the evaluation of machine learning algorithms in predicting CO2 internal corrosion rate. It is because there are still gaps on study on evaluating suitable machine learning algorithms for corrosion prediction. The selected algorithms for this paper are Artificial Neural Network, Support Vector Machine and Random Forest. As there is limited data available for corrosion studies, a synthetic data was generated. The synthetic dataset was generated via random Gaussian function and incorporated de Waard-Milliams model, an empirical determination model for CO2 internal corrosion. Based on the experiment conducted, Artificial Neural Network shows a more robust result in comparison to the other algorithms. © Springer Nature Switzerland AG. 2019.