Use of Artificial Neural Networks for Predicting Crude Oil Effect on Carbon Dioxide Corrosion of Carbon Steels

The role of crude oil on carbon dioxide (CO2) corrosion has gained special attention in the last few years because of its signifi cance when predicting corrosion rates. However, the complexity and variability of crude oils makes it hard to model its effects, which can infl uence not only wettability properties but also the corrosiveness of the associated brine. This study evaluates the usefulness of artifi cial neural networks (ANN) to predict the corrosion inhibition offered by crude oils as a function of several of their properties that have been related in previous studies to the protectiveness of crude oils, i.e., nitrogen and sulfur contents, resins and asphaltenes, total acid number, nickel and vanadium content, etc. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany