Advanced Predictive Modelling of Crude Oil Corrosivity: Insights from Modified McConomy Predictions and Experimental Data

Abstract Predicting high temperature crude oil corrosivity is of critical importance and a particularly difficult aspect of refinery operations. Corrosivity is one of many factors that are often incorporated into the purchase prices for the refinery feed stocks as well as a necessary input when assessing the serviceability of materials of construction in refinery process equipment. Many organizations have proprietary prediction methodology for handling crude corrosivity, which are often based on McConomy predictions and modified to account for previous refining experience. In addition, the methodology is usually for use as a risk assessment and risk-based inspection tool and not for materials selection or corrosion rate prediction. This paper focuses on investigating the comparison of modified McConomy predictions, American Petroleum Institute (API) Risk Based Inspection (RBI) and experimental data that were derived from an extensive Joint Industry Program (JIP) conducted to quantify the influence of crude oil chemistry on naphthenic acid corrosion and sulfidic corrosion, or a combination of both. Through meticulous corrosion evaluations performed in closely simulated relevant refinery conditions, the study serves as a valuable tool for assessing the influence of temperature, wall shear stress (WSS), and active sulfur content on corrosion rates. It also provides a prediction model to characterize the active sulfur level as a function of sulfur compound concentration and temperature, which is further utilized to predict corrosion rates of multiple alloys.