A novel crude oil classification approach based on average carbon number determination and case studies

Traditional crude oil classification based on American Petroleum Institute gravity (API) and saturates, aromatics, resins, and asphaltenes (SARA) content variation would encounter serious limitation during the heavy oil development. In this study, the average carbon number (ACN) is proposed for the first time as a quantitative parameter to establish a novel and efficient method for crude oil classification, which shows a closer correlation to the viscosity variation of crude oil in the Tahe oilfield. The ACN is determined according to the total mole amount of CO x (CO + CO₂) emissions during the ramped temperature oxidation (RTO) process, where a constant heating rate is used in conducting the kinetic cell (KC) experiments. The results show that the most effective heavy oil dilution process occurs when the ACN difference lies within the range of 6–14 under the investigated conditions. Pilot tests conducted in the Tahe oilfield confirm that the regular of screening light hydrocarbon with maintaining this range significantly enhances downhole dilution efficiency while reducing diluent consumption. ACN obtained from KC experiment is a more accurate and promising method for determining the carbon number, especially for heavy oil. This classification method could be used as a quantitative and effective solution for optimizing viscosity prediction and diluent selection in the heavy oil up and downstream industry. • A quantitative method for determining average carbon number value was developed via kinetic cell experiments. • Crude oil was quantitatively characterized by the average carbon number value, showing a stronger correlation with viscosity than SARA or API index. • The kinetic cell method is more accurate than the Gas chromatography method in determining average carbon number for heavy oils.