Investigation of physical parameters of crude oils and their impact on kinematic viscosity of vacuum residue and heavy product blends for crude oil selection

The aim of the present study is to predict kinematic viscosity (KV) of vacuum residue (VR) and refinery heavy product blends based on physical parameters of the crude oil at a predetermined temperature. The impact of various physical parameters of crude oils on KV-VR is investigated in detail here. Based on this, the physical parameters of the crude oil include at least one of VR yield and Conradson Carbon Residue (CCR) content. The first correlation models have been developed by coefficients obtained by regression analysis between the physical parameters of crude oil and KV-VR of the crude oil at different temperatures viz. 50 0 C, 100 0 C and 135 0 C. The second correlation model is developed for optimizing an amount of cutter stock (diluents) for producing refinery heavy product blends. The second correlation model is compared with previously published viscosity blending correlations (Refutas index method, Chevron, Chirinos) for 54 blend samples. The second correlation model showed good agreement with experiments and Chevron and Chirinos correlations. The third correlation model is used to predict KV-VR of crude oil at a first predetermined temperature from the KV-VR of the crude oil at a second predetermined temperature. The models have been implemented for Kuwait crude oil selection and the same has been illustrated as case study. Based on the present work, a quick method has been proposed for ranking and selection of oils. ? 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

The study aims to predict kinematic viscosity of vacuum residue and refinery heavy product blends based on physical parameters of crude oil at a predetermined temperature. Correlation models were developed and compared with experimental results, showing good agreement.