The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee–Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee–Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear regression and artificial neural network (ANN), were employed to model the molecular weight of the 430 petroleum fluid samples. It was found that the ANN model demonstrated the best accuracy of prediction with a relative standard error (RSE) of 7.2%, followed by the newly developed nonlinear regression correlation with an RSE of 10.9%. The best available molecular weight correlations in the literature were those of API (RSE = 12.4%), Goosens (RSE = 13.9%); and Riazi and Daubert (RSE = 15.2%). The well known molecular weight correlations of Lee–Kesler, and Twu, for the data set of 430 data points, exhibited RSEs of 26.5, and 30.3% respectively.
The intercriteria analysis developed on the base of intuitionistic fuzziness and index matrices was applied to evaluate processing data of the LUKOIL Neftohim Burgas H-Oil ebullated bed vacuum residue hydrocracker with the aim of revealing the reasons for increased fouling registered during the 3rd cycle of the H-Oil hydrocracker. It was found that when the ratio of the Δ T of the 1st reactor to the Δ T of the 2nd reactor gets lower than 2.0, an excessive H-Oil equipment fouling occurs. The fouling was also found to be favored by processing of lower Conradson carbon content vacuum residual oils and increased throughput and depressed by increasing the dosage of the HCAT nanodispersed catalyst. The fouling in the atmospheric tower bottom section is facilitated by a lower aromatic content in the atmospheric tower bottom product. The addition of FCC slurry oil not only increases aromatic content but also dissolves some of the asphaltenes in the atmospheric residual hydrocracked oil and decreases its colloidal instability index. The fouling in the vacuum tower bottom section is facilitated by a higher saturate content in the VTB. Surprisingly, it was found that the asphaltene content in the VTB depresses the fouling rate. No relation was found of the sediment content in the hydrocracked residual oils measured by hot filtration tests and by the centrifuge method to the equipment fouling of the H-Oil hydrocracker.
Inter-criteria analysis was employed in VGO samples having a saturate content between 0.8 and 93.1 wt.% to define the statistically significant relations between physicochemical properties, empirical structural models and vacuum gas oil compositional information. The use of a logistic function and employment of a non-linear least squares method along with the aromatic ring index allowed for our newly developed correlation to accurately predict the saturate content of VGOs. The empirical models developed in this study can be used not only for obtaining the valuable structural information necessary to predict the behavior of VGOs in the conversion processes but can also be utilized to detect incorrectly performed SARA analyses. This work confirms the possibility of predicting the contents of VGO compounds from physicochemical properties and empirical models.
The production of heavy fuel oil from hydrocracked vacuum residue requires dilution of the residue with cutter stocks to reduce viscosity. The hydrocracked residue obtained from different vacuum residue blends originating from diverse crude oils may have divergent properties and interact with the variant cutter stocks in a dissimilar way leading to changeable values of density, sediment content, and viscosity of the obtained fuel oil. H-Oil hydrocracked vacuum residues (VTBs) obtained from different crude blends (Urals, Siberian Light (LSCO), and Basrah Heavy) were diluted with the high aromatic fluid catalytic cracking (FCC) light cycle, heavy cycle, and slurry oil, and the low aromatic fluid catalytic cracking feed hydrotreater diesel cutter stocks and their densities, sediment content, and viscosity of the mixtures were investigated. Intercriteria analysis evaluation of the data generated in this study was performed. It was found that the densities of the blends H-Oil VTB/cutter stocks deviate from the regular solution behavior because of the presence of attractive and repulsive forces between the molecules of the H-Oil VTB and the cutter stocks. Urals and Basrah Heavy crude oils were found to enhance the attractive forces, while the LSCO increases the repulsive forces between the molecules of H-Oil VTBs and those of the FCC gas oils. The viscosity of the H-Oil VTB obtained during hydrocracking of straight run vacuum residue blend was established to linearly depend on the viscosity of the H-Oil vacuum residue feed blend. The applied equations to predict viscosity of blends containing straight run and hydrocracked vacuum residues and cutter stocks proved their good prediction ability with an average relative absolute deviation (%AAD) of 8.8%. While the viscosity was found possible to predict, the sediment content of the blends H-Oil VTBs/cutter stocks was recalcitrant to forecast.
252 literature sources and about 5000 crude oil assays were reviewed in this work. The review has shown that the petroleum characterization can be classified in three categories: crude oil assay; SARA characterization; and molecular characterization. It was found that the range of petroleum property variation is so wide that the same crude oil property cannot be measured by the use of a single standard method. To the best of our knowledge for the first time the application of the additive rule to predict crude oil asphaltene content from that of the vacuum residue multiplied by the vacuum residue TBP yield was examined. It was also discovered that a strong linear relation between the contents of C5-, and C7-asphaltenes in crude oil and derived thereof vacuum residue fraction exists. The six parameter Weibull extreme function showed to best fit the TBP data of all crude oil types, allowing construction of a correct TBP curve and detection of measurement errors. A new SARA reconstitution approach is proposed to overcome the poor SARA analysis mass balance when crude oils with lower density are analyzed. The use of a chemometric approach with combination of spectroscopic data was found very helpful in extracting information about the composition of complex petroleum matrices consisting of a large number of components.
This paper evaluates the influence of crude oil (vacuum residue) properties, the processing of fluid catalytic cracking slurry oil, and recycle of hydrocracked vacuum residue diluted with fluid catalytic cracking heavy cycle oil, and the operating conditions of the H-Oil vacuum residue hydrocracking on the quality of the H-Oil liquid products. 36 cases of operation of a commercial H-Oil® ebullated bed hydrocracker were studied at different feed composition, and different operating conditions. Intercriteria analysis was employed to define the statistically meaningful relations between 135 parameters including operating conditions, feed and products characteristics. Correlations and regression equations which related the H-Oil® mixed feed quality and the operating conditions (reaction temperature, and reaction time (throughput)) to the liquid H-Oil® products quality were developed. The developed equations can be used to find the optimal performance of the whole refinery considering that the H-Oil liquid products are part of the feed for the units: fluid catalytic cracking, hydrotreating, road pavement bitumen, and blending.
Model compounds were used to provide some chemical boundaries for the eight-fraction SAR-ADTM characterization method for heavy oils. It was found that the Saturates fraction consists of linear and highly cyclic alkanes; the Aro-1 fraction consists of molecules with a single aromatic ring; the Aro-2 fraction consists of mostly 2 and 3-ring fused aromatic molecules, the pericondensed 4-ring molecule pyrene, and molecules with 3–5 rings that are not fused; and the Aro-3 fraction consists of 4-membered linear and catacondensed aromatics, larger pericondensed aromatics, and large polycyclic aromatic hydrocarbons. The Resins fraction consists of mostly fused aromatic ring systems containing polar functional groups and metallated polar vanadium oxide porphyrin compounds, and the Asphaltene fraction consists of both island- and archipelago-type structures with a broad range of molecular weight variation, aromaticity, and heteroatom contents. The behavior of the eight SAR-ADTM fractions during hydrocracking and pyrolysis was investigated, and quantitative relations were established. Intercriteria analysis and evaluation of SAR-ADTM data of hydrocracked vacuum residue and sediment formation rate in commercial ebullated bed vacuum residue hydrocracking were performed. It showed that total asphaltene content, toluene-soluble asphaltenes, and colloidal instability index contribute to sediment formation, while Resins and Cyclohexane-soluble asphaltenes had no statistically meaningful relation to sediment formation for the studied range of operation conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.