Challenges in Petroleum Characterization—A Review

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

Challenges in Petroleum Characterization—A Review

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

Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks

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

Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks

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