Browsing by Author "Palichev G.N."

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks
    (2023-02-01) Stratiev D.; Sotirov S.; Sotirova E.; Nenov S.; Dinkov R.; Shishkova I.; Kolev I.V.; Yordanov D.; Vasilev S.; Atanassov K.; Simeonov S.; Palichev G.N.
    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.
  • No Thumbnail Available
    Item
    Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN
    (2023-08-01) Palichev G.N.; Stratiev D.; Sotirov S.; Sotirova E.; Nenov S.; Shishkova I.; Dinkov R.; Atanassov K.; Ribagin S.; Stratiev D.D.; Pilev D.; Yordanov D.
    The refractive index is an important physical property that is used to estimate the structural characteristics, thermodynamic, and transport properties of petroleum fluids, and to determine the onset of asphaltene flocculation. Unfortunately, the refractive index of opaque petroleum fluids cannot be measured unless special experimental techniques or dilution is used. For that reason, empirical correlations, and metaheuristic models were developed to predict the refractive index of petroleum fluids based on density, boiling point, and SARA fraction composition. The capability of these methods to accurately predict refractive index is discussed in this research with the aim of contrasting the empirical correlations with the artificial neural network modelling approach. Three data sets consisting of specific gravity and boiling point of 254 petroleum fractions, individual hydrocarbons, and hetero-compounds (Set 1); specific gravity and molecular weight of 136 crude oils (Set 2); and specific gravity, molecular weight, and SARA composition data of 102 crude oils (Set 3) were used to test eight empirical correlations available in the literature to predict the refractive index. Additionally, three new empirical correlations and three artificial neural network (ANN) models were developed for the three data sets using computer algebra system Maple, NLPSolve with Modified Newton Iterative Method, and Matlab. For Set 1, the most accurate refractive index prediction was achieved by the ANN model, with %AAD of 0.26% followed by the new developed correlation for Set 1 with %AAD of 0.37%. The best literature empirical correlation found for Set 1 was that of Riazi and Daubert (1987), which had %AAD of 0.40%. For Set 2, the best performers were the models of ANN, and the new developed correlation of Set 2 with %AAD of refractive index prediction was 0.21%, and 0.22%, respectively. For Set 3, the ANN model exhibited %AAD of refractive index prediction of 0.156% followed by the newly developed correlation for Set 3 with %AAD of 0.163%, while the empirical correlations of Fan et al. (2002) and Chamkalani (2012) displayed %AAD of 0.584 and 0.552%, respectively.
  • No Thumbnail Available
    Item
    Study of Bulk Properties Relation to SARA Composition Data of Various Vacuum Residues Employing Intercriteria Analysis
    (2022-12-01) Stratiev D.; Shishkova I.; Palichev G.N.; Atanassov K.; Ribagin S.; Nenov S.; Nedanovski D.; Ivanov V.
    Twenty-two straight run vacuum residues extracted from extra light, light, medium, heavy, and extra heavy crude oils and nine different hydrocracked vacuum residues were characterized for their bulk properties and SARA composition using four and eight fractions (SAR-ADTM) methods. Intercriteria analysis was employed to determine the statistically meaningful relations between the SARA composition data and the bulk properties. The determined strong relations were modeled using the computer algebra system Maple and NLPSolve with the Modified Newton Iterative Method. It was found that the SAR-ADTM saturates, and the sum of the contents of saturates and ARO-1 can be predicted from vacuum residue density, while the SAR-ADTM asphaltene fraction content, and the sum of asphaltenes, and resins contents correlate with the softening point of the straight run vacuum residues. The softening point of hydrocracked vacuum residues was found to strongly negatively correlates with SAR-ADTM Aro-1 fraction, and strongly positively correlates with SAR-ADTM Aro-3 fraction. While in the straight run vacuum residues, the softening point is controlled by the content of SAR-ADTM asphaltene fraction in the H-Oil hydrocracked vacuum residues, the softening point is controlled by the content of SAR-ADTM Aro-3 fraction content. During high severity H-Oil operation, resulting in higher conversion, hydrocracked vacuum residue with higher SAR-ADTM Aro-3 fraction content is obtained, which makes it harder and more brittle.