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

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dc.contributor.authorDicho Stratiev
dc.contributor.authorSotir Sotirov
dc.contributor.authorEvdokia Sotirova
dc.contributor.authorSvetoslav Nenov
dc.contributor.authorRosen Dinkov
dc.contributor.authorIvelina Shishkova
dc.contributor.authorIliyan Venkov Kolev
dc.contributor.authorDobromir Yordanov
dc.contributor.authorSvetlin Vasilev
dc.contributor.authorKrassimir Atanassov
dc.contributor.authorStanislav Simeonov
dc.contributor.authorGeorgi Nikolov Palichev
dc.date.accessioned2024-05-25T13:38:32Z
dc.date.available2024-05-25T13:38:32Z
dc.date.issued2023-01-31
dc.description.abstractThe 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.
dc.identifier.doi10.3390/pr11020426
dc.identifier.issn2227-9717
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85149253138&origin=inward&txGid=f80c711caf2dfdeb4cd7c8017d696380
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/28
dc.publisherMDPI AG
dc.relation.ispartofProcesses
dc.titlePrediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks
dc.typejournal-article
oaire.citation.issue2
oaire.citation.volume11
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