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

Authors :
Dicho Stratiev
Sotir Sotirov
Evdokia Sotirova
Svetoslav Nenov
Rosen Dinkov
Ivelina Shishkova
Iliyan Venkov Kolev
Dobromir Yordanov
Svetlin Vasilev
Krassimir Atanassov
Stanislav Simeonov
Georgi Nikolov Palichev
Source :
Processes, Volume 11, Issue 2, Pages: 426
Publication Year :
2023
Publisher :
Multidisciplinary Digital Publishing Institute, 2023.

Abstract

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.

Details

Language :
English
ISSN :
22279717
Database :
OpenAIRE
Journal :
Processes
Accession number :
edsair.doi.dedup.....e561c4b11e44f4774527cb42e8a9e1c9
Full Text :
https://doi.org/10.3390/pr11020426