2012
DOI: 10.1080/10916461003773021
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The Prediction of the Density of Undersaturated Crude Oil Using Multilayer Feed-Forward Back-Propagation Perceptron

Abstract: Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network-based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them.

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Cited by 8 publications
(10 citation statements)
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“…Densities, , of the binary mixture n-hexane (1) + n-hexadecane (2) Density data for every mixture were also correlated as a function of temperature and pressure through Eq. 18, with an AAD % lower or equal to 0.07%.…”
Section: Resultsmentioning
confidence: 99%
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“…Densities, , of the binary mixture n-hexane (1) + n-hexadecane (2) Density data for every mixture were also correlated as a function of temperature and pressure through Eq. 18, with an AAD % lower or equal to 0.07%.…”
Section: Resultsmentioning
confidence: 99%
“…It is employed in various reservoir engineering calculations, reservoir simulation, and design of transport and processing facilities. 1,2 In this work we perform studies on density of two alkane binary mixtures from (278.15 to 463.15) K and up to 60 MPa in the whole composition range. Thus, we have studied the system n-hexane + n-decane, as well as a more asymmetric system n-hexane + n-hexadecane.…”
Section: Introductionmentioning
confidence: 99%
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“…Standing and Katz (1942) presented a method to calculate the oil density at the bubble point based on the principle of an ideal solution. Rostami et al (2012Rostami et al ( , 2013 used neural networks and Gaussian process regression to estimate the oil density.…”
Section: Introductionmentioning
confidence: 99%