1999
DOI: 10.2118/56850-pa
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Neural Network Model for Estimating the PVT Properties of Middle East Crude Oils

Abstract: Summary The importance of pressure/volume/temperature (PVT) properties, such as the bubblepoint pressure, solution gas-oil ratio, and oil formation volume factor, makes their accurate determination necessary for reservoir performance calculations. An enormous amount of PVT data has been collected and correlated over many years for different types of hydrocarbon systems. Almost all of these correlations were developed with linear or nonlinear multiple regression or graphical techniques. Artifi… Show more

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Cited by 63 publications
(9 citation statements)
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“…The basic advantages of ANN over the conventional correlations is neural networks have large degrees of freedom for fitting parameters and thus, capture the systems' non-linearity better than regression methods. Furthermore, ANN can be further trained and refined when additional data become available in order to improve their prediction accuracy while it is impossible to make any further change in a linear or non-linear regression model as soon as a model development is over (Farshad, Garber, & Lorde, 2000;Gharbi & Elsharkawy, 1996;Gharbi & Elsharkawy, 1997;Makinde, Ako, Orodu, & Asuquo, 2012). The FFBP model has been applied in many previous studies of renewable energy (e.g., Ravinesh C Deo & Sahin, 2017;Ghorbani, Khatibi, Hosseini, & Bilgili, 2013).…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…The basic advantages of ANN over the conventional correlations is neural networks have large degrees of freedom for fitting parameters and thus, capture the systems' non-linearity better than regression methods. Furthermore, ANN can be further trained and refined when additional data become available in order to improve their prediction accuracy while it is impossible to make any further change in a linear or non-linear regression model as soon as a model development is over (Farshad, Garber, & Lorde, 2000;Gharbi & Elsharkawy, 1996;Gharbi & Elsharkawy, 1997;Makinde, Ako, Orodu, & Asuquo, 2012). The FFBP model has been applied in many previous studies of renewable energy (e.g., Ravinesh C Deo & Sahin, 2017;Ghorbani, Khatibi, Hosseini, & Bilgili, 2013).…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…From these oil fields, 429 laboratory PVT analyses data were obtained and used. The data sets were extracted from various papers (Glaso 1980, Al-Marhoun 1988, Dokla & Osman 1992, Ghetto et al 1994, Gharbi & Elsharkawy 1997. The data sets were divided into two groups: one group including 286 data sets used as training data for constructing the correlation, and the other including 143 data sets used as test data for the correlation validation.…”
Section: Development Of the New Correlationmentioning
confidence: 99%
“…In addition to the data generated from experimental part of this study, data from the literature were also used in developing the PSO–ANN model. The data used for developing the neural‐genetic model are from Alston et al,8 Cardenas,40 Dong,41 Dong et al,42 Eissa and Shokir,16 Emera and Sarma,34, 35 Frimodig et al,36 Gardner et al,37 Gharbi and Elsharkawy,43 Graue and Zana,38 Jacobson,44 Metcalfe,11 Okuno,33 Sebastian et al24 and Yuan et al45 These data are presented in Appendix B.…”
Section: Experimental Procedures and Data Collectionmentioning
confidence: 99%