2008
DOI: 10.1080/10916460701399493 View full text |Buy / Rent full text
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Abstract: This paper presents a model for predicting the bubble-point pressure (P b ) and oil formation-volume-factor at bubble-point (B ob ) for crude samples collected from some producing wells in the Niger-Delta region of Nigeria. The model was developed using artificial neural networks with 542 experimentally obtained PressureVolume-Temperature (PVT) data sets. The model accurately predicts the P b and B ob as functions of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reserv… Show more

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“…The data set comprised of unsaturated pressure, temperature, and bubble point pressure, above the bubble-point viscosity, API gravity, and specific gravity of oil. Recently, Obanijesu and Araromi (2008) used data obtained from the same area to predict bubble-point pressure and formation volume factor by applying a neural network modeling approach; however, the data are not descriptive enough to provide general characteristic features of oil fields in the area. The data were first preprocessed so that they always fell within a range [ 1, 1], which will make the training of a neuro-fuzzy network more efficient.…”
Section: Data Acquisition and Analysismentioning
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“…The data set comprised of unsaturated pressure, temperature, and bubble point pressure, above the bubble-point viscosity, API gravity, and specific gravity of oil. Recently, Obanijesu and Araromi (2008) used data obtained from the same area to predict bubble-point pressure and formation volume factor by applying a neural network modeling approach; however, the data are not descriptive enough to provide general characteristic features of oil fields in the area. The data were first preprocessed so that they always fell within a range [ 1, 1], which will make the training of a neuro-fuzzy network more efficient.…”
Section: Data Acquisition and Analysismentioning
“…In case of bubble point pressure estimation from PVT data, including solution gas-oil-ratio (Rs), gas specific gravity (Yg), temperature (T), and stock-tank oil gravity (c o ); several studies showed superiority of neural network to empirical correlations (Al-Marhoun and Osman 2002;Kh.A. El-M Shokir and Sayyouh 2003;Obanijesu and Araromi 2008;Dutta and Gupta 2010). In recent years, a growing tendency is observed among researchers to utilize support vector in their regression problems.…”
Section: Introductionmentioning