Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
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This paper presents a model for predicting the bubble-point pressure (Pb) and oil formation-volume-factor at bubble-point (Bob) 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 Pressure-Volume-Temperature (PVT) data sets. The model accurately predicts the Pb and Bob as functions of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. In order to obtain a generalized accurate model, backpropagation with momentum for error minimization was used. The accuracy of the developed model in this study was compared with some published correlations. Apart from its accuracy, this model takes a shorter time to predict the PVT properties when compared with empirical correlations. The immediate reason for this may have to do with the non-linear nature of the empirical correlations.
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