Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability

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...

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Main Authors: Obanijesu, Emmanuel, Araromi, D.
Format: Journal Article
Published: Taylor & Francis Group 2008
Online Access:http://hdl.handle.net/20.500.11937/6660
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author Obanijesu, Emmanuel
Araromi, D.
author_facet Obanijesu, Emmanuel
Araromi, D.
author_sort Obanijesu, Emmanuel
building Curtin Institutional Repository
collection Online Access
description 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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institution Curtin University Malaysia
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publishDate 2008
publisher Taylor & Francis Group
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spelling curtin-20.500.11937-66602017-09-13T14:37:52Z Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability Obanijesu, Emmanuel Araromi, D. 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. 2008 Journal Article http://hdl.handle.net/20.500.11937/6660 10.1080/10916460701399493 Taylor & Francis Group restricted
spellingShingle Obanijesu, Emmanuel
Araromi, D.
Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title_full Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title_fullStr Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title_full_unstemmed Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title_short Predicting bubble-point pressure and formation-volume factor of Nigerian crude oil system for environmental sustainability
title_sort predicting bubble-point pressure and formation-volume factor of nigerian crude oil system for environmental sustainability
url http://hdl.handle.net/20.500.11937/6660