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dc.contributor.authorObanijesu, Emmanuel
dc.contributor.authorOmidiora, E.
dc.date.accessioned2017-01-30T11:27:57Z
dc.date.available2017-01-30T11:27:57Z
dc.date.created2016-09-12T08:36:47Z
dc.date.issued2009
dc.identifier.citationObanijesu, E. and Omidiora, E. 2009. The artificial neural network's prediction of crude oil viscosity for pipeline safety. Petroleum Science and Technology. 27 (4): pp. 412-426.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/11969
dc.identifier.doi10.1080/10916460701853846
dc.description.abstract

Predicting crude oil viscosity is a challenge faced by reservoir engineers in production planning. Some early researchers have propounded some theories based on crude oil properties and have encountered various problems leading to errors in forecasted values. This article discusses work carried out with a model using an artificial neural network (ANN) for predicting crude oil viscosity of Nigerian crude oil. The model was started through adoption of a classical regression technique empirical method for dead oil viscosity as a function of American Institute for Petroleum (API) and reduced temperature. The Peng-Robinson equation of state and other thermodynamic properties are introduced, coupled with the Standing model for calculating bubble point pressure (Pb). The developed model was evaluated using existing measured real-life data collected from 10 oil fields within the Niger Delta region of Nigeria. Both the predicted and measured viscosities were plotted against each corresponding reservoir pressure to establish the model's level of reliability. The superimposition of the pressure-viscosity relationship shows that at each point, the viscosity model captures the physical behavior of viscosity variations with pressure. In each case, the ANN does not require a data relationship to predict the crude oil viscosity but rather relies on the field data obtained for training. For this reason, it is recommended that the ANN approach should be applied in oil fields for reduction in error, computational time, and cost of overproduction and underproduction.

dc.publisherTaylor & Francis Group
dc.titleThe artificial neural network's prediction of crude oil viscosity for pipeline safety
dc.typeJournal Article
dcterms.source.volume27
dcterms.source.number4
dcterms.source.startPage412
dcterms.source.endPage426
dcterms.source.issn1091-6466
dcterms.source.titlePetroleum Science and Technology
curtin.departmentDepartment of Chemical Engineering
curtin.accessStatusFulltext not available


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