Development of a robust model for prediction of under-saturated reservoir oil viscosity
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- @Article{Hajirezaie:2017:JML,
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author = "Sassan Hajirezaie and Amin Pajouhandeh and
Abdolhossein Hemmati-Sarapardeh and Maysam Pournik and
Bahram Dabir",
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title = "Development of a robust model for prediction of
under-saturated reservoir oil viscosity",
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journal = "Journal of Molecular Liquids",
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volume = "229",
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pages = "89--97",
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year = "2017",
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ISSN = "0167-7322",
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DOI = "doi:10.1016/j.molliq.2016.11.088",
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URL = "http://www.sciencedirect.com/science/article/pii/S0167732216320608",
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abstract = "Fluid viscosity is considered as one of the most
important parameters for reservoir simulation,
performance evaluation, designing production
facilities, etc. In this communication, a robust model
based on Genetic Programming (GP) approach was
developed for prediction of under-saturated reservoir
oil viscosity. A third order polynomial correlation for
prediction of under-saturated oil viscosity as a
function of bubble point viscosity, pressure
differential (pressure minus bubble point pressure) and
pressure ratio (pressure divided by bubble point
pressure) was proposed. To this end, a large number of
experimental viscosity databank including 601 data sets
from various regions covering a wide range of reservoir
conditions was collected from literature. Statistical
and graphical error analyses were employed to evaluate
the performance and accuracy of the model. The results
indicate that the developed model is able to estimate
oil viscosity with an average absolute percentage
relative error of 4.47percent. These results in
addition to the graphical results confirmed the
robustness and superiority of the developed model
compared to the most well-known existing correlations
of under-saturated oil viscosity. Additionally, the
investigation of relative impact of input parameters on
under-saturated reservoir oil viscosity demonstrates
that bubble point viscosity has the greatest impact on
oil viscosity.",
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keywords = "genetic algorithms, genetic programming,
Under-saturated reservoir oil viscosity, Statistical
and graphical error analyses, Relevancy factor",
- }
Genetic Programming entries for
Sassan Hajirezaie
Amin Pajouhandeh
Abdolhossein Hemmati-Sarapardeh
Maysam Pournik
Bahram Dabir
Citations