Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Kaydani:2014:JPSE,
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author = "Hossein Kaydani and Ali Mohebbi and Mehdi Eftekhari",
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title = "Permeability estimation in heterogeneous oil
reservoirs by multi-gene genetic programming
algorithm",
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journal = "Journal of Petroleum Science and Engineering",
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volume = "123",
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pages = "201--206",
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year = "2014",
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note = "Neural network applications to reservoirs:
Physics-based models and data models",
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ISSN = "0920-4105",
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DOI = "doi:10.1016/j.petrol.2014.07.035",
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URL = "http://www.sciencedirect.com/science/article/pii/S0920410514002344",
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abstract = "Permeability estimation has a significant impact on
petroleum fields operation and reservoir management.
Different methods were proposed to measure this
parameter, which some of them are inaccurate, and some
others such as core analysis are cost and time
consuming. Intelligent techniques are powerful tools to
recognise the possible patterns between input and
output spaces, which can be applied to predict
reservoir parameters. This study proposed a new
approach based on multi-gene genetic programming (MGGP)
to predict permeability in one of the heterogeneous oil
reservoirs in Iran. The MGGP model with artificial
neural networks (ANNs), adaptive neuro-fuzzy inference
system (ANFIS) and genetic programming (GP) model were
used to predict the permeability and obtained results
were compared statistically. The comparison of results
showed that the MGGP model can be applied effectively
in permeability prediction, which gives low
computational time. Furthermore, one equation based on
the MGGP model using well log and core experimental
data was generated to predict permeability in porous
media.",
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keywords = "genetic algorithms, genetic programming, rock
permeability, porous media, core analysis",
- }
Genetic Programming entries for
Hossein Kaydani
Ali Mohebbi
Mehdi Eftekhari
Citations