Estimating the Geological Properties in Oil Reservoirs through Multi-gene Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Maynard:2018:CEC,
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author = "Jeff A. Maynard and Alvaro Talavera and
Leonardo Forero and Marco {Aurelio Pacheco}",
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title = "Estimating the Geological Properties in Oil Reservoirs
through Multi-gene Genetic Programming",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2018.8477910",
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abstract = "Oil exploitation and production fields require
allocating large investments to reduce low
production-associated risks, which can be minimized by
the successful characterization of oil reservoirs. The
characterization process lies on geological property
estimates generated during well-drilling procedures and
on information extracted from 3D seismic data.
Computational intelligence techniques proved to be
efficient tools to estimate nonlinear relations, which
can be applied to predict reservoir parameters. The aim
of the current study is to address an approach based on
the application of the Multi-Gene Genetic Programming
(mgGP) algorithm to estimate porosity in an oil
reservoir by using seismic data and well logs. The
relation between seismic and porosity data about
Namorado oil field was satisfactorily represented by
means of mgGP.",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Jeff Maynard Guillen
Alvaro Talavera Lopez
Leonardo Alfredo Forero Mendoza
Marco Aurelio Cavalcanti Pacheco
Citations