Estimating the Geological Properties in Oil Reservoirs through Multi-gene Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Maynard:2018:CEC,

• author = "Jeff A. Maynard and Alvaro Talavera and Leonardo Forero and Marco {Aurelio Pacheco}",
• title = "Estimating the Geological Properties in Oil Reservoirs through Multi-gene Genetic Programming",
• booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2018",
• editor = "Marley Vellasco",
• address = "Rio de Janeiro, Brazil",
• month = "8-13 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2018.8477910",
• 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.",
• notes = "WCCI2018",

}

Genetic Programming entries for Jeff Maynard Guillen Alvaro Talavera Lopez Leonardo Alfredo Forero Mendoza Marco Aurelio Cavalcanti Pacheco

Citations