Genetic Programming in Geostatistical Reservoir Geophysics

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Azevedo:2016:CSCI,

• author = "Leonardo Azevedo and Ruben Nunes and Amilcar Soares",
• booktitle = "2016 International Conference on Computational Science and Computational Intelligence (CSCI)",
• title = "Genetic Programming in Geostatistical Reservoir Geophysics",
• year = "2016",
• pages = "1208--1213",
• abstract = "Hydrocarbon reservoir modelling and characterisation is a critical step for the success of oil and/or gas exploration and production projects. Reservoir modelling is frequently based on the results provided by geostatistical seismic inversion techniques. These procedures are computationally heavy and expensive even for small-to-medium size fields due to the use of stochastic sequential simulation as the model perturbation technique. This work proposes the use of machine learning techniques, specifically symbolic regression, a category from the group of genetic programming methodologies, as a proxy to surpass the need of stochastic sequential simulation without compromising the advantage of using these simulation methodologies, for example uncertainty assessment of the property of interest. The proposed methodology is illustrated with an application example to a real case study and the results compared with the traditional geostatistical seismic inversion approach.",
• keywords = "genetic algorithms, genetic programming, Computational modelling, Correlation coefficient, Data models, Iterative methods, Mathematical model, Reflection, Stochastic processes, genetic programming geostatistical seismic inversion seismic reservoir characterisation",
• DOI = "doi:10.1109/CSCI.2016.0228",
• month = dec,
• notes = "Also known as \cite{7881521}",

}

Genetic Programming entries for Leonardo Azevedo Ruben Nunes Amilcar Soares

Citations