Multi-attribute Seismic Cell Reservoir/Non-Reservoir Classification with Kernel Based Methods

Rener Castro and Alex Bordignon and Hélio Lopes and Thomas Lewiner and Geovan Tavares and Rogério Santos and Amin Murad. ( 2007 )
in: 27th gOcad Meeting, ASGA

Abstract

Kernel machines are recognized as an important class of artificial intelligence methods. Their use in geosciences applications have been increasing in recent years. In this paper we propose a new scheme to distinguish each seismic cell as reservoir or non-reservoir. For this, we use multi-attribute seismic and well data for the learning process. The method shows to be very promising and by the use of a friendly GoCAD interface the user specify the few parameters of the method.

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    BibTeX Reference

    @inproceedings{CastroRM2007,
 abstract = { Kernel machines are recognized as an important class of artificial intelligence methods. Their use in geosciences applications have been increasing in recent years. In this paper we propose a new scheme to distinguish each seismic cell as reservoir or non-reservoir. For this, we use multi-attribute seismic and well data for the learning process. The method shows to be very promising and by the use of a friendly GoCAD interface the user specify the few parameters of the method. },
 author = { Castro, Rener AND Bordignon, Alex AND Lopes, Hélio AND Lewiner, Thomas AND Tavares, Geovan AND Santos, Rogério AND Murad, Amin },
 booktitle = { 27th gOcad Meeting },
 month = { "june" },
 publisher = { ASGA },
 title = { Multi-attribute Seismic Cell Reservoir/Non-Reservoir Classification with Kernel Based Methods },
 year = { 2007 }
}