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dc.contributor.author
Cersósimo, Darío Sergio
dc.contributor.author
Ravazzoli, Claudia Leonor
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dc.contributor.author
García Martinez, Ramón
dc.date.available
2020-10-13T17:45:01Z
dc.date.issued
2016-03
dc.identifier.citation
Cersósimo, Darío Sergio; Ravazzoli, Claudia Leonor; García Martinez, Ramón; Prediction of lateral variations in reservoir properties throughout an interpreted seismic horizon using an artificial neural network; Society of Exploration Geophysicists; The Leading Edge; 35; 3; 3-2016; 265-269
dc.identifier.issn
1070-485X
dc.identifier.uri
http://hdl.handle.net/11336/115806
dc.description.abstract
Successful use of an artificial neural network is shown to predict lateral variations of seismic velocity, density, thickness, and gamma rays associated with sand dune reservoirs identified on a previously interpreted seismic horizon. The work is presented in two main sections. Section one is a feasibility analysis based on synthetic data. A known geologic model is used, performed by pseudowells, in which lateral variations in seismic velocity, density, and gamma ray values are related to the dunes. The synthetic seismic model and the attributes derived are used as training input in the neural network. Section two is a real case example where the methodology is applied to a real seismic data set. Results indicate that using a set of data and attributes restricted to a time interval corresponding to a previously interpreted seismic horizon is more efficient than using a whole data cube, involving a very large volume of data.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Society of Exploration Geophysicists
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
ATTRIBUTES
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HORIZONS
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INTERPRETATION
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MODELING
dc.subject.classification
Ciencias de la Información y Bioinformática
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dc.subject.classification
Ciencias de la Computación e Información
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Prediction of lateral variations in reservoir properties throughout an interpreted seismic horizon using an artificial neural network
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-08-19T20:22:43Z
dc.identifier.eissn
1938-3789
dc.journal.volume
35
dc.journal.number
3
dc.journal.pagination
265-269
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Washington
dc.description.fil
Fil: Cersósimo, Darío Sergio. GALP Energía; Portugal
dc.description.fil
Fil: Ravazzoli, Claudia Leonor. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
dc.description.fil
Fil: García Martinez, Ramón. Universidad Nacional de Lanús; Argentina
dc.journal.title
The Leading Edge
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1190/tle35030265.1
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://library.seg.org/doi/abs/10.1190/tle35030265.1
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