Título: | CONVOLUTIONAL NEURAL NETWORK FOR SEISMIC HORIZONS IDENTIFICATION | ||||||||||||
Autor: |
MAYARA GOMES SILVA |
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Colaborador(es): |
MARCELO GATTASS - Orientador |
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Catalogação: | 07/NOV/2022 | Língua(s): | PORTUGUESE - BRAZIL |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=61112&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=61112&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.61112 | ||||||||||||
Resumo: | |||||||||||||
Oil and gas are important in the world economy, used as raw materials in
various products. For the extraction of these products, it is necessary to carry
out the characterization of the hydrocarbon reservoirs. This characterization
extracts a volume with seismic data from the region of interest. These data
are interpreted to identify various features, such as the classification of seismic facies, horizons, faults, and gas. A large amount of seismic volume data
makes manual interpretation increasingly challenging. Many researchers in the
field of seismic interpretation have invested in methods using neural networks.
Convolutional Neural Networks (CNN) are widely used in computer vision
problems and get great results in many situations with 2D data. The present
work aimed to apply convolutional neural networks in the supervised mapping
of seismic horizons. We evaluated our proposal using the F3 block with seismic
facies annotations. The data representation in the input layer are patches of
sections. In the horizon forecast, we evaluate the architectures of ResUnet and
DC-Unet. We use the Generalized Dice and the Focal Tversky loss functions
for the loss function. The method delivered promising results with the ResUnet
and Focal Tversky loss function on data based on 128x128 patches, reaching approximately 56 percent on the Dice metric. The full implementation and the trained
networks are available at https://github.com/mayaragomys/seismic_horizons.
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