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Filtros : "INTELIGÊNCIA ARTIFICIAL" "Financiamento EPUSP" Limpar

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  • Artigo de periodico

    Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images (2023)

    Rubo, Rafael Andrello; Michelon, Mateus Fontana; Carneiro, Cleyton de Carvalho

    Source: Earth Science Informatics. Unidade: EP

    Subjects: PETROGRAFIA, INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      RUBO, Rafael Andrello e MICHELON, Mateus Fontana e CARNEIRO, Cleyton de Carvalho. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images. Earth Science Informatics, p. 10 2023, 2023Tradução . . Disponível em: https://doi.org/10.1007/s12145-022-00901-9. Acesso em: 29 maio 2024.

    • APA

      Rubo, R. A., Michelon, M. F., & Carneiro, C. de C. (2023). Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images. Earth Science Informatics, 10 2023. doi:10.1007/s12145-022-00901-9

    • NLM

      Rubo RA, Michelon MF, Carneiro C de C. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images [Internet]. Earth Science Informatics. 2023 ;10 2023.[citado 2024 maio 29 ] Available from: https://doi.org/10.1007/s12145-022-00901-9

    • Vancouver

      Rubo RA, Michelon MF, Carneiro C de C. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images [Internet]. Earth Science Informatics. 2023 ;10 2023.[citado 2024 maio 29 ] Available from: https://doi.org/10.1007/s12145-022-00901-9

  • Artigo de periodico

    Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models (2023)

    Tamoto, Hugo; Gioria, Rafael dos Santos ; Carneiro, Cleyton de Carvalho

    Source: Journal of Petroleum Science and Engineering. Unidade: EP

    Subjects: PETROGRAFIA, INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      TAMOTO, Hugo e GIORIA, Rafael dos Santos e CARNEIRO, Cleyton de Carvalho. Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models. Journal of Petroleum Science and Engineering, v. 220, p. 10 , 2023Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2022.111169. Acesso em: 29 maio 2024.

    • APA

      Tamoto, H., Gioria, R. dos S., & Carneiro, C. de C. (2023). Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models. Journal of Petroleum Science and Engineering, 220, 10 . doi:10.1016/j.petrol.2022.111169

    • NLM

      Tamoto H, Gioria R dos S, Carneiro C de C. Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models [Internet]. Journal of Petroleum Science and Engineering. 2023 ; 220 10 .[citado 2024 maio 29 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169

    • Vancouver

      Tamoto H, Gioria R dos S, Carneiro C de C. Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models [Internet]. Journal of Petroleum Science and Engineering. 2023 ; 220 10 .[citado 2024 maio 29 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169
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