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Filtros : "Journal of Petroleum Science and Engineering" "PETROGRAFIA" Limpar

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  • Artigo de periodico

    Prediction of nuclear magnetic resonance porosity well-logs in a carbonate reservoir using supervised machine learning models (2024)

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

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

    Subjects: PETROGRAFIA, INTELIGÊNCIA ARTIFICIAL

    PrivadoAcesso à fonteDOIHow to cite
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    • 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 , 2024Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2022.111169. Acesso em: 09 nov. 2025.

    • APA

      Tamoto, H., Gioria, R. dos S., & Carneiro, C. de C. (2024). 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. 2024 ; 220 10 .[citado 2025 nov. 09 ] 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. 2024 ; 220 10 .[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169

  • Artigo de periodico

    Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images (2019)

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

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

    Subjects: INTELIGÊNCIA ARTIFICIAL, PETROGRAFIA, ROCHAS SEDIMENTARES

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

    • ABNT

      RUBO, Rafael Andrello et al. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. Journal of Petroleum Science and Engineering, v. 183, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2019.106382. Acesso em: 09 nov. 2025.

    • APA

      Rubo, R. A., Carneiro, C. de C., Michelon, M. F., & Gioria, R. dos S. (2019). Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. Journal of Petroleum Science and Engineering, 183. doi:10.1016/j.petrol.2019.106382

    • NLM

      Rubo RA, Carneiro C de C, Michelon MF, Gioria R dos S. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images [Internet]. Journal of Petroleum Science and Engineering. 2019 ;183[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382

    • Vancouver

      Rubo RA, Carneiro C de C, Michelon MF, Gioria R dos S. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images [Internet]. Journal of Petroleum Science and Engineering. 2019 ;183[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382
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