Filtros : "Journal of Petroleum Science and Engineering" "Pinto, Marcio Augusto Sampaio" Limpar

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

    Subjects: RESERVATÓRIOS DE PETRÓLEO, FILTROS DE KALMAN

    Acesso à fonteDOIHow to cite
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    • ABNT

      RANAZZI, Paulo Henrique e LUO, Xiaodong e PINTO, Marcio Augusto Sampaio. Improving pseudo-optimal Kalman-gain localization using the random shuffle method. Journal of Petroleum Science and Engineering, v. 215, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2022.110589. Acesso em: 08 nov. 2025.
    • APA

      Ranazzi, P. H., Luo, X., & Pinto, M. A. S. (2022). Improving pseudo-optimal Kalman-gain localization using the random shuffle method. Journal of Petroleum Science and Engineering, 215. doi:10.1016/j.petrol.2022.110589
    • NLM

      Ranazzi PH, Luo X, Pinto MAS. Improving pseudo-optimal Kalman-gain localization using the random shuffle method [Internet]. Journal of Petroleum Science and Engineering. 2022 ; 215[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2022.110589
    • Vancouver

      Ranazzi PH, Luo X, Pinto MAS. Improving pseudo-optimal Kalman-gain localization using the random shuffle method [Internet]. Journal of Petroleum Science and Engineering. 2022 ; 215[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2022.110589
  • Source: Journal of Petroleum Science and Engineering. Unidade: EP

    Subjects: PERFURAÇÃO DE POÇOS, PETRÓLEO, REDES NEURAIS

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    • ABNT

      AGOSTINI, Cristiano Eduardo e PINTO, Marcio Augusto Sampaio. Probabilistic Neural Network with Bayesian-based, spectral torque imaging and Deep Convolutional Autoencoder for PDC bit wear monitoring. Journal of Petroleum Science and Engineering, v. 193, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2020.1074342. Acesso em: 08 nov. 2025.
    • APA

      Agostini, C. E., & Pinto, M. A. S. (2020). Probabilistic Neural Network with Bayesian-based, spectral torque imaging and Deep Convolutional Autoencoder for PDC bit wear monitoring. Journal of Petroleum Science and Engineering, 193. doi:10.1016/j.petrol.2020.1074342
    • NLM

      Agostini CE, Pinto MAS. Probabilistic Neural Network with Bayesian-based, spectral torque imaging and Deep Convolutional Autoencoder for PDC bit wear monitoring [Internet]. Journal of Petroleum Science and Engineering. 2020 ; 193[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2020.1074342
    • Vancouver

      Agostini CE, Pinto MAS. Probabilistic Neural Network with Bayesian-based, spectral torque imaging and Deep Convolutional Autoencoder for PDC bit wear monitoring [Internet]. Journal of Petroleum Science and Engineering. 2020 ; 193[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2020.1074342
  • Source: Journal of Petroleum Science and Engineering. Unidade: EP

    Assunto: RESERVATÓRIOS DE PETRÓLEO

    Acesso à fonteDOIHow to cite
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    • ABNT

      RANAZZI, Paulo Henrique e PINTO, Marcio Augusto Sampaio. Influence of the Kalman gain localization in adaptive ensemble smoother history matching. Journal of Petroleum Science and Engineering, v. 179, p. 244 - 256, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2019.04.079. Acesso em: 08 nov. 2025.
    • APA

      Ranazzi, P. H., & Pinto, M. A. S. (2019). Influence of the Kalman gain localization in adaptive ensemble smoother history matching. Journal of Petroleum Science and Engineering, 179, 244 - 256. doi:10.1016/j.petrol.2019.04.079
    • NLM

      Ranazzi PH, Pinto MAS. Influence of the Kalman gain localization in adaptive ensemble smoother history matching [Internet]. Journal of Petroleum Science and Engineering. 2019 ; 179 244 - 256.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2019.04.079
    • Vancouver

      Ranazzi PH, Pinto MAS. Influence of the Kalman gain localization in adaptive ensemble smoother history matching [Internet]. Journal of Petroleum Science and Engineering. 2019 ; 179 244 - 256.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2019.04.079

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