Filtros : "Journal of Petroleum Science and Engineering" "PERFURAÇÃO DE POÇOS" Limpar

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

    Subjects: PERFURAÇÃO DE POÇOS, FLUÍDOS DE PERFURAÇÃO, SOLUBILIDADE

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    • ABNT

      DUARTE, Antonio Carlos Magalhães et al. An experimental study of gas solubility in glycerin based drilling fluid applied to well control. Journal of Petroleum Science and Engineering, v. 207, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2021.109194. Acesso em: 09 nov. 2025.
    • APA

      Duarte, A. C. M., Ribeiro, P. R., KIM, N. R., Mendes, J. R. P., Policarpo, N. A., & Vianna, A. (2021). An experimental study of gas solubility in glycerin based drilling fluid applied to well control. Journal of Petroleum Science and Engineering, 207. doi:10.1016/j.petrol.2021.109194
    • NLM

      Duarte ACM, Ribeiro PR, KIM NR, Mendes JRP, Policarpo NA, Vianna A. An experimental study of gas solubility in glycerin based drilling fluid applied to well control [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 207[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2021.109194
    • Vancouver

      Duarte ACM, Ribeiro PR, KIM NR, Mendes JRP, Policarpo NA, Vianna A. An experimental study of gas solubility in glycerin based drilling fluid applied to well control [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 207[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2021.109194
  • 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: 09 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. 09 ] 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. 09 ] Available from: https://doi.org/10.1016/j.petrol.2020.1074342
  • Source: Journal of Petroleum Science and Engineering. Unidade: EP

    Subjects: PERFURAÇÃO DE POÇOS, EQUILÍBRIO QUÍMICO

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    • ABNT

      KIM, N. R e RIBEIRO, P. R e PESSÔA FILHO, Pedro de Alcântara. PVT behavior of methane and ester-based drilling emulsions. Journal of Petroleum Science and Engineering, v. No 2015, p. 360-366, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2015.08.018. Acesso em: 09 nov. 2025.
    • APA

      Kim, N. R., Ribeiro, P. R., & Pessôa Filho, P. de A. (2015). PVT behavior of methane and ester-based drilling emulsions. Journal of Petroleum Science and Engineering, No 2015, 360-366. doi:10.1016/j.petrol.2015.08.018
    • NLM

      Kim NR, Ribeiro PR, Pessôa Filho P de A. PVT behavior of methane and ester-based drilling emulsions [Internet]. Journal of Petroleum Science and Engineering. 2015 ; No 2015 360-366.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2015.08.018
    • Vancouver

      Kim NR, Ribeiro PR, Pessôa Filho P de A. PVT behavior of methane and ester-based drilling emulsions [Internet]. Journal of Petroleum Science and Engineering. 2015 ; No 2015 360-366.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2015.08.018

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