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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
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: 07 ago. 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 ago. 07 ] 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 ago. 07 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169
Artigo de periodico
Improving pseudo-optimal Kalman-gain localization using the random shuffle method (2022)
Ranazzi, Paulo Henrique; Luo, Xiaodong; Pinto, Marcio Augusto Sampaio
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: RESERVATÓRIOS DE PETRÓLEO, FILTROS DE KALMAN
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 07 ago. 2024.
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 2024 ago. 07 ] 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 2024 ago. 07 ] Available from: https://doi.org/10.1016/j.petrol.2022.110589
Artigo de periodico
Probabilistic Neural Network with Bayesian-based, spectral torque imaging and Deep Convolutional Autoencoder for PDC bit wear monitoring (2020)
Agostini, Cristiano Eduardo; Pinto, Marcio Augusto Sampaio
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: PERFURAÇÃO DE POÇOS, PETRÓLEO, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 07 ago. 2024.
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 2024 ago. 07 ] 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 2024 ago. 07 ] Available from: https://doi.org/10.1016/j.petrol.2020.1074342

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