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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
Fonte: Journal of Petroleum Science and Engineering. Unidade: EP
Assuntos: 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 , 2024Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2022.111169. Acesso em: 04 dez. 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 dez. 04 ] 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 dez. 04 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169
Artigo de periodico
Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations (2023)
Tamoto, Hugo ; Gioria, Rafael dos Santos ; Carneiro, Cleyton de Carvalho
Fonte: Geoenergy Science and Engineering. Unidade: EP
Assuntos: CARBONATOS, RESERVATÓRIOS, WIRELESS
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. Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations. Geoenergy Science and Engineering, v. 229, p. 1-8, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.geoen.2023.212138. Acesso em: 04 dez. 2025.
APA
Tamoto, H., Gioria, R. dos S., & Carneiro, C. de C. (2023). Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations. Geoenergy Science and Engineering, 229, 1-8. doi:10.1016/j.geoen.2023.212138
NLM
Tamoto H, Gioria R dos S, Carneiro C de C. Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations [Internet]. Geoenergy Science and Engineering. 2023 ;229 1-8.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1016/j.geoen.2023.212138
Vancouver
Tamoto H, Gioria R dos S, Carneiro C de C. Enhancing wireline formation testing with explainable machine learning: Predicting effective and non-effective stations [Internet]. Geoenergy Science and Engineering. 2023 ;229 1-8.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1016/j.geoen.2023.212138
Parte de monografia/livro
Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models (2023)
Tamoto, Hugo ; Contreras, Rodrigo Colnago ; Santos, Francisco Lledo dos ; Viana, Monique Simplicio ; Gioria, Rafael dos Santos ; Carneiro, Cleyton de Carvalho
Fonte: Artificial Intelligence and Soft Computing. Nome do evento: International Conference on Artificial Intelligence and Soft Computing. Unidade: EP
Assuntos: APRENDIZADO COMPUTACIONAL, POÇOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAMOTO, Hugo et al. Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models. Artificial Intelligence and Soft Computing. Tradução . Suica: Escola Politécnica, Universidade de São Paulo, 2023. v. 13588. p. 115–130. Disponível em: https://doi.org/10.1007/978-3-031-23492-7_11. Acesso em: 04 dez. 2025.
APA
Tamoto, H., Contreras, R. C., Santos, F. L. dos, Viana, M. S., Gioria, R. dos S., & Carneiro, C. de C. (2023). Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models. In Artificial Intelligence and Soft Computing (Vol. 13588, p. 115–130). Suica: Escola Politécnica, Universidade de São Paulo. doi:10.1007/978-3-031-23492-7_11
NLM
Tamoto H, Contreras RC, Santos FL dos, Viana MS, Gioria R dos S, Carneiro C de C. Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models [Internet]. In: Artificial Intelligence and Soft Computing. Suica: Escola Politécnica, Universidade de São Paulo; 2023. p. 115–130.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1007/978-3-031-23492-7_11
Vancouver
Tamoto H, Contreras RC, Santos FL dos, Viana MS, Gioria R dos S, Carneiro C de C. Synthetic Slowness Shear Well-Log Prediction Using Supervised Machine Learning Models [Internet]. In: Artificial Intelligence and Soft Computing. Suica: Escola Politécnica, Universidade de São Paulo; 2023. p. 115–130.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1007/978-3-031-23492-7_11

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