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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
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: 08 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. 08 ] 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. 08 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169
Artigo de periodico
Data-driven technique estimates skin factor and average pressure during oil-flowing periods (2022)
Kubota, Leonardo Kenji; Gioria, Rafael dos Santos
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Assunto: ÓLEO E GAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
KUBOTA, Leonardo Kenji e GIORIA, Rafael dos Santos. Data-driven technique estimates skin factor and average pressure during oil-flowing periods. Journal of Petroleum Science and Engineering, v. 219, p. 17, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2022.111061. Acesso em: 08 nov. 2025.
APA
Kubota, L. K., & Gioria, R. dos S. (2022). Data-driven technique estimates skin factor and average pressure during oil-flowing periods. Journal of Petroleum Science and Engineering, 219, 17. doi:10.1016/j.petrol.2022.111061
NLM
Kubota LK, Gioria R dos S. Data-driven technique estimates skin factor and average pressure during oil-flowing periods [Internet]. Journal of Petroleum Science and Engineering. 2022 ; 219 17.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2022.111061
Vancouver
Kubota LK, Gioria R dos S. Data-driven technique estimates skin factor and average pressure during oil-flowing periods [Internet]. Journal of Petroleum Science and Engineering. 2022 ; 219 17.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1016/j.petrol.2022.111061
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
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: 08 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. 08 ] 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. 08 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382

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