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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: 09 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. 09 ] 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. 09 ] Available from: https://doi.org/10.1016/j.petrol.2022.111169
Artigo de periodico
Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs (2021)
Oliveira, Lucas Abreu Blanes de; Carneiro, Cleyton de Carvalho
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: ESPECTROSCOPIA ATÔMICA, ESPECTROSCOPIA DE RAIO GAMA, INTELIGÊNCIA ARTIFICIAL, PRÉ-SAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Lucas Abreu Blanes de e CARNEIRO, Cleyton de Carvalho. Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs. Journal of Petroleum Science and Engineering, v. 196, n. Ja 2021. Artigo 108080, p. 1-25, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2020.108080. Acesso em: 09 nov. 2025.
APA
Oliveira, L. A. B. de, & Carneiro, C. de C. (2021). Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs. Journal of Petroleum Science and Engineering, 196( Ja 2021. Artigo 108080), 1-25. doi:10.1016/j.petrol.2020.108080
NLM
Oliveira LAB de, Carneiro C de C. Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 196( Ja 2021. Artigo 108080): 1-25.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2020.108080
Vancouver
Oliveira LAB de, Carneiro C de C. Synthetic geochemical well logs generation using ensemble machine learning techniques for the Brazilian pre-salt reservoirs [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 196( Ja 2021. Artigo 108080): 1-25.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2020.108080
Artigo de periodico
Influence of carbonate reservoir mineral heterogeneities on contact angle measurements (2021)
Ferrari, Jean Vicente ; Silveira, Bruno Marco de Oliveira; Arismendi Florez, Jhonatan Jair ; Fagundes, Thais Bortotti; Silva, Mayara Antunes da Trindade; Skinner, Rodrigo; Ulsen, Carina ; Carneiro, Cleyton de Carvalho
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: PRÉ-SAL, MINERAIS, CARBONATOS, CARBONATOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERRARI, Jean Vicente et al. Influence of carbonate reservoir mineral heterogeneities on contact angle measurements. Journal of Petroleum Science and Engineering, v. 199, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2020.108313. Acesso em: 09 nov. 2025.
APA
Ferrari, J. V., Silveira, B. M. de O., Arismendi Florez, J. J., Fagundes, T. B., Silva, M. A. da T., Skinner, R., et al. (2021). Influence of carbonate reservoir mineral heterogeneities on contact angle measurements. Journal of Petroleum Science and Engineering, 199. doi:10.1016/j.petrol.2020.108313
NLM
Ferrari JV, Silveira BM de O, Arismendi Florez JJ, Fagundes TB, Silva MA da T, Skinner R, Ulsen C, Carneiro C de C. Influence of carbonate reservoir mineral heterogeneities on contact angle measurements [Internet]. Journal of Petroleum Science and Engineering. 2021 ;199[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2020.108313
Vancouver
Ferrari JV, Silveira BM de O, Arismendi Florez JJ, Fagundes TB, Silva MA da T, Skinner R, Ulsen C, Carneiro C de C. Influence of carbonate reservoir mineral heterogeneities on contact angle measurements [Internet]. Journal of Petroleum Science and Engineering. 2021 ;199[citado 2025 nov. 09 ] Available from: https://doi.org/10.1016/j.petrol.2020.108313
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: 09 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. 09 ] 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. 09 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382

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