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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: 02 dez. 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 dez. 02 ] 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 dez. 02 ] Available from: https://doi.org/10.1016/j.petrol.2020.108080
Artigo de periodico
Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks (2021)
Oliveira, Lucas Abreu Blanes de; Custódio, Luiz Felipe Niedermaier; Fagundes, Thais Bortotti; Ulsen, Carina ; Carneiro, Cleyton de Carvalho
Source: Energy and AI. Unidade: EP
Subjects: GEOQUÍMICA, CONCENTRAÇÃO DE MINERAIS, MINERALOGIA, PRÉ-SAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Lucas Abreu Blanes de et al. Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks. Energy and AI, v. 3, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.egyai.2021.100050. Acesso em: 02 dez. 2025.
APA
Oliveira, L. A. B. de, Custódio, L. F. N., Fagundes, T. B., Ulsen, C., & Carneiro, C. de C. (2021). Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks. Energy and AI, 3, 1-13. doi:10.1016/j.egyai.2021.100050
NLM
Oliveira LAB de, Custódio LFN, Fagundes TB, Ulsen C, Carneiro C de C. Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks [Internet]. Energy and AI. 2021 ;3 1-13.[citado 2025 dez. 02 ] Available from: https://doi.org/10.1016/j.egyai.2021.100050
Vancouver
Oliveira LAB de, Custódio LFN, Fagundes TB, Ulsen C, Carneiro C de C. Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks [Internet]. Energy and AI. 2021 ;3 1-13.[citado 2025 dez. 02 ] Available from: https://doi.org/10.1016/j.egyai.2021.100050

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