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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
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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: 25 set. 2024.
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 2024 set. 25 ] 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 2024 set. 25 ] 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
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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: 25 set. 2024.
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 2024 set. 25 ] 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 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2022.111061
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
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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: 25 set. 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 set. 25 ] 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 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2022.110589
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
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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: 25 set. 2024.
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 2024 set. 25 ] 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 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2020.108080
Artigo de periodico
An experimental study of gas solubility in glycerin based drilling fluid applied to well control (2021)
Duarte, Antonio Carlos Magalhães; Ribeiro, Paulo R; KIM, Nilo Ricardo; Mendes, José Ricardo Pelaquim; Policarpo, Nara Angélica; Vianna, Angelo
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: PERFURAÇÃO DE POÇOS, FLUÍDOS DE PERFURAÇÃO, SOLUBILIDADE
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ABNT
DUARTE, Antonio Carlos Magalhães et al. An experimental study of gas solubility in glycerin based drilling fluid applied to well control. Journal of Petroleum Science and Engineering, v. 207, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2021.109194. Acesso em: 25 set. 2024.
APA
Duarte, A. C. M., Ribeiro, P. R., KIM, N. R., Mendes, J. R. P., Policarpo, N. A., & Vianna, A. (2021). An experimental study of gas solubility in glycerin based drilling fluid applied to well control. Journal of Petroleum Science and Engineering, 207. doi:10.1016/j.petrol.2021.109194
NLM
Duarte ACM, Ribeiro PR, KIM NR, Mendes JRP, Policarpo NA, Vianna A. An experimental study of gas solubility in glycerin based drilling fluid applied to well control [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 207[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2021.109194
Vancouver
Duarte ACM, Ribeiro PR, KIM NR, Mendes JRP, Policarpo NA, Vianna A. An experimental study of gas solubility in glycerin based drilling fluid applied to well control [Internet]. Journal of Petroleum Science and Engineering. 2021 ; 207[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2021.109194
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
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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: 25 set. 2024.
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 2024 set. 25 ] 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 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2020.108313
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
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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: 25 set. 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 set. 25 ] 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 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2020.1074342
Artigo de periodico
Influence of the Kalman gain localization in adaptive ensemble smoother history matching (2019)
Ranazzi, Paulo Henrique; Pinto, Marcio Augusto Sampaio
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Assunto: RESERVATÓRIOS DE PETRÓLEO
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ABNT
RANAZZI, Paulo Henrique e PINTO, Marcio Augusto Sampaio. Influence of the Kalman gain localization in adaptive ensemble smoother history matching. Journal of Petroleum Science and Engineering, v. 179, p. 244 - 256, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2019.04.079. Acesso em: 25 set. 2024.
APA
Ranazzi, P. H., & Pinto, M. A. S. (2019). Influence of the Kalman gain localization in adaptive ensemble smoother history matching. Journal of Petroleum Science and Engineering, 179, 244 - 256. doi:10.1016/j.petrol.2019.04.079
NLM
Ranazzi PH, Pinto MAS. Influence of the Kalman gain localization in adaptive ensemble smoother history matching [Internet]. Journal of Petroleum Science and Engineering. 2019 ; 179 244 - 256.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2019.04.079
Vancouver
Ranazzi PH, Pinto MAS. Influence of the Kalman gain localization in adaptive ensemble smoother history matching [Internet]. Journal of Petroleum Science and Engineering. 2019 ; 179 244 - 256.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2019.04.079
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
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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: 25 set. 2024.
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 2024 set. 25 ] 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 2024 set. 25 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382

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