Filtros : "PINTO, MARCIO AUGUSTO SAMPAIO" Limpar

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  • Source: Geoenergy Science and Engineering. Unidade: EP

    Subjects: AQUÍFEROS, SEQUESTRO DE CARBONO

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      IZADPANAKI, Amin et al. A review of carbon storage in saline aquifers: Key obstacles and solutions. Geoenergy Science and Engineering, v. 250, p. 30 , 2025Tradução . . Disponível em: https://doi.org/10.1016/j.geoen.2025.213806. Acesso em: 02 nov. 2025.
    • APA

      Izadpanaki, A., Kumar, N., Tassinari, C. C. G., Ali, M., Ahmad, T., & Pinto, M. A. S. (2025). A review of carbon storage in saline aquifers: Key obstacles and solutions. Geoenergy Science and Engineering, 250, 30 . doi:10.1016/j.geoen.2025.213806
    • NLM

      Izadpanaki A, Kumar N, Tassinari CCG, Ali M, Ahmad T, Pinto MAS. A review of carbon storage in saline aquifers: Key obstacles and solutions [Internet]. Geoenergy Science and Engineering. 2025 ;250 30 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.geoen.2025.213806
    • Vancouver

      Izadpanaki A, Kumar N, Tassinari CCG, Ali M, Ahmad T, Pinto MAS. A review of carbon storage in saline aquifers: Key obstacles and solutions [Internet]. Geoenergy Science and Engineering. 2025 ;250 30 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.geoen.2025.213806
  • Source: Geosystem Engineering. Unidade: EP

    Subjects: RESERVATÓRIOS, POÇOS, POÇOS

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      FURLANETTI, Michael e GILDIN, Eduardo e PINTO, Marcio Augusto Sampaio. Gradient boosting trees feature importance for detecting interwell connectivity in an oilfield. Geosystem Engineering, p. 19 , 2025Tradução . . Disponível em: https://doi.org/10.1080/12269328.2025.2520551. Acesso em: 02 nov. 2025.
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      Furlanetti, M., Gildin, E., & Pinto, M. A. S. (2025). Gradient boosting trees feature importance for detecting interwell connectivity in an oilfield. Geosystem Engineering, 19 . doi:10.1080/12269328.2025.2520551
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      Furlanetti M, Gildin E, Pinto MAS. Gradient boosting trees feature importance for detecting interwell connectivity in an oilfield [Internet]. Geosystem Engineering. 2025 ;19 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1080/12269328.2025.2520551
    • Vancouver

      Furlanetti M, Gildin E, Pinto MAS. Gradient boosting trees feature importance for detecting interwell connectivity in an oilfield [Internet]. Geosystem Engineering. 2025 ;19 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1080/12269328.2025.2520551
  • Source: Energy and Power Engineering. Unidades: EP, RUSP, IEE

    Subjects: GÁS NATURAL, PETRÓLEO, CARBONO

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      BORGES, Cristiano Moura et al. Exploring the Synergy of Gas-Integrated Technologies in the Búzios Pre-Salt Field: A Case Study for a Pioneering Strategy in Early Monetization of Deep-Water Offshore Gas. Energy and Power Engineering, v. 17, n. 8, p. 191-216, 2025Tradução . . Disponível em: https://doi.org/10.4236/epe.2025.178011. Acesso em: 02 nov. 2025.
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      Borges, C. M., Arend, L., Costa, H. K. de M., Vieira, D. P., Hu, X., Feng, J., et al. (2025). Exploring the Synergy of Gas-Integrated Technologies in the Búzios Pre-Salt Field: A Case Study for a Pioneering Strategy in Early Monetization of Deep-Water Offshore Gas. Energy and Power Engineering, 17( 8), 191-216. doi:10.4236/epe.2025.178011
    • NLM

      Borges CM, Arend L, Costa HK de M, Vieira DP, Hu X, Feng J, Nishimoto K, Santos EM dos, Pinto MAS. Exploring the Synergy of Gas-Integrated Technologies in the Búzios Pre-Salt Field: A Case Study for a Pioneering Strategy in Early Monetization of Deep-Water Offshore Gas [Internet]. Energy and Power Engineering. 2025 ; 17( 8): 191-216.[citado 2025 nov. 02 ] Available from: https://doi.org/10.4236/epe.2025.178011
    • Vancouver

      Borges CM, Arend L, Costa HK de M, Vieira DP, Hu X, Feng J, Nishimoto K, Santos EM dos, Pinto MAS. Exploring the Synergy of Gas-Integrated Technologies in the Búzios Pre-Salt Field: A Case Study for a Pioneering Strategy in Early Monetization of Deep-Water Offshore Gas [Internet]. Energy and Power Engineering. 2025 ; 17( 8): 191-216.[citado 2025 nov. 02 ] Available from: https://doi.org/10.4236/epe.2025.178011
  • Source: Geoenergy Science and Engineering. Unidade: EP

    Subjects: RESERVATÓRIOS DE PETRÓLEO, POÇOS, PETRÓLEO

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      COIMBRA, Alexandre e PINTO, Marcio Augusto Sampaio. Reservoir characterization based on data incorporation throughout production development. Geoenergy Science and Engineering, v. 252, p. 15 , 2025Tradução . . Disponível em: https://doi.org/10.1016/j.geoen.2025.213911. Acesso em: 02 nov. 2025.
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      Coimbra, A., & Pinto, M. A. S. (2025). Reservoir characterization based on data incorporation throughout production development. Geoenergy Science and Engineering, 252, 15 . doi:10.1016/j.geoen.2025.213911
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      Coimbra A, Pinto MAS. Reservoir characterization based on data incorporation throughout production development. [Internet]. Geoenergy Science and Engineering. 2025 ; 252 15 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.geoen.2025.213911
    • Vancouver

      Coimbra A, Pinto MAS. Reservoir characterization based on data incorporation throughout production development. [Internet]. Geoenergy Science and Engineering. 2025 ; 252 15 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.geoen.2025.213911
  • Source: Journal Of Petroleum Exploration And Production Technology. Unidade: EP

    Subjects: RESERVATÓRIOS DE PETRÓLEO, POÇOS

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      ARANHA, Pedro Esteves e POLICARPO, Nara Angélica e SAMPAIO, Márcio Santos. Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production. Journal Of Petroleum Exploration And Production Technology, v. 14, p. 567-581, 2024Tradução . . Disponível em: https://doi.org/10.1007/s13202-023-01720-4. Acesso em: 02 nov. 2025.
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      Aranha, P. E., Policarpo, N. A., & Sampaio, M. S. (2024). Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production. Journal Of Petroleum Exploration And Production Technology, 14, 567-581. doi:10.1007/s13202-023-01720-4
    • NLM

      Aranha PE, Policarpo NA, Sampaio MS. Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production [Internet]. Journal Of Petroleum Exploration And Production Technology. 2024 ; 14 567-581.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-023-01720-4
    • Vancouver

      Aranha PE, Policarpo NA, Sampaio MS. Unsupervised machine learning model for predicting anomalies in subsurface safety valves and application in offshore wells during oil production [Internet]. Journal Of Petroleum Exploration And Production Technology. 2024 ; 14 567-581.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-023-01720-4
  • Source: Fuel. Unidade: EP

    Subjects: AQUÍFEROS, MECANISMOS, SEQUESTRO DE CARBONO

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      IZADPANAKI, Amin et al. A review of carbon storage in saline aquifers: Mechanisms, prerequisites, and key considerations. Fuel, v. 369, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.fuel.2024.131744. Acesso em: 02 nov. 2025.
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      Izadpanaki, A., Blunt, M. J., KUmar, N., Ali, M., Tassinari, C. C. G., & Pinto, M. A. S. (2024). A review of carbon storage in saline aquifers: Mechanisms, prerequisites, and key considerations. Fuel, 369. doi:10.1016/j.fuel.2024.131744
    • NLM

      Izadpanaki A, Blunt MJ, KUmar N, Ali M, Tassinari CCG, Pinto MAS. A review of carbon storage in saline aquifers: Mechanisms, prerequisites, and key considerations [Internet]. Fuel. 2024 ; 369[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.fuel.2024.131744
    • Vancouver

      Izadpanaki A, Blunt MJ, KUmar N, Ali M, Tassinari CCG, Pinto MAS. A review of carbon storage in saline aquifers: Mechanisms, prerequisites, and key considerations [Internet]. Fuel. 2024 ; 369[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.fuel.2024.131744
  • Source: Journal Of Petroleum Exploration And Production Technology. Unidade: EP

    Subjects: QUÍMICA INDUSTRIAL, PROCESSOS QUÍMICOS

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      SILVA, João Victor Gois et al. Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs. Journal Of Petroleum Exploration And Production Technology, v. 14, p. 2807-2831, 2024Tradução . . Disponível em: https://doi.org/10.1007/s13202-024-01855. Acesso em: 02 nov. 2025.
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      Silva, J. V. G., Silveira, B. M. de O., Ferrari, J. V., & Pinto, M. A. S. (2024). Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs. Journal Of Petroleum Exploration And Production Technology, 14, 2807-2831. doi:10.1007/s13202-024-01855-y
    • NLM

      Silva JVG, Silveira BM de O, Ferrari JV, Pinto MAS. Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs [Internet]. Journal Of Petroleum Exploration And Production Technology. 2024 ; 14 2807-2831.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-024-01855
    • Vancouver

      Silva JVG, Silveira BM de O, Ferrari JV, Pinto MAS. Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs [Internet]. Journal Of Petroleum Exploration And Production Technology. 2024 ; 14 2807-2831.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-024-01855
  • Source: SPE Journal. Unidade: EP

    Subjects: INDÚSTRIA PETROQUÍMICA, RESERVATÓRIOS DE PETRÓLEO

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      ARANHA, Pedro Esteves et al. A system to detect oilwell anomalies using deep learning and decision diagram dual approach. SPE Journal, v. 29, n. 3, p. 1540-1553, 2024Tradução . . Disponível em: https://doi.org/10.2118/218017-pa. Acesso em: 02 nov. 2025.
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      Aranha, P. E., Lopes, L. G. O. L., Paranhos Sobrinho, E., Oliveira, I. de M. N., Araújo, J. P. de, Santos, B. B. dos, et al. (2024). A system to detect oilwell anomalies using deep learning and decision diagram dual approach. SPE Journal, 29( 3), 1540-1553. doi:10.2118/218017-pa
    • NLM

      Aranha PE, Lopes LGOL, Paranhos Sobrinho E, Oliveira I de MN, Araújo JP de, Santos BB dos, Lima Junior E, Silva TB da, Vieira T, Lira WWM, Policarpo NA, Pinto MAS. A system to detect oilwell anomalies using deep learning and decision diagram dual approach [Internet]. SPE Journal. 2024 ; 29( 3): 1540-1553.[citado 2025 nov. 02 ] Available from: https://doi.org/10.2118/218017-pa
    • Vancouver

      Aranha PE, Lopes LGOL, Paranhos Sobrinho E, Oliveira I de MN, Araújo JP de, Santos BB dos, Lima Junior E, Silva TB da, Vieira T, Lira WWM, Policarpo NA, Pinto MAS. A system to detect oilwell anomalies using deep learning and decision diagram dual approach [Internet]. SPE Journal. 2024 ; 29( 3): 1540-1553.[citado 2025 nov. 02 ] Available from: https://doi.org/10.2118/218017-pa
  • Source: Computers & Geosciences. Unidade: EP

    Assunto: REDES E COMUNICAÇÃO DE DADOS

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      RANAZZI, Paulo Henrique e XIAODONG, Luo e PINTO, Marcio Augusto Sampaio. Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models. Computers & Geosciences, v. no 2024, p. 13 , 2024Tradução . . Disponível em: https://doi.org/10.1016/j.cageo.2024.105747. Acesso em: 02 nov. 2025.
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      Ranazzi, P. H., Xiaodong, L., & Pinto, M. A. S. (2024). Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models. Computers & Geosciences, no 2024, 13 . doi:10.1016/j.cageo.2024.105747
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      Ranazzi PH, Xiaodong L, Pinto MAS. Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models [Internet]. Computers & Geosciences. 2024 ; no 2024 13 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.cageo.2024.105747
    • Vancouver

      Ranazzi PH, Xiaodong L, Pinto MAS. Improving the training performance of generative adversarial networks with limited data: Application to the generation of geological models [Internet]. Computers & Geosciences. 2024 ; no 2024 13 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.cageo.2024.105747
  • Source: Proceedings. Conference titles: Offshore Technology Conference Brasil. Unidade: EP

    Subjects: GEOLOGIA ESTRUTURAL, INTELIGÊNCIA ARTIFICIAL, POÇOS, PRÉ-SAL

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      FERNANDES, M. A. e GILDIN, Eduardo e PINTO, Marcio Augusto Sampaio. Data-Driven workflow for categorization of brines applied to a pre-salt field. 2023, Anais.. Rio de Janeiro: Escola Politécnica, Universidade de São Paulo, 2023. Disponível em: https://doi.org/10.4043/32950-MS. Acesso em: 02 nov. 2025.
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      Fernandes, M. A., Gildin, E., & Pinto, M. A. S. (2023). Data-Driven workflow for categorization of brines applied to a pre-salt field. In Proceedings. Rio de Janeiro: Escola Politécnica, Universidade de São Paulo. doi:10.4043/32950-MS
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      Fernandes MA, Gildin E, Pinto MAS. Data-Driven workflow for categorization of brines applied to a pre-salt field [Internet]. Proceedings. 2023 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.4043/32950-MS
    • Vancouver

      Fernandes MA, Gildin E, Pinto MAS. Data-Driven workflow for categorization of brines applied to a pre-salt field [Internet]. Proceedings. 2023 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.4043/32950-MS
  • Source: Energies. Unidade: EP

    Subjects: PERMEABILIDADE DO SOLO, RESERVATÓRIOS, APRENDIZADO COMPUTACIONAL

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      REGINATO, Leonardo Fonseca e GIORIA, Rafael dos Santos e PINTO, Marcio Augusto Sampaio. Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection. Energies, v. 16, n. 13, 2023Tradução . . Disponível em: https://doi.org/10.3390/en16134849. Acesso em: 02 nov. 2025.
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      Reginato, L. F., Gioria, R. dos S., & Pinto, M. A. S. (2023). Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection. Energies, 16( 13). doi:10.3390/en16134849
    • NLM

      Reginato LF, Gioria R dos S, Pinto MAS. Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection [Internet]. Energies. 2023 ;16( 13):[citado 2025 nov. 02 ] Available from: https://doi.org/10.3390/en16134849
    • Vancouver

      Reginato LF, Gioria R dos S, Pinto MAS. Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection [Internet]. Energies. 2023 ;16( 13):[citado 2025 nov. 02 ] Available from: https://doi.org/10.3390/en16134849
  • Source: Computers & Structures. Unidade: EP

    Subjects: TOPOLOGIA, MÉTODOS TOPOLÓGICOS, FLAMBAGEM

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      MENDES, Eduardo Aguiar et al. Topology optimization for stability problems of submerged structures using the TOBS method. Computers & Structures, v. 259, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.compstruc.2021.106685. Acesso em: 02 nov. 2025.
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      Mendes, E. A., Sivapuram, R., Rodríguez, R. Q., Pinto, M. A. S., & Picelli, R. R. (2022). Topology optimization for stability problems of submerged structures using the TOBS method. Computers & Structures, 259. doi:10.1016/j.compstruc.2021.106685
    • NLM

      Mendes EA, Sivapuram R, Rodríguez RQ, Pinto MAS, Picelli RR. Topology optimization for stability problems of submerged structures using the TOBS method [Internet]. Computers & Structures. 2022 ; 259[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.compstruc.2021.106685
    • Vancouver

      Mendes EA, Sivapuram R, Rodríguez RQ, Pinto MAS, Picelli RR. Topology optimization for stability problems of submerged structures using the TOBS method [Internet]. Computers & Structures. 2022 ; 259[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.compstruc.2021.106685
  • Source: Fuel. Unidade: EP

    Assunto: RESERVATÓRIOS DE PETRÓLEO

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      KUMAR, Narenda et al. Fundamental aspects, mechanisms and emerging possibilities of CO2 miscible flooding in enhanced oil recovery: a review. Fuel, v. 330, p. 22 , 2022Tradução . . Disponível em: https://doi.org/10.1016/j.fuel.2022.125633. Acesso em: 02 nov. 2025.
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      Kumar, N., Pinto, M. A. S., Ojha, K., Hoteit, H., & Mandal, A. (2022). Fundamental aspects, mechanisms and emerging possibilities of CO2 miscible flooding in enhanced oil recovery: a review. Fuel, 330, 22 . doi:10.1016/j.fuel.2022.125633
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      Kumar N, Pinto MAS, Ojha K, Hoteit H, Mandal A. Fundamental aspects, mechanisms and emerging possibilities of CO2 miscible flooding in enhanced oil recovery: a review [Internet]. Fuel. 2022 ; 330 22 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.fuel.2022.125633
    • Vancouver

      Kumar N, Pinto MAS, Ojha K, Hoteit H, Mandal A. Fundamental aspects, mechanisms and emerging possibilities of CO2 miscible flooding in enhanced oil recovery: a review [Internet]. Fuel. 2022 ; 330 22 .[citado 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.fuel.2022.125633
  • Source: Journal of Petroleum Science and Engineering. Unidade: EP

    Subjects: RESERVATÓRIOS DE PETRÓLEO, FILTROS DE KALMAN

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      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: 02 nov. 2025.
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      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
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      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 2025 nov. 02 ] 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 2025 nov. 02 ] Available from: https://doi.org/10.1016/j.petrol.2022.110589
  • Source: SPE Production & Operations -. Unidade: EP

    Subjects: RESERVATÓRIOS DE PETRÓLEO, CARBONATOS

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      GOMES FILHO, Luiz Carlos e PINTO, Marcio Augusto Sampaio. Well production optimization under the scale effect and CO2-WAG Injection in a carbonate model of the Brazilian pre-salt. SPE Production & Operations -, 2022Tradução . . Disponível em: https://doi.org/10.2118/212269-PA. Acesso em: 02 nov. 2025.
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      Gomes Filho, L. C., & Pinto, M. A. S. (2022). Well production optimization under the scale effect and CO2-WAG Injection in a carbonate model of the Brazilian pre-salt. SPE Production & Operations -. doi:10.2118/212269-PA
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      Gomes Filho LC, Pinto MAS. Well production optimization under the scale effect and CO2-WAG Injection in a carbonate model of the Brazilian pre-salt [Internet]. SPE Production & Operations -. 2022 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.2118/212269-PA
    • Vancouver

      Gomes Filho LC, Pinto MAS. Well production optimization under the scale effect and CO2-WAG Injection in a carbonate model of the Brazilian pre-salt [Internet]. SPE Production & Operations -. 2022 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.2118/212269-PA
  • Source: Oil & Gas Science and Technology. Unidade: EP

    Subjects: RESERVATÓRIOS, INJEÇÃO (ENGENHARIA), CARBONATOS

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      REGINATO, Leonardo Fonseca et al. Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA. Oil & Gas Science and Technology, v. 76, 2021Tradução . . Disponível em: https://doi.org/10.2516/ogst/2020094. Acesso em: 02 nov. 2025.
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      Reginato, L. F., Pedroni, L. G., Compan, A. L. M., Skinner, R., & Pinto, M. A. S. (2021). Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA. Oil & Gas Science and Technology, 76. doi:10.2516/ogst/2020094
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      Reginato LF, Pedroni LG, Compan ALM, Skinner R, Pinto MAS. Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA [Internet]. Oil & Gas Science and Technology. 2021 ; 76[citado 2025 nov. 02 ] Available from: https://doi.org/10.2516/ogst/2020094
    • Vancouver

      Reginato LF, Pedroni LG, Compan ALM, Skinner R, Pinto MAS. Optimization of ionic concentrations in engineered water injection in carbonate reservoir through ANN and FGA [Internet]. Oil & Gas Science and Technology. 2021 ; 76[citado 2025 nov. 02 ] Available from: https://doi.org/10.2516/ogst/2020094
  • Source: Proceedings. Conference titles: Ibero-Latin-American Congress on Computational Methods in Engineering - CILAMCE. Unidades: IF, EP

    Subjects: TOPOLOGIA, MÉTODO DOS ELEMENTOS FINITOS

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      CASTRO, Thaís Almeida Ribeiro de et al. A geometry trimming approach for topology optimization of acoustic problems. Proceedings. Belo Horizonte: Associação Brasileira de Métodos Computacionais em Engenharia. Disponível em: https://cilamce.com.br/anais/arearestrita/apresentacoes/236/9339.pdf. Acesso em: 02 nov. 2025. , 2021
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      Castro, T. A. R. de, Sivapuram, R., Andrade, M. A. B., Pinto, M. A. S., & Sanches, R. P. (2021). A geometry trimming approach for topology optimization of acoustic problems. Proceedings. Belo Horizonte: Associação Brasileira de Métodos Computacionais em Engenharia. Recuperado de https://cilamce.com.br/anais/arearestrita/apresentacoes/236/9339.pdf
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      Castro TAR de, Sivapuram R, Andrade MAB, Pinto MAS, Sanches RP. A geometry trimming approach for topology optimization of acoustic problems [Internet]. Proceedings. 2021 ;[citado 2025 nov. 02 ] Available from: https://cilamce.com.br/anais/arearestrita/apresentacoes/236/9339.pdf
    • Vancouver

      Castro TAR de, Sivapuram R, Andrade MAB, Pinto MAS, Sanches RP. A geometry trimming approach for topology optimization of acoustic problems [Internet]. Proceedings. 2021 ;[citado 2025 nov. 02 ] Available from: https://cilamce.com.br/anais/arearestrita/apresentacoes/236/9339.pdf
  • Source: Journal of Petroleum Exploration and Production Technology. Unidade: EP

    Subjects: RESERVATÓRIOS DE PETRÓLEO, DIÓXIDO DE CARBONO

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    • ABNT

      PINTO, Marcio Augusto Sampaio e MELLO, Samuel Ferreira de e SCHIOZER, Denis José. Impact of physical phenomena and cyclical reinjection in miscible CO2-WAG recovery in carbonate reservoirs. Journal of Petroleum Exploration and Production Technology, v. 10, p. 3865–3881, 2020Tradução . . Disponível em: https://doi.org/10.1007/s13202-020-00925-1. Acesso em: 02 nov. 2025.
    • APA

      Pinto, M. A. S., Mello, S. F. de, & Schiozer, D. J. (2020). Impact of physical phenomena and cyclical reinjection in miscible CO2-WAG recovery in carbonate reservoirs. Journal of Petroleum Exploration and Production Technology, 10, 3865–3881. doi:10.1007/s13202-020-00925-1
    • NLM

      Pinto MAS, Mello SF de, Schiozer DJ. Impact of physical phenomena and cyclical reinjection in miscible CO2-WAG recovery in carbonate reservoirs [Internet]. Journal of Petroleum Exploration and Production Technology. 2020 ; 10 3865–3881.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-020-00925-1
    • Vancouver

      Pinto MAS, Mello SF de, Schiozer DJ. Impact of physical phenomena and cyclical reinjection in miscible CO2-WAG recovery in carbonate reservoirs [Internet]. Journal of Petroleum Exploration and Production Technology. 2020 ; 10 3865–3881.[citado 2025 nov. 02 ] Available from: https://doi.org/10.1007/s13202-020-00925-1
  • Source: Oil & Gas Science and Technology. Unidade: EP

    Subjects: COMPLETAÇÃO, RESERVATÓRIOS

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    • ABNT

      SCHAEFER, Bruno Cruz e PINTO, Marcio Augusto Sampaio. Efficient workflow for optimizing intelligent well completion using production parameters in real-time. Oil & Gas Science and Technology, v. 75, n. 69, 2020Tradução . . Disponível em: https://doi.org/10.2516/ogst/2020061. Acesso em: 02 nov. 2025.
    • APA

      Schaefer, B. C., & Pinto, M. A. S. (2020). Efficient workflow for optimizing intelligent well completion using production parameters in real-time. Oil & Gas Science and Technology, 75( 69). doi:10.2516/ogst/2020061
    • NLM

      Schaefer BC, Pinto MAS. Efficient workflow for optimizing intelligent well completion using production parameters in real-time [Internet]. Oil & Gas Science and Technology. 2020 ; 75( 69):[citado 2025 nov. 02 ] Available from: https://doi.org/10.2516/ogst/2020061
    • Vancouver

      Schaefer BC, Pinto MAS. Efficient workflow for optimizing intelligent well completion using production parameters in real-time [Internet]. Oil & Gas Science and Technology. 2020 ; 75( 69):[citado 2025 nov. 02 ] Available from: https://doi.org/10.2516/ogst/2020061
  • Source: Proceedings. Conference titles: Rio Oil & Gas Expo and Conference. Unidades: IEE, EP

    Subjects: RESERVATÓRIOS, CARBONO

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      MASULINO, Nathália Weber Neiva e TASSINARI, Colombo Celso Gaeta e PINTO, Marcio Augusto Sampaio. Avaliação técnico-econômica para armazenamento de CO2 em reservatórios não convencionais da bacia do Paraná. Proceedings. Rio de Janeiro: IBP. Disponível em: https://doi.org/10.48072/2525-7579.rog.2020.537. Acesso em: 02 nov. 2025. , 2020
    • APA

      Masulino, N. W. N., Tassinari, C. C. G., & Pinto, M. A. S. (2020). Avaliação técnico-econômica para armazenamento de CO2 em reservatórios não convencionais da bacia do Paraná. Proceedings. Rio de Janeiro: IBP. doi:10.48072/2525-7579.rog.2020.537
    • NLM

      Masulino NWN, Tassinari CCG, Pinto MAS. Avaliação técnico-econômica para armazenamento de CO2 em reservatórios não convencionais da bacia do Paraná [Internet]. Proceedings. 2020 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.48072/2525-7579.rog.2020.537
    • Vancouver

      Masulino NWN, Tassinari CCG, Pinto MAS. Avaliação técnico-econômica para armazenamento de CO2 em reservatórios não convencionais da bacia do Paraná [Internet]. Proceedings. 2020 ;[citado 2025 nov. 02 ] Available from: https://doi.org/10.48072/2525-7579.rog.2020.537

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