Data-driven deep-learning forecasting for oil production and pressure
Rafael de Oliveira Werneck, Raphael Prates, Renato Moura, Maiara Moreira Gonçalves, Manuel Castro, Aurea Soriano-Vargas, Pedro Ribeiro Mendes Júnior, M. Manzur Hossain, Marcelo Ferreira Zampieri, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, Anderson Rocha
ARTIGO
Inglês
Agradecimentos: This work was conducted in association with the ongoing Project registered under ANP number 21373-6 as "Desenvolvimento de Técnicas de Aprendizado de Máquina para Análise de Dados Complexos de Produção de um Campo do Pre-Sal" (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil , under...
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Agradecimentos: This work was conducted in association with the ongoing Project registered under ANP number 21373-6 as "Desenvolvimento de Técnicas de Aprendizado de Máquina para Análise de Dados Complexos de Produção de um Campo do Pre-Sal" (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil , under the ANP RD levy as "Compromisso de Investimentos com Pesquisa e Desenvolvimento". The authors also thank Schlumberger and CMG for software licenses and Vitor Ferreira for helping with the PN-DCA method
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Abstract: Production forecasting plays an important role in oil and gas production, aiding engineers to perform field management. However, this can be challenging for complex reservoirs such as the highly heterogeneous carbonate reservoirs from Brazilian Pre-salt fields. We propose a new setup for...
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Abstract: Production forecasting plays an important role in oil and gas production, aiding engineers to perform field management. However, this can be challenging for complex reservoirs such as the highly heterogeneous carbonate reservoirs from Brazilian Pre-salt fields. We propose a new setup for forecasting multiple outputs using machine-learning algorithms and evaluate a set of deep-learning architectures suitable for time-series forecasting. The setup proposed is called N-th Day and it provides a coherent solution for the problem of forecasting multiple data points in which a sliding window mechanism guarantees there is no data leakage during training. We also devise four deep-learning architectures for forecasting, stacking the layers to focus on different timescales, and compare them with different existing off-the-shelf methods. The obtained results confirm that specific architectures, as those we propose, are crucial for oil and gas production forecasting. Although LSTM and GRU layers are designed to capture temporal sequences, the experiments also indicate that the investigated scenario of production forecasting requires additional and specific structures
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Fechado
Hossain, Mohammed Manzur
Autor
Data-driven deep-learning forecasting for oil production and pressure
Rafael de Oliveira Werneck, Raphael Prates, Renato Moura, Maiara Moreira Gonçalves, Manuel Castro, Aurea Soriano-Vargas, Pedro Ribeiro Mendes Júnior, M. Manzur Hossain, Marcelo Ferreira Zampieri, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, Anderson Rocha
Data-driven deep-learning forecasting for oil production and pressure
Rafael de Oliveira Werneck, Raphael Prates, Renato Moura, Maiara Moreira Gonçalves, Manuel Castro, Aurea Soriano-Vargas, Pedro Ribeiro Mendes Júnior, M. Manzur Hossain, Marcelo Ferreira Zampieri, Alexandre Ferreira, Alessandra Davólio, Denis Schiozer, Anderson Rocha
Fontes
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Journal of petroleum science and engineering v. 210, n. art. 109937, Mar. 2022 |