Data-driven deep-learning forecasting for oil production and pressure

Werneck, Rafael de Oliveira Prates, Raphael Moura, Renato Goncalves, Maiara Moreira Castro, Manuel Soriano-Vargas, Aurea Mendes Junior, Pedro Ribeiro Hossain, M. Manzur Zampieri, Marcelo Ferreira Ferreira, Alexandre Davolio, Alessandra Schiozer, Denis Rocha, Anderson

Journal of Petroleum Science & Engineering2022，Vol.21016.DOI:10.1016/j.petrol.2021.109937

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NSTL
Elsevier

Data-driven deep-learning forecasting for oil production and pressure

Werneck, Rafael de Oliveira ¹Prates, Raphael ¹Moura, Renato ¹Goncalves, Maiara Moreira ¹Castro, Manuel ¹Soriano-Vargas, Aurea ¹Mendes Junior, Pedro Ribeiro ¹Hossain, M. Manzur ¹Zampieri, Marcelo Ferreira ¹Ferreira, Alexandre ¹Davolio, Alessandra ¹Schiozer, Denis ¹Rocha, Anderson¹

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作者信息

1. Univ Estadual Campinas
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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.

Key words

Forecasting/Data-driven/Deep learning/Oil production/Pre-salt/NEURAL-NETWORK/PREDICTION

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出版年

2022

Journal of Petroleum Science & Engineering

ISSN：0920-4105

被引量10

参考文献量48

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