计算机科学
深度学习
人工智能
生产(经济)
滑动窗口协议
石油生产
机器学习
领域(数学)
堆积
数据挖掘
作者
Rafael de Oliveira Werneck,Raphael Prates,Renato Lopes Moura,Maiara Moreira Gonçalves,Manuel Castro,Aurea Soriano-Vargas,Pedro Ribeiro Mendes Júnior,Manzur Hossain,M. F. Zampieri,Alexandre Donizete Ferreira,Alessandra Davólio,Denis Schiozer,Anderson Rocha
标识
DOI:10.1016/j.petrol.2021.109937
摘要
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. • Evaluation of different setups for forecasting. • Proposal of a new setup for forecasting. • Evaluation of different pre-processing techniques. • Comparison with off-the-shelf techniques.
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