A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction

Guangzhao Zhou, Zanquan Guo, Simin Sun and Qingsheng Jin

Applied Energy, 2023, vol. 344, issue C, No S030626192300613X

Abstract: In the coming decades, the demand for shale oil will likely surge because of predicted increases in the global population and productivity. Efficiently predicting shale oil production is therefore critical for understanding both the reliability of applied unconventional resources and crude supply. A novel method was proposed to extract the spatial–temporal properties of production data for shale oil prediction on the basis of a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with an attention mechanism (AM). The strengths and advantages of the CNN layer were selected as the input variables affecting shale oil production. The BiGRU network provides new and more detailed temporal information or irregular trends in temporal series components. AM also contributes to understanding the impact of intrinsic information to guarantee learning accuracy. CNN-BiGRU-AM performs the desired behavior of evaluation indicators in contrast to conventional machine learning and deep learning for forecasting shale oil production. The profound impact of this work lies in delivering state-of-the-art research aids that highlight the large but uneven impact of shale oil production prediction.

Keywords: Shale oil; Convolutional neural network; Bidirectional gated recurrent unit; Attention mechanism; Production prediction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2023.121249

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