A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting-Reference-Cited by-同舟云学术

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A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting

Published:2024-02 Issue: Volume:233 Page:212491
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Xu Ziming^ORCID,Leung Juliana Y.^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

China Scholarship Council

Publisher

Elsevier BV

Reference53 articles.

1. Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems;Abadi,2016

2. Strength, deformation and conductivity coupling of rock joints;Barton,1985

3. Canada energy regulator/Régie de l'énergie du Canada;CER,2022

4. Analysis of fracture interference – coupling of flow and geomechanical computations with discrete fracture modeling using MRST;Chen;J. Petrol. Sci. Eng.,2022

5. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling;Chung,2014

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network;SPE Journal;2024-07-01

2. Corrigendum to “A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting” [Geoenergy Sci. Eng., 233, February 2024, 212491];Geoenergy Science and Engineering;2024-02

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