Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs
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DOI: 10.1016/j.energy.2021.122747
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References listed on IDEAS
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Cited by:
- Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
- Xianlin Ma & Mengyao Hou & Jie Zhan & Zhenzhi Liu, 2023. "Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques," Energies, MDPI, vol. 16(9), pages 1-16, April.
- Zhou, Guangzhao & Duan, Xianggang & Chang, Jin & Bo, Yu & Huang, Yuhan, 2023. "Investigation of CH4/CO2 competitive adsorption-desorption mechanisms for enhanced shale gas production and carbon sequestration using nuclear magnetic resonance," Energy, Elsevier, vol. 278(PB).
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Keywords
Principal component analysis; Support vector regression; Absolute open flow potential; Shale gas;All these keywords.
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