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Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs

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  • Wang, Lian
  • Yao, Yuedong
  • Wang, Kongjie
  • Adenutsi, Caspar Daniel
  • Zhao, Guoxiang
  • Lai, Fengpeng

Abstract

Forecasting productivity of shale gas has gained much attention in recent years. However, few researchers considered forecasting absolute open flow potential (AOFP). The conventional method of determining the AOFP of gas wells is through a systematic well testing which is time consuming and has adverse economic effects. Therefore, the objective of this research is to establish an efficient and applicable method for forecasting shale gas well AOFP. In this study, we combined one of the most classic unsupervised machine learning methods namely, principal component analysis (PCA) with least squares support vector regression (LSSVR) which is an efficient supervised machine learning method in forecasting the AOFP for shale gas wells. This method was named hybrid machine learning method based on PCA and LSSVR model (LSSVR-PCA). The proposed method was applied to the Weiyuan, Changning as well as Zhaotong shale gas reservoirs in the Sichuan basin, southwest China. Results showed that the proposed method could forecast AOFP with high accuracy and efficiency if adequate well data are accessible. This hybrid machine learning method also provides an intelligent approach for field engineers to forecast the AOFP and appropriately allocate production of gas wells.

Suggested Citation

  • Wang, Lian & Yao, Yuedong & Wang, Kongjie & Adenutsi, Caspar Daniel & Zhao, Guoxiang & Lai, Fengpeng, 2022. "Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221029960
    DOI: 10.1016/j.energy.2021.122747
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).
    3. 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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