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A framework for predicting the production performance of unconventional resources using deep learning

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  • Wang, Sen
  • Qin, Chaoxu
  • Feng, Qihong
  • Javadpour, Farzam
  • Rui, Zhenhua

Abstract

Predicting the production performance of multistage fractured horizontal wells is essential for developing unconventional resources such as shale gas and oil. Accurate predictions of the production performance of wells that have not been put into production are necessary to optimize hydraulic fracture parameters prior to operation. However, traditional analytic methods are made inefficient by their strong dependency on historical production data and their huge computational expense. To conquer this issue, we developed deep belief network (DBN) models to predict the production performance of unconventional wells effectively and accurately. We ran 815 numerical simulation cases to construct a database for model training and optimized the hyperparameters of our network model using the Bayesian optimization algorithm. DBN models exhibit greater prediction accuracy and generalization ability than traditional machine-learning techniques such as back-propagation (BP) neural networks, and support vector regression (SVR). We also used the trained DBN model as a proxy to optimize the fracturing design and obtained outstanding results. Our proposed model could predict the production performance of an unconventional well instantaneously with considerable accuracy and shows excellent reusability, making it a powerful tool in optimizing fracturing designs. Our work lays a solid basis for anticipating the production performance of unconventional reservoirs and sheds light on the construction of data-driven models in the areas of energy conversion and utilization.

Suggested Citation

  • Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:appene:v:295:y:2021:i:c:s0306261921004827
    DOI: 10.1016/j.apenergy.2021.117016
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    4. Yang, Jiuqiang & Lin, Niantian & Zhang, Kai & Fu, Chao & Zhang, Chong, 2024. "Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis," Energy, Elsevier, vol. 299(C).
    5. Zhou, Guangzhao & Guo, Zanquan & Sun, Simin & Jin, Qingsheng, 2023. "A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction," Applied Energy, Elsevier, vol. 344(C).
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    7. Yang, Run & Liu, Xiangui & Yu, Rongze & Hu, Zhiming & Duan, Xianggang, 2022. "Long short-term memory suggests a model for predicting shale gas production," Applied Energy, Elsevier, vol. 322(C).
    8. Zekun Guo & Hongjun Wang & Xiangwen Kong & Li Shen & Yuepeng Jia, 2021. "Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation," Energies, MDPI, vol. 14(17), pages 1-17, September.
    9. Łukasz Klimkowski, 2024. "An Artificial Neural Network Model for a Comprehensive Assessment of the Production Performance of Multiple Fractured Unconventional Tight Gas Wells," Energies, MDPI, vol. 17(13), pages 1-26, June.
    10. Du, Shuyi & Wang, Jiulong & Wang, Meizhu & Yang, Jiaosheng & Zhang, Cong & Zhao, Yang & Song, Hongqing, 2023. "A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns," Energy, Elsevier, vol. 263(PE).
    11. Xianmin Zhang & Jiawei Ren & Qihong Feng & Xianjun Wang & Wei Wang, 2021. "Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm," Energies, MDPI, vol. 14(20), pages 1-16, October.
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