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A Combination Approach of the Numerical Simulation and Data-Driven Analysis for the Impacts of Refracturing Layout and Time on Shale Gas Production

Author

Listed:
  • Chenhong Zhu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • J. G. Wang

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
    State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Na Xu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Liang

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Bowen Hu

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Peibo Li

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Refracturing can alleviate the rapid decline of shale gas production with a low drilling cost, but an appropriate fracture layout and optimal refracturing time have been unclear without a heavy computation load. This paper proposes a combination approach with a numerical simulation and data-driven analysis to quickly evaluate the impacts of the refracturing layout and refracturing time on shale gas production. Firstly, a multiphysical coupling model with the creep of natural fractures is established for the numerical simulation on shale gas production. Secondly, the effects of the refracturing layout and refracturing time on the shale gas production are investigated through a single factor sensitivity analysis, but this analysis cannot identify the fracture interaction. Thirdly, the influence of fractures interaction on shale gas production is explored through a combination of a global sensitivity analysis (GSA) and an artificial neural network (ANN). The GSA results observed that the adjacent fractures have more salient interferences, which means that a denser fracture network will not significantly increase the total gas production, or will reduce the contribution from each fracture, resulting in higher fracturing costs. The new fractures that are far from existing fractures have greater contributions to cumulative gas production. In addition, the optimal refracturing time varies with the refracturing layout and is optimally implemented within 2–3 years. A suitable refracturing scale and time should be selected, based on the remaining gas reserve. These results can provide reasonable insights for the refracturing design on the refracturing layout and optimal time. This ANN-GSA approach provides a fast evaluation for the optimization of the refracturing layout and time without enormous numerical simulations.

Suggested Citation

  • Chenhong Zhu & J. G. Wang & Na Xu & Wei Liang & Bowen Hu & Peibo Li, 2022. "A Combination Approach of the Numerical Simulation and Data-Driven Analysis for the Impacts of Refracturing Layout and Time on Shale Gas Production," Sustainability, MDPI, vol. 14(23), pages 1-30, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16072-:d:990532
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    References listed on IDEAS

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    1. Pier Francesco Orrù & Andrea Zoccheddu & Lorenzo Sassu & Carmine Mattia & Riccardo Cozza & Simone Arena, 2020. "Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    2. Przemyslaw Michal Wilczynski & Andrzej Domonik & Pawel Lukaszewski, 2021. "Brittle Creep and Viscoelastic Creep in Lower Palaeozoic Shales from the Baltic Basin, Poland," Energies, MDPI, vol. 14(15), pages 1-22, July.
    3. Fen Yang & Hossein Moayedi & Amir Mosavi, 2021. "Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
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