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Hydraulic Fracture Design with a Proxy Model for Unconventional Shale Gas Reservoir with Considering Feasibility Study

Author

Listed:
  • Kyoungsu Kim

    (Department of Energy System Engineering, Seoul National University, Seoul 08826, Korea)

  • Jonggeun Choe

    (Department of Energy System Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

Shale gas is a natural gas trapped in shale formation and is being actively developed in North America. Due to the low permeability of a shale gas reservoir in the range from 10 −8 to 10 −6 Darcy, horizontal drilling and multi-stage hydraulic fracturing are needed for its development. This paper presents a fast and reliable proxy model to forecast shale gas productions and an optimum hydraulic fracturing design for its development. The proxy model uses a robust regression scheme and can replace a commercial reservoir simulator. The proxy model proposed can determine the influence of impact factors on the production at each production time. The calculation speed of the proposed proxy model is about 1.4 million times faster than that of a reservoir simulator compared. The most economical hydraulic fracture design using the proxy model has a length of 168 m at each stage, which is determined by examining a large number of hydraulic fracturing designs considering economic feasibility.

Suggested Citation

  • Kyoungsu Kim & Jonggeun Choe, 2019. "Hydraulic Fracture Design with a Proxy Model for Unconventional Shale Gas Reservoir with Considering Feasibility Study," Energies, MDPI, vol. 12(2), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:220-:d:196908
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    References listed on IDEAS

    as
    1. Chao Tang & Xiaofan Chen & Zhimin Du & Ping Yue & Jiabao Wei, 2018. "Numerical Simulation Study on Seepage Theory of a Multi-Section Fractured Horizontal Well in Shale Gas Reservoirs Based on Multi-Scale Flow Mechanisms," Energies, MDPI, vol. 11(9), pages 1-20, September.
    2. Jaejun Kim & Joe M. Kang & Changhyup Park & Yongjun Park & Jihye Park & Seojin Lim, 2017. "Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir," Energies, MDPI, vol. 10(4), pages 1-16, April.
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    Cited by:

    1. Dongkwon Han & Sunil Kwon, 2021. "Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs," Energies, MDPI, vol. 14(12), pages 1-24, June.
    2. Xiaogui Zhou & Haiming Liu & Yintong Guo & Lei Wang & Zhenkun Hou & Peng Deng, 2019. "An Evaluation Method of Brittleness Characteristics of Shale Based on the Unloading Experiment," Energies, MDPI, vol. 12(9), pages 1-24, May.

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