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A Big Data Method Based on Random BP Neural Network and Its Application for Analyzing Influencing Factors on Productivity of Shale Gas Wells

Author

Listed:
  • Qun Zhao

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Leifu Zhang

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Zhongguo Liu

    (R&D Department, China National Petroleum Corporation, Beijing 100007, China)

  • Hongyan Wang

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Jie Yao

    (Downhole Testing Company of CNPC Bohai Drilling Engineering Company Limited, Langfang 065099, China)

  • Xiaowei Zhang

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Rongze Yu

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Tianqi Zhou

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

  • Lixia Kang

    (PetroChina Research Institute of Petroleum Exploration and Development, Beijing 100083, China)

Abstract

In recent years, big data and artificial intelligence technology have developed rapidly and are now widely used in fields of geophysics, well logging, and well test analysis in the exploration and development of oil and gas. The development of shale gas requires a large number of production wells, so big data and artificial intelligence technology have inherent advantages for evaluating the productivity of gas wells and analyzing the influencing factors for a whole development block. To this end, this paper combines the BP neural network algorithm with random probability analysis to establish a big data method for analyzing the influencing factors on the productivity of shale gas wells, using artificial intelligence and in-depth extraction of relevant information to reduce the unstable results from single-factor statistical analysis and the BP neural network. We have modeled and analyzed our model with a large amount of data. Under standard well conditions, the influences of geological and engineering factors on the productivity of a gas well can be converted to the same scale for comparison. This can more intuitively and quantitatively reflect the influences of different factors on gas well productivity. Taking 100 production wells in the Changning shale gas block as a case, random BP neural network analysis shows that maximum EUR can be obtained when a horizontal shale gas well has a fracture coefficient of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections.

Suggested Citation

  • Qun Zhao & Leifu Zhang & Zhongguo Liu & Hongyan Wang & Jie Yao & Xiaowei Zhang & Rongze Yu & Tianqi Zhou & Lixia Kang, 2022. "A Big Data Method Based on Random BP Neural Network and Its Application for Analyzing Influencing Factors on Productivity of Shale Gas Wells," Energies, MDPI, vol. 15(7), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2526-:d:782893
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    References listed on IDEAS

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    1. Klaus Jaffe & Enrique ter Horst & Laura H Gunn & Juan Diego Zambrano & German Molina, 2020. "A network analysis of research productivity by country, discipline, and wealth," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-15, May.
    2. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
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    Cited by:

    1. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
    2. Wenbin Cai & Huiren Zhang & Zhimin Huang & Xiangyang Mo & Kang Zhang & Shun Liu, 2023. "Development and Analysis of Mathematical Plunger Lift Models of the Low-Permeability Sulige Gas Field," Energies, MDPI, vol. 16(3), pages 1-12, January.

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