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Characterization and Prediction of Complex Natural Fractures in the Tight Conglomerate Reservoirs: A Fractal Method

Author

Listed:
  • Lei Gong

    (College of Geosciences, Northeast Petroleum University, Daqing 163318, China)

  • Xiaofei Fu

    (College of Geosciences, Northeast Petroleum University, Daqing 163318, China)

  • Shuai Gao

    (College of Geosciences, Northeast Petroleum University, Daqing 163318, China)

  • Peiqiang Zhao

    (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Qingyong Luo

    (State Key Laboratory of Petroleum Resource and Prospecting in China University of Petroleum, Beijing 100083, China)

  • Lianbo Zeng

    (State Key Laboratory of Petroleum Resource and Prospecting in China University of Petroleum, Beijing 100083, China)

  • Wenting Yue

    (Department of Overseas Strategy & Production Planning Research in CNPC International Research Center, Beijing 100083, China)

  • Benjian Zhang

    (Northwest Oil and Gas Field of Southwest Oil & Gas field Company, PetroChina, Jiangyou 621709, China)

  • Bo Liu

    (College of Geosciences, Northeast Petroleum University, Daqing 163318, China)

Abstract

Using the conventional fracture parameters is difficult to characterize and predict the complex natural fractures in the tight conglomerate reservoirs. In order to quantify the fracture behaviors, a fractal method was presented in this work. Firstly, the characteristics of fractures were depicted, then the fracture fractal dimensions were calculated using the box-counting method, and finally the geological significance of the fractal method was discussed. Three types of fractures were identified, including intra-gravel fractures, gravel edge fractures and trans-gravel fractures. The calculations show that the fracture fractal dimensions distribute between 1.20 and 1.50 with correlation coefficients being above 0.98. The fracture fractal dimension has exponential correlation with the fracture areal density, porosity and permeability and can therefore be used to quantify the fracture intensity. The apertures of micro-fractures are distributed between 10 μm and 100 μm, while the apertures of macro-fractures are distributed between 50 μm and 200 μm. The areal densities of fractures are distributed between 20.0 m·m −2 and 50.0 m·m −2 , with an average of 31.42 m·m −2 . The cumulative frequency distribution of both fracture apertures and areal densities follow power law distribution. The fracture parameters at different scales can be predicted by extrapolating these power law distributions.

Suggested Citation

  • Lei Gong & Xiaofei Fu & Shuai Gao & Peiqiang Zhao & Qingyong Luo & Lianbo Zeng & Wenting Yue & Benjian Zhang & Bo Liu, 2018. "Characterization and Prediction of Complex Natural Fractures in the Tight Conglomerate Reservoirs: A Fractal Method," Energies, MDPI, vol. 11(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2311-:d:167297
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    Cited by:

    1. Jianchao Cai & Shuyu Sun & Ali Habibi & Zhien Zhang, 2019. "Emerging Advances in Petrophysics: Porous Media Characterization and Modeling of Multiphase Flow," Energies, MDPI, vol. 12(2), pages 1-5, January.
    2. Xixin Wang & Jiagen Hou & Yuming Liu & Ling Ji & Jian Sun & Xun Gong, 2018. "Impacts of the Base-Level Cycle on Pore Structure of Mouth Bar Sand Bars: A Case Study of the Paleogene Kongdian Formation, Bohai Bay Basin, China," Energies, MDPI, vol. 11(10), pages 1-16, October.
    3. Guoping Liu & Lianbo Zeng & Chunyuan Han & Mehdi Ostadhassan & Wenya Lyu & Qiqi Wang & Jiangwei Zhu & Fengxiang Hou, 2020. "Natural Fractures in Carbonate Basement Reservoirs of the Jizhong Sub-Basin, Bohai Bay Basin, China: Key Aspects Favoring Oil Production," Energies, MDPI, vol. 13(18), pages 1-23, September.

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