Journal of Jilin University(Earth Science Edition) ›› 2018, Vol. 48 ›› Issue (4): 1244-1252.doi: 10.13278/j.cnki.jjuese.20170285

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Fracture Properties and Anisotropic Parameters Inversion of Shales Based on Rock Physics Model

Zhang Bing1, Guo Zhiqi1, Xu Cong2, Liu Cai1, Liu Xiwu3,4,5, Liu Yuwei3,4,5   

  1. 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
    2. Northeast Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Changchun 130021, China;
    3. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China;
    4. SinoPEC Key Laboratory of Shale Oil/Gas Exploration and Production Technology, Beijing 100083, China;
    5. SinoPEC Petroleum Exploration and Production Research Institute, Beijing 100083, China
  • Received:2017-10-26 Online:2018-07-26 Published:2018-07-26
  • Supported by:
    Supported by National Natural Science Foundation of China (41430322, 41404090), the NSFC and SinoPEC Joict key Project (U1663207) and National Key S & T Special Project of China (2017ZX05049-002)

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Abstract: Organic-rich shale reservoirs usually have complex mineral compositions and pore system in a small scale. This study presents a workflow of the fracture properties and anisotropy parameter inversion of shales based on the rock physics model and improved particle swarm optimization algorithm. A two-pore system for fractured shales was built through applying the self-consistent equivalent medium theory and Chapman's multi-scale porosity model. This inversion workflow can be used to estimate multi-parameter simultaneously by utilizing the simulated annealing-based particle swarm optimization algorithm. The intelligence algorithm has the ability to find global optimization and fast convergence. The workflow was applied in the Longmaxi shale-gas reservoir in the Sichuan basin to estimate the aspect ratio of pores, fracture density, and anisotropy parameters. The result is consistent with the existing research and provides diversified information for the shale reservoir evaluation.

Key words: shale reservoir, fracture properties, anisotropy, Chapman's model, particle swarm optimization algorithm, Sichuan basin

CLC Number: 

  • P631.4
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