Journal of Jilin University(Earth Science Edition) ›› 2018, Vol. 48 ›› Issue (1): 298-306.doi: 10.13278/j.cnki.jjuese.20160343

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A Method for Improving Accuracy of Reservoir Fracture Identification

Pan Baozhi, Liu Wenbin, Zhang Lihua, Guo Yuhang, Aruhan   

  1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
  • Received:2017-11-28 Online:2018-01-26 Published:2018-01-26
  • Supported by:
    Supported by National Nature Science Foundation(41174096) and the 12th Five-Year Major Projects(2011ZX05040-002)

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Abstract: In the exploration and development of oil field, it is always a difficult problem to accurately and efficiently identify fractures in reservoirs. The conventional well log is easy to identify the fracture but the accuracy is low. The imaging log has high resolution and high accuracy but complicated for artificial identification. In order to conquer the shortcomings of conventional and electrical imaging well log fracture identification, this paper presents an efficient and accurate method for reservoir fracture identification. The authors selected fracture samples based on the response of fractures in electric imaging log to establish conventional log comprehensive factor Y1 and imaging log factor Y2 for fracture recognition, then combined Y1 and Y2 to build reservoir fracture identification factor Y3. Y3 is used to identify fractures, which is more accurate than conventional log with the sampling interval 0.002 54 m which is much less than the conventional log interval 0.125 m. Compared with the electric imaging log, it can not only save time and labor but also increase the efficiency of the automatic fracture pick up. The method was applied to the identification of volcanic reservoir fractures in Wangfu area, which quickly and accurately identified the reservoir fractures, and its accuracy is about 80%. This can be a reference for the identification of other types of reservoir fractures.

Key words: fracture, comprehensive identification, imaging logging, conventional logging, volcanic reservoir

CLC Number: 

  • P631.8
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