Journal of Jilin University(Earth Science Edition) ›› 2017, Vol. 47 ›› Issue (4): 1295-1307.doi: 10.13278/j.cnki.jjuese.201704305

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Estimating Porosity Spectrum of Fracture and Karst Cave from Conductivity Image by Morphological Filtering

Li Zhenling1, Shen Jinsong2, Li Xining2, Wang Lei2, Dan Weining3, Guo Sen1, Zhu Zhongmin2, Yu Renjiang3   

  1. 1. China Petroleum Well Logging Company, CNPC, Xi'an 710077, China;
    2. Faculty of Geophysics and Informatics, China University of Petroleum, Beijing 102249, China;
    3. Research Institude of Exploration and Development, CNPC Huabei Oilfield Company, Renqiu 062552, Hebei, China
  • Received:2016-11-22 Online:2017-07-26 Published:2017-07-26
  • Supported by:
    Supported by Major Science and Technology Special Project of China National Petroleum Corporation (2017E-15)

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Abstract: From the electrical image logging data, which has complete coverage and high resolution, by adoption of the multi-scale morphology method, the total porosity volume has been separated into matrix porosity, fracture porosity and karst cave porosity, and the porosity spectrum of the fracture and karst cave has been derived as well. Firstly, the response modes of the FMI (formation microscanner image) corresponding to various fractures and karst caves were analyzed. Secondly, operators of mathematical morphology were introduced, and the method of structuring element selection and filtering operator construction were proposed to improve signal-noise ratio and identify conductivity anomaly from the FMI measurements. After that, based on the edge detection of the conductivity anomaly that were formed by fracture and karst caves, the detection results of karst caves were fitted with elliptic or polygonal functions, and the fracture results were fitted by polynomial. Thus, fracture and karst cave parameters, as well as the spectrum of porosity were deduced from the fitted edge detection results. Finelly, examples of numerical simulation data and field data were provided for the verification of the effectiveness and stability of the multi-scale morphology method in application of FMI processing.

Key words: electric imaging logging, multi-scale morphological filtering, structure element, edge detection of fracture and karst cave anomalies, porosity spectrum of fracture and karst cave

CLC Number: 

  • P631.8
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