Lithologic Reservoirs ›› 2020, Vol. 32 ›› Issue (2): 115-121.doi: 10.12108/yxyqc.20200212

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Fracture classification method based on proximal support vector machine

HE Jian1,2, WU Gang3, NIE Wenliang1,2, LIU Songming1,2, HUANG Wei1,2   

  1. 1. Geophysical Institute, Chengdu University of Technology, Chengdu 610059, China;
    2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China;
    3. Research Institute of Exploration and Development, Shengli Oilfield Company, Sinopec, Dongying 257015, Shandong, China
  • Received:2019-03-14 Revised:2019-05-27 Online:2020-03-21 Published:2020-01-19

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Abstract: Fractured oil and gas reservoirs are widely found in all kinds of rock formations,how to make fine characterization and comprehensive prediction of fracture zone is the key to the exploration of fractured oil and gas reservoirs. In order to avoid the problem of multiple solutions,many scholars usually use multi-attribute to predict it synthetically. However,there are many complex nonlinear relationships between the development degree of cracks and some seismic attributes,the effective use of the correspondences for nonlinear prediction is also a difficult problem in the comprehensive prediction of fracture zones. The proximal support vector machine algorithm was introduced into the classification of fracture zone,and the nonlinear model between three kinds of seismic properties depicting reservoir fracture zone and crack development information in well were established, and the best discriminating rule reflecting the characteristics of fracture zone was obtained. Based on the rule, multiple seismic attributes can be comprehensively discriminated,the multi-solution of single attribute was overcome,and the classification accuracy of reservoir fracture zone was improved. Example showed that the algorithm weakened the limitation of identifying reservoir fracture zones by single factor classification,and it can provide a new research idea for the accurate classification of the development of fracture zones in rock formations.

Key words: fracture classification method, nonlinear model, proximal support vector machine, multi-attribute

CLC Number: 

  • P631.4
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