Lithologic Reservoirs ›› 2020, Vol. 32 ›› Issue (1): 144-151.doi: 10.12108/yxyqc.20200116

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Prediction method of well pattern infilling effect for ultra-low permeability reservoir in X oilfield

ZHANG Yichao1, CHEN Minfeng1,2, QU Dan1, MAO Meifen1, YANG Ziyou1   

  1. 1. College of Petroleum Engineering, China University of Petroleum(Beijing), Beijing 102249, China;
    2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China
  • Received:2019-05-20 Revised:2019-08-15 Online:2020-01-21 Published:2019-11-22

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Abstract: The dynamic development effect prediction research based on actual reservoir well pattern infilling adjustment can provide adjustment basis for oilfield development planning. Based on the dynamic analysis of the development before and after well pattern infilling adjustment for ultra-low permeability reservoir in X-oilfield, the change law of infilling yield-increasing effect and its main influencing factors were determined. Based on the typical numerical model established to reflect the characteristics of actual reservoir,the response surface design method and stepwise regression method were applied to comprehensively study the impact of different factors on the yield-increasing effect after well patter infilling,and the prediction model of yield-increasing effect of well pattern infilling adjustment considering multi-factors and reflecting the characteristics of ultra-low permeability reservoirs was obtained. The practical application of the prediction model for the ultra-low permeability reservoir in X oilfield shows that the prediction accuracy is high,and it has strong pertinence and practicability. The method established to characterize the yield-increasing effect of infilling adjustment can provide reference for the prediction of infilling adjustment effect of ultra-low permeability reservoirs,and provide a reliable basis for the development planning of such reservoirs.

Key words: ultra-low permeability reservoir, well pattern infilling, inverted nine-spot method, yield-increasing effect, response surface design, prediction model

CLC Number: 

  • TE33+1.1
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