Lithologic Reservoirs ›› 2019, Vol. 31 ›› Issue (1): 139-146.doi: 10.12108/yxyqc.20190116

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A new algorithm for non-gradient optimization of injection-production parameters for polymer flooding

XIE Xiaoqing1,2   

  1. 1. State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China;
    2. CNOOC Research Institute Ltd., Beijing 100028, China
  • Received:2018-04-27 Revised:2018-06-24 Online:2019-01-18 Published:2019-01-18

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Abstract: Non-gradient optimization algorithm for optimization algorithms theory research is meaningful and has been applied in many practical problems,and it can also be used to solve the optimal control model of injectionproduction parameters for polymer flooding. Aiming at the advantages and disadvantages of finite difference stochastic approximation(FDSA)and simultaneous perturbation stochastic approximation(SPSA),a simultaneous perturbation stochastic approximation algorithm guided by finite difference gradient(SPSA-FDG)was proposed. Based on CMG reservoir numerical simulation software,reservoir numerical simulation model was established, by using the SPSA-FDG,optimal control study of injection-production parameters for polymer flooding was carried out,and the optimized results with the three algorithms were analyzed. The results show that the convergence of SPSA-FDG algorithm is better than that of FDSA and SPSA algorithm,and it is easy to combine with any reservoir numerical simulator to solve the optimal control problem of polymer flooding. Compared with uniform polymer flooding scheme,it is found that by using the optimized scheme calculated by SPSA-FDG algorithm,polymer flooding efficiency time is ahead of schedule,the water cut drop funnel is deepened,the cumulative recovery degree is improved,and the overall reservoir development effect is better. SPSA-FDG algorithm can be applied in other chemical flooding fields to improve the effect of chemical flooding.

Key words: polymer flooding, non-gradient algorithm, injection-production parameter optimization, optimal control

CLC Number: 

  • TE357.46
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