Lithologic Reservoirs ›› 2025, Vol. 37 ›› Issue (6): 172-179.doi: 10.12108/yxyqc.20250616

• PETROLEUM ENGINEERING AND OIL & GAS FIELD DEVELOPMENT • Previous Articles    

Minimum miscible pressure model of CO2 flooding based on kernel ridgeregression multi-parameter optimization

LI Jiamin1,2, ZHANG Yizhong1,2, ZHANG Maolin1,2, QIN Bowen1,2, YANG Yuxin1,2   

  1. 1. Hubei Cooperative Innovation Center of Unconventional Oil & Gas, Yangtze University, Wuhan 430100, China;
    2. School of Petroleum Engineering, Yangtze University, Wuhan 430100, China
  • Received:2025-02-21 Revised:2025-03-20 Published:2025-11-07

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Abstract: Based on slim tubes experiment data, the grey corelation method was employed to identify the main controlling factors affecting CO2 oil recovery efficiency and determine their weight assignment. Kernel ridge re‐gression(KRR)algorithm was used to train parameter sets, with model hyperparameters optimized through ge‐netic algorithm(GA)and grid search(GS), the minimum miscible pressure(MMP)prediction model was established. The results show that: (1) The primary factors influencing CO2 flooding are reservoir temperature, crude oil composition, and injection gas composition. Under pure CO2 injection conditions, the order of grey corelation grade as follows: T > x(C2—C4) > M(C7+) > x(C5—C6) > x(CH4+N2). Under the condition of CO2 injection containing impurities, the impact ranking of impurity type and content on MMP is: x(N2) > x(C1) > x(C2—C4)inj > x(H2S).(2) Compared with Ridge model and ElasticNet model, KRR model exhibits higher prediction accuracy and lower error. Among them, KRR-GA model demonstrates the best overall performance, with the mean absolute percentage error(EMAP) of 4.11%, root mean square error(ERMS) of 0.856 MPa, and coefficient of determination(R2) of 0.981 on the test set.(3) KRR-GA model demonstrates superior applicability for heavy crude oil reservoirs and conventional black oil reservoirs, while KRR-GS model is more suitable for light crude oil reservoirs with high H2S content in the injection gas.

Key words: kernel ridge regression(KRR), minimum miscible pressure, CO2 flooding, grey correlationmethod, genetic algorithm, reservoir temperature, crude oil components, injection gas composition

CLC Number: 

  • TE341
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