Lithologic Reservoirs ›› 2012, Vol. 24 ›› Issue (1): 108-111.doi: 10.3969/j.issn.1673-8926.2012.01.021

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Prediction of minimum miscibility pressure in CO2 flooding based on general regression neural network

LI Hu， PU Chunsheng， WU Feipeng   

  1. College of Petroleum Engineering， China University of Petroleum， Dongying 257061， China
  • Online:2012-02-20 Published:2012-02-20

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Abstract:

To reduce the inconvenience and errors in traditional prediction methods for minimum miscibility pressure （MMP）, a general regression neural network（GRNN） model was established for MMP prediction in CO2 flooding. The main factors affecting CO2 MMP, such as reservoir temperature, mole percentage of oil components (volatile and intermediate） and molecular weight of C5+ , were employed as the input variables of the GRNN, and the CO2 MMP was used as the output variable. To evaluate the advantage of the new method, the predicted results were compared between the GRNN model and the traditional empirical formula. The results show that the GRNN model is feasible for CO2 MMP prediction, and also has the characteristics of good precision, fast convergence, wide applicability and simple application.

Key words: stochastic modeling, fluvial facies, experimental design, Monte-Carlo calculation, uncertainty analysis

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