CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut ***,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-di...
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CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut ***,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)*** is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal *** accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were *** algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control *** results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal *** contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff *** top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz *** research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.
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