Four Methods to Estimate Minimum Miscibility Pressure of CO2-Oil Based on Machine Learning

作     者：Ding Li Xiangliang Li Yinghua Zhang Lixin Sun Shiling Yuan 

作者机构：Key Lab of Colloid and Interface ChemistryShandong UniversityJinanShandong 250100China Shengli Oil Field Exploration and Development Research InstituteDongyingShandong 257000China 

出 版 物：《Chinese Journal of Chemistry》 (中国化学（英文版）)

年 卷 期：2019年第37卷第12期

页      面：1271-1278页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：This work was supported by the National Natural Science Foundation of China(No.21573130) 

主　　题：prediction analysis similarity 

摘      要：Summary of main observation and conclusion CO2 flooding accounts for a considerable proportion in gas *** CO2 as a gas displacement agent is benefit for enhanced oil recovery(EOR),and the alleviation of the greenhouse effect by the permanent storage of CO2 in the *** miscibility pressure(MMP)of CO2-oil is a key factor affecting EOR,which determines the yield and economic benefit of crude oil ***,it is of great importance to use fast,accurate and cheap prediction methods for MMP *** the present study,to evaluate the reliability of four recently developed prediction models based on machine learning(i.e.,neural network analysis(NNA),genetic function approximation(GFA),multiple linear regression(MLR),partial least squares(PLS)),136 sets of data are selected for calculation via outlier analysis from 147 sets of ***,we compared the four models with existing prediction models from the *** analysis of correlation coefficients and multiple error functions shows that the four models can solve the MMP prediction problem well,and the model using intelligent algorithm has a higher prediction accuracy than the simple linear ***,intelligent methods based on similarity algorithm have little difference from each ***,a sensitivity analysis was conducted.