Under the fan-delta sedimentary environment of Mahu Sag in Junggar Basin，the tight glutenite reservoirs feature complex internal structure，variable physical properties，and complicated spatial distribution. Therefore，the quantitative description for the variation rules of the spatial distribution，physical properties and oil content of the reservoirs is required.However，former studies have proved that it is difficult to solve the problem efficiently by routine seismic attribute analysis and post-stack or pre-stack seismic inversion methods. Thus，the extended elastic impedance（EEI）inversion technology is adopted for the quantitative prediction of tight glutenite reservoirs. By using petrophysical analysis based on logging data，we analyzed the correlation of geological parameters and EEI of multi-well target intervals with variation of Chi projection angle.In this way，the optimal Chi projection angle was determined，and the data of drilled wells were applied to make cross-plots. We performed high-resolution acoustic impedance（AI）and gradient impedance（GI）inversion by the joint method of amplitude versus offset（AVO）attribute analysis，pre-stack simultaneous inversion and stochastic inversion.Then，given the optimal Chi projection angle，the EEI attributes corresponding to reservoir parameters to be predicted were estimated.Finally，we made cross-plots with the reservoir parameters and estimated EEI attributes，fitted relationship expressions according to classes from the neural network clustering of lithofacies，calculated reservoir parameters，and corrected errors. The practical applications indicate that the key geological parameters of reservoirs such as physical properties and oil/gas content can be quantitatively and effectively described by the EEI inversion. The technology is conducive to raising the success rate of the prospecting and development of tight oil and gas reservoirs，which is worthy of trials and wide application.