Abstract: The dolomite reservoirs of the fourth member of the Majiagou Formation in the Ordos Basin are characterized by thin layers,tightness,complex pore shapes,strong heterogeneity,and relatively weak seismic responses.The mechanisms behind the seismic prediction of the reservoirs remains uncertain, making fluid identification challenging.Traditional geophysical methods based on a single attribute fail to accurately predict fluids.Therefore,by comprehensively accounting for the pore shapes and connectivity,this study proposed a new fluid identification method using petrophysical modeling and analysis,as well as wave theory-based prestack AVO inversion,frequency-dependent AVO inversion of fluid factors,and probabilistic neural network(PNN)-based prediction of pore structure parameters.This method,comprehensively considering the impacts of elastic parameters,physical parameters,and dispersion properties,achieve remarkable results.Compared to traditional single-attribute fluid identification method,the proposed method demonstrates higher accuracy,particularly for gas-bearing areas,fully verifying its effectiveness in fluid identification and highlighting its potential for widespread application.