In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years’ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system(IPIS). Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content(Vsh), numbered rock type(RN), porosity(Φ), permeability(K), true resistivity(RT) and spontaneous-potential(SP). Secondly, Vsh, Φ and K are predicted from well logs through artificial neural networks(ANNs). Finally, all the six parameters are input into a neuro-fuzzy inference machine(NFIM) to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system’s accuracy and effectiveness. The recent lithology characteristics extend due to more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore complex structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years’ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil / water layers by conventional well log interpretation. This Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related related to reservoir measurements have been selected carefully. They are shale content (Vsh), numbered rock type (RN) , porosity (Φ), permeability (K), true resi Secondary, Vsh, Φ and K are predicted from well logs through artificial neural networks (ANNs). Finally, all the six parameters are input into a neuro-fuzzy inference machine (NFIM) to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system’s accuracy and effectiveness.