Abstract: Tight sandstone reservoirs deposited in braided river deltas are generally characterized by low porosity, low permeability, complex pore-throat structures, and strong heterogeneity. Conventional well log interpretation methods mostly rely on single parameters or empirical criteria, making it difficult to simultaneously characterize reservoir porosity and permeability, thus limiting the accuracy and interpretability of reservoir classification. To address these issues, this study proposes an intelligent well logging classification method for tight sandstone reservoirs based on flow zone index (FZI) constraints. First, core FZI is calculated using rock physics experimental data from the study area, and the cumulative frequency method is applied to classify FZI values and establish the classification criterion for reservoir indicator F. Then, reservoir porosity is calculated using the neutron–density crossplot method, and a permeability model is established from porosity–permeability experimental data to estimate reservoir permeability. Based on the calculated porosity and permeability, the logging-derived FZI and classification indicator F are further computed for the study area. Finally, conventional log curves, including GR, AC, DEN, CNL, and RD, together with the model-derived porosity, permeability, FZI, and F, are used as input data. F is incorporated with the conventional log curves to form the input feature vector, thereby imposing seepage-physics constraints on the clustering process. A Gaussian Mixture Model (GMM) is then employed to achieve intelligent classification of tight sandstone reservoirs and establish the corresponding classification criteria. The proposed method is applied to a typical braided-river-delta depositional area in the western margin of Well Pen-1 in the Junggar Basin, achieving fine classification of low-porosity and low-permeability reservoirs and clarifying the relationship between reservoir types and gas-bearing properties. The results demonstrate that this method effectively improves the accuracy of reservoir classification and shows strong potential for application to other tight sandstone reservoirs with low porosity and low permeability.