In fluvial sedimentary environments, tight sandstone gas reservoirs exhibit complex characteristics, such as multi-phase vertical stacking, rapid lateral distribution variations, and pronounced heterogeneity, which pose substantial challenges for three-dimensional geological modeling. As development transitions to a stable production phase, the distribution of residual gas becomes increasingly intricate and dispersed, demanding high-precision geological models to guide resource exploitation. To enhance the accuracy of reservoir models, a study was conducted in the SF well area of the central Sulige Gas Field. Traditional modeling techniques, which primarily relied on vertical well data, were refined by incorporating horizontal well data as constraints, thus enabling precise geological modeling. By analyzing the intersections of horizontal sections with geological layers and introducing virtual wells to control structural planes, the accuracy of subtle inter-well structures was significantly enhanced. Statistical comparisons between well types and sandstone encounter rates allowed for the objective adjustment of sandstone distribution probabilities within the geological model, mitigating the optimistic bias often present in reservoir predictions. The 3D inner core declustering method was employed to construct a lithofacies model, effectively reducing the statistical bias caused by the high-density planar distribution of horizontal well data, thereby further enhancing the validity and accuracy of reservoir predictions. This method notably increased the predictive accuracy of geological models under conditions of large well spacing, boosting accuracy from 70 to 84%. Based on the high-precision model, eight vertical wells were strategically deployed, achieving a drilling compliance rate of 80%, demonstrating favorable application outcomes and establishing a robust foundation for the characterization and exploitation of residual gas reserves.