The Upper Paleozoic Shihezi Formation in Block L of the eastern Ordos Basin harbors extensive tight sandstone gas reservoirs. However, these reservoirs exhibit strong heterogeneity, thin sand bodies, and overlapping elastic properties between gas- and water-bearing layers, which significantly limit the effectiveness of conventional pre-stack inversion methods in delineating thin sand bodies and predicting gas saturation. To address these challenges, we propose an integrated high-resolution gas prediction technique combining geostatistical inversion with deep learning. First, within a Bayesian sequential inversion framework, we jointly inverted well-log data, seismic data, and geological constraints to obtain high-resolution elastic parameters, substantially improving the identification of thin sand bodies (<5 m). Second, we employed a long short-term memory network to extract temporal features from inverted elastic parameter sequences and establish a non-linear mapping between gas/water-sensitive attributes and water saturation; this step incorporates horizon constraints and an attribute optimization strategy to enhance prediction accuracy. Field applications demonstrated that our method achieved superior performance compared to conventional approaches, with an 85% consistency rate between predicted gas saturation and drilling results. The integration of geostatistical inversion and deep learning provides a robust workflow for characterizing thin, heterogeneous tight gas reservoirs, offering significant potential for optimizing exploration and development strategies in the Ordos Basin.