Well logging curves are essential for recording the physical parameters of formations during drilling, providing vital information for analyzing rock properties, evaluating hydrocarbon reservoirs, and understanding reservoir distribution. As oil and gas exploration continues to progress, the complexity of subtle and hidden reservoirs has increased, posing challenges for traditional exploration techniques. Despite their importance, conventional well logging data suffer from low resolution, which significantly limits their ability to address the requirements of detailed reservoir characterization. In particular, the inability to precisely identify modification points in thin interbedded reservoirs remains a critical bottleneck in reservoir analysis. To overcome these limitations, developing high-resolution interpretation methods for well logging data has become an urgent priority in the field of reservoir analysis and geological exploration. This study proposes a novel reservoir prediction model based on the ResNet50 regression algorithm. By integrating vertically continuous optical thin-section data, which can capture fine-scale and complex vertical geological features, with five conventional well logging parameters, the proposed model aims to improve the resolution and accuracy of reservoir analysis. This combination leverages the strengths of image-based geological analysis and traditional well logging to deliver a more precise interpretation of subsurface formations. The model was validated using data collected from five intervals of the Permian formation in a specific well area. A total of 570 continuous geological image samples, combined with their corresponding well logging data, were utilized for model training and prediction. The results demonstrate that the model effectively enhances the resolution of well logging data, improving it from the traditional 12.5 cm to 6.25 cm. This significant improvement not only increases the precision of well logging interpretation but also provides a more detailed understanding of reservoir characteristics. The model’s performance was rigorously evaluated using three widely recognized metrics: the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results revealed that the model excels in predicting parameters such as acoustic time (AC), compensated neutron (CNL), resistivity (RT), and gamma ray (GR), achieving an average prediction error below 0.094. This highlights the model’s reliability and superior performance in reservoir prediction tasks. However, challenges remain in predicting density (DEN), where the model’s accuracy is impacted in intervals with significant lithological heterogeneity or complex geological conditions.