<p>Addressing the current practice of multi-layer commingled testing in the Subei Basin, the productivity results of commingled tests were allocated to individual layers by comprehensively considering parameters such as porosity, permeability, formation thickness, and deep induction resistivity. This established a relatively scientific productivity allocation method, effectively resolving the issue of productivity partitioning. Through the analysis of productivity-influencing factors, it was found that in Type I and Type II reservoirs, when the production intensity is below a certain threshold value, well-logging data can still reflect most of the formation information. In this case, acoustic time difference, porosity, permeability, and actual fracturing pressure can effectively indicate the post-stimulation production intensity. However, when the production intensity exceeds the threshold, well-logging data can only reflect a small portion of the formation information. Here, the fracability of the reservoir and engineering factors play a decisive role. Consequently, only the brittleness index and actual fracturing pressure can effectively reflect the post-stimulation production intensity. A quantitative productivity evaluation model was established based on the identified productivity-influencing factors. Analysis indicates that this method is suitable for early-stage productivity prediction, as it is highly correlated with well-logging parameters that characterize the reservoir’s original state. In the studied area, the post-stimulation productivity of the reservoir can not only be qualitatively evaluated but also quantitatively assessed with high precision, achieving an absolute error within ±0.5 t/d. This systematic summary, combined with statistical analysis and production practices, further refines the productivity prediction method for low-porosity and low-permeability sandstone reservoirs after fracturing stimulationg.</p>