Identification and Distribution Prediction of Sweet Spots in Tight Reservoirs Based on Machine Learning—Taking Satan 1 Block in Jinan Depression of Junggar Basin as an Example

This study focuses on the tight reservoirs of the Jingzigou Formation in the Satan 1 block of the Jinan Sag, Junggar Basin. By integrating analyses of sedimentary microfacies, reservoir characteristics, and fracture distribution, it innovatively applies machine learning algorithms for the quantitative identification and prediction of “sweet spots”. The results indicate that subaqueous distributary channels within the braided river delta front are the dominant sedimentary microfacies. The reservoir exhibits typical tight oil characteristics, with porosity primarily below 10% and permeability generally less than 0.01 mD. Sedimentary microfacies significantly control reservoir quality, with the subaqueous distributary channels exhibiting the best physical properties. Mid- to high-angle structural fractures effectively enhance reservoir permeability and show a strong positive correlation with oil saturation. This research employs machine learning techniques—including Decision Trees, Random Forest, and Support Vector Machines—to establish a comprehensive sweet spot classification model by integrating pore-throat structure, petrophysical parameters, reservoir thickness, and fracture development intensity. Among these, the Random Forest algorithm demonstrated optimal performance across all evaluation metrics. Prediction results reveal that Class I and Class II sweet spots are predominantly distributed in the northern slope area, while Class III sweet spots are located in the central trough and southern nose-like structural zone. These classification results show a high consistency with actual production data, confirming the effectiveness and applicability of machine learning for sweet spot prediction in this study area. The research outcomes provide reliable geological guidance for well placement optimization and reserve development in the Satan 1 block, offering significant reference value for the prediction and development of sweet spots in similar heterogeneous tight oil reservoirs.