Fracturing Sweet Spot Evaluation and Prediction in Tight Sandstone Gas Reservoirs Using a GRA–LightGBM Hybrid Model

The development of tight sandstone gas reservoirs in the Ordos Basin is increasingly challenged by complex geological conditions and declining resource quality. Accurate identification of productivity-controlling factors and reliable prediction of fracturing sweet spots are therefore essential for improving reservoir development efficiency. In this study, geological, engineering, and production data from 56 wells in the target area were collected and preprocessed using forward and reverse normalization. Grey Relational Analysis was first used to identify the dominant factors controlling absolute open flow, and the selected variables were then incorporated into Light Gradient Boosting Machine to establish an integrated GRA-LightGBM prediction framework. The results indicate that permeability, average total hydrocarbon content, porosity, brittleness index, fracture toughness, and clay content are the primary productivity-controlling factors in the study area. The proposed GRA-LightGBM model achieved an R2 value of 0.9233, indicating strong agreement between predicted and measured AOF values. Comparative experiments with traditional machine learning models and tree-based ensemble models further demonstrated that GRA-LightGBM provides more accurate and stable predictions, with smaller residual fluctuations and better overall performance. Based on the prediction results, the spatial distribution of fracturing sweet spots was visualized using the Petrel platform. This study provides an effective data-driven workflow for dominant factor identification, AOF prediction, and sweet spot delineation, offering technical support for the optimization of hydraulic fracturing and well deployment in tight sandstone gas reservoirs.