Pore Pressure Prediction Using DASP-Based Feature Selection and a Physics-Constrained Attention-Enhanced CNN

Accurate prediction of pore pressure is crucial for ensuring drilling safety and improving the efficiency of oil and gas development. However, regarding feature selection, most existing studies focus primarily on the correlations among features, with little consideration given to inter-well distribution differences; this may result in insufficient model generalization capabilities for cross-well prediction tasks. To improve cross-well prediction accuracy, this paper introduces the DASP (Domain Adaptation with SHAP-guided Particle Swarm Optimization) method for feature selection. Using 20 logging and rock mechanical parameters as input features, a four-stage selection process reduces the number of features to 12, achieving approximately 40% feature dimensionality reduction. In terms of model performance comparison, models such as RF (Random Forest), XGB (eXtreme Gradient Boosting), LGB (Light Gradient Boosting Machine), and CNN (Convolutional Neural Network) were constructed for comparative analysis. The results indicate that LGB performs better during the validation phase, while in the cross-well testing phase, RF achieves the best prediction accuracy among the base models, demonstrating strong generalization stability. Addressing the issue that CNNs still have room for further optimization in cross-well prediction, this study further refines their architecture by introducing an attention mechanism to enhance the model’s adaptive weighting capability for key features and combining it with a physical constraint mechanism to suppress non-physical prediction fluctuations, thereby improving the model’s stability and geological plausibility. Experimental results demonstrate that the improved model shows significant improvements in both error metrics and fitting capabilities. The combination of the DASP-based feature selection method and the improved CNN model effectively enhances the accuracy of cross-well pore pressure prediction, providing new technical insights and data support for intelligent pore pressure prediction and drilling safety decision-making.