Application of the TPE-XGBoost Model in Predicting Breakdown Pressure for Horizontal Drilling Based on Physical Constraints

Horizontal well fracturing serves as a critical technology for enhancing production from tight sandstone gas reservoirs, where accurate prediction of formation breakdown pressure is essential for optimizing fracture design and improving stimulation effectiveness. This study proposes a novel fusion-driven workflow for predicting breakdown pressure in horizontal wells by synergistically integrating physics-based mechanistic modeling with data-driven machine learning. The approach overcomes the computational limitations of conventional analytical models and mitigates the data scarcity constraints inherent in purely empirical methods by using high-fidelity mechanistic simulations to generate physically consistent training samples. Results demonstrate that the hybrid dataset, with an optimal fusion ratio of 1:1.5 between field data and mechanistic-derived samples, yields the highest predictive accuracy. The proposed model, built on an XGBoost algorithm whose hyperparameters are efficiently optimized via a tree-structured Parzen estimator (TPE), exhibits superior generalization capability and robustness, achieving an average prediction error of 7.45% on unseen well data. This work confirms that the fusion framework provides a reliable and practical tool for breakdown pressure prediction in cased horizontal wells, which can directly support the design and implementation of efficient and sustainable fracturing operations in tight gas reservoirs.