Application of the TPE-XGBoost Model in Predicting Breakdown Pressure for Horizontal Drilling Based on Physical Constraints
Year: 2026
Type: article
Abstract: 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.
Source: Processes
DOI: 10.3390/pr14040630
Institution: China University of Petroleum, East China
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: Southwest Petroleum University
OpenAlex ID: W7128725240
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