Study on the Influence of Interactive Effect of Geological Engineering Parameters of Deep Shale Gas on Productivity and Construction Optimization Analysis

This paper proposes a shale gas Evaluation of ultimate recovery (EUR) prediction framework based on multialgorithm integration optimization and interactive effect visualization analysis. By integrating XGBoost, CatBoost, and LightGBM algorithms and introducing Bayesian hyperparameter optimization technology, an EUR prediction model for the Luzhou block is constructed. Compared with traditional single‐model methods, this framework achieves the following for the first time: 1) a multialgorithm collaborative parameter tuning mechanism based on Bayesian optimization and 2) visualization analysis of the interaction effects of geological and engineering parameters based on interpretability techniques. The results show that, under conditions of limited well data, the LightGBM algorithm optimized by Bayesian methods outperforms other algorithms in predicting EUR, with an error rate of only 13.7%. Through marginal effect analysis of single‐factor and two‐factor interactions, the study investigated the influence of feature values on EUR contributions and defined the optimal range of feature parameters for achieving higher EUR within the block. This research provides a new paradigm for integrated optimization of shale gas geological engineering.