同济大学学报（自然科学版）2026，Vol.54Issue(8)：1176-1185,10.DOI:10.11908/j.issn.0253-374x.25162
水力压裂深层储层破裂压力预测可解释人工智能模型
Explainable Artificial Intelligence Model for Fracture Pressure Prediction in Deep Reservoir Hydraulic Fracturing
刘宇杭 1庄晓莹 2任辉龙 1盛茂 3申洋4
作者信息
- 1. 同济大学土木工程学院,上海 200092
- 2. 同济大学土木工程学院,上海 200092||莱布尼茨汉诺威大学数学与物理学院,德国汉诺威30167
- 3. 中国石油大学(北京)石油工程学院,北京 102249
- 4. 中国商飞复合材料中心,上海 200126
- 折叠
摘要
Abstract
To address the limited interpretability of existing models for predicting fracture pressure during hydraulic fracturing in deep reservoirs,this paper develops a multi-granularity interpretable framework integrating Shapley additive explanations(SHAP)and Kolmogorov-Amold network(KAN)based on field engineering data.The proposed framework achieves high prediction accuracy with an average coefficient of determination of 0.958 5.Interpretability analysis reveals that at the macro level,torque-related features dominate the prediction process.At the micro level,engineering parameters such as weight on bit and torque control fracture pressure by affecting rock tensile strength and in-situ stress levels,with stress level identified as the dominant controlling factor.These findings are consistent with existing theoretical and experimental studies.The macro-level feature attribution from SHAP and the micro-level mechanism interpretation from KAN mutually validate each other,demonstrating the synergistic advantages of the proposed multi-granularity interpretable framework.关键词
破裂压力/可解释人工智能/柯尔莫哥洛夫-阿诺德网络(KAN)/深层储层油井工程/沙普利加性解释(SHAP)/现场数据Key words
fracture pressure/explainable artificial intelligence/Kolmogorov-Amold network(KAN)/deep reservoir well engineering/Shapley additive explanations(SHAP)/field data分类
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刘宇杭,庄晓莹,任辉龙,盛茂,申洋..水力压裂深层储层破裂压力预测可解释人工智能模型[J].同济大学学报（自然科学版）,2026,54(8):1176-1185,10.
