石油科学通报2026，Vol.11Issue(3)：894-909,16.DOI:10.3969/j.issn.2096-1693.2026.03.015
基于PI-DeepONet的储层应力场预测与泛化训练策略研究
Reservoir stress-field prediction and generalization-oriented training strategies based on PI-DeepONet
摘要
Abstract
In reservoir stress field simulation,Physics-Informed Neural Networks(PINN)can achieve high-precision unsupervised solutions;however,their model structure tightly couples the computational domain with physical parameters,resulting in applicability only to a fixed set of material properties.This leads to limited generalization under varying working conditions.To enhance the generalization capability of PINN,this study develops an intelligent computational approach based on the Physics-Informed Deep Operator Network(PI-DeepONet).By introducing a branch-trunk architecture and employing the Hadamard product to fuse parameter and coordinate features,an end-to-end mapping from reservoir physical parameters to the stress-displacement field is established.Furthermore,a hard-constraint mechanism and a staged progressive training strategy are integrated to construct a stress field operator model with strong generalization capacity.The results demonstrate that this method overcomes the non-generalizability of conventional PINN,achieving approximately 30.6%improvement in training efficiency under sparse physical-space discretization,and around 62.4%enhancement in prediction accuracy under dense discretization by applying hard constraints.This research provides a reliable intelligent computational framework for efficient hydrocarbon reservoir development and CO2 geological storage assessment.关键词
物理信息深度算子网络/物理信息神经网络/储层应力场/硬约束/模型泛化性Key words
PI-DeepONet/PINN/reservoir stress field/hard constraint mechanism/model generalization分类
能源科技引用本文复制引用
季源,陈掌星,李俊,彭岩,吴克柳,王笑涵..基于PI-DeepONet的储层应力场预测与泛化训练策略研究[J].石油科学通报,2026,11(3):894-909,16.基金项目
新疆维吾尔自治区重点研发项目(2024B01013-1)和天山英才培养计划(T2024TSYCCX0070)联合资助 （2024B01013-1）
