摘要：Accurate estimation of estimated ultimate recovery (EUR) is critical for shale reservoir development but remains challenging due to the complex interplay of geological and production factors. This paper presents a hybrid machine learning framework that combines static geological parameters with dynamic production data to enhance EUR prediction. Key innovations include a dual physical constraint mechanism incorporating the Arps decline equation and Darcy＇s law, and a dynamic weighting strategy that adaptively balances static and dynamic feature contributions based on production stage. The model achieves an R2 of 0.85 for wells with complete production history and 0.83 for those with limited data-a 5.7% improvement over conventional static methods in the Duvernay shale. Notably, using only 20 months of production data combined with static parameters, the model attains high prediction accuracy (R 2 = 0.83), demonstrating strong performance even under data scarcity. This approach provides a reliable tool for EUR prediction in marginal or undeveloped oil fields, supporting informed investment decisions and optimized development.

ISSN号：2470-1343

卷、期、页：卷10期48: 59947-59962

发表日期：2025-12-09

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）

发表期刊名称：ACS OMEGA

参与作者：张柯,顾斐

通讯作者：李烨,皮之洋,葛琛琦,包鹏虎,张雨杰

第一作者：惠钢,CHEN ZHANGXING,李靖

论文类型：期刊论文

论文概要：李烨,皮之洋,惠钢,CHEN ZHANGXING,李靖,张柯,葛琛琦,包鹏虎,张雨杰,顾斐，Application of Dynamic-Static Neural Network Model Integrating Physical Constraints in EUR Prediction of Shale Gas Wells，ACS OMEGA，2025，卷10期48: 59947-59962

论文题目：Application of Dynamic-Static Neural Network Model Integrating Physical Constraints in EUR Prediction of Shale Gas Wells