常规井注入和生产数据包含重要信息，可用于闭环油藏管理和快速现场决策。传统的基于物理的数值油藏模拟在短期决策周期中计算量过大。作为替代方案，简化物理模型的适用范围通常有限。纯机器学习 (ML) 模型通常缺乏物理可解释性，预测能力有限。

Routine well injection and production data contain significant information that can be used for closed-loop reservoir management and rapid field decision-making. Traditional physics-based numerical reservoir simulation can be prohibitive computationally for short-term decision cycles. As an alternative, reduced physics models often have a limited range of applicability. Pure machine-learning (ML) models often lack physical interpretability and can have limited predictive power.