Abstract Using the original form of Capacitance-Resistance Model (CRM), as a waterflooding performance prediction tool, for modeling real reservoirs makes some unavoidable errors. Combination of this model with available data assimilation methods yields more powerful simulation tool with updating parameters over time. However, the inherent uncertainty arisen by modeling complex reservoirs with only a limited number of CRM parameters is not addressed yet. In this study, the model error behavior has been simulated through a physically-based dynamical system in which it has been correlated with the original model parameters. The ensemble-based Kalman filter (EnKF) data assimilation method has been employed to practice observation data. To show the validity of the developed CRM-Error system, we have employed it to replicate the data obtained from a synthetic model of an Iranian reservoir. Results show that acceptable ranges for the production rates have been achieved via this model in comparison with observed data.

中文翻译：

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电容-电阻模型中的错误行为建模：促进快速、可靠的储层性能预测代理

摘要 使用电容-电阻模型（CRM）的原始形式作为注水性能预测工具，对真实油藏进行建模会产生一些不可避免的错误。将该模型与可用的数据同化方法相结合，可以产生更强大的仿真工具，并随时间更新参数。然而，仅使用有限数量的 CRM 参数对复杂储层进行建模所产生的固有不确定性尚未得到解决。在这项研究中，模型误差行为已通过基于物理的动力系统进行模拟，其中它已与原始模型参数相关联。基于集合的卡尔曼滤波器（EnKF）数据同化方法已被用于实践观测数据。为了证明所开发的 CRM-Error 系统的有效性，我们使用它来复制从伊朗水库的合成模型中获得的数据。结果表明，与观察到的数据相比，通过该模型已经实现了可接受的生产率范围。