An incomplete data set of flow rate and pressure is detrimental to reservoir management and operation. It has the potential to increase uncertainty and has the potential to unfavorably affect operational and managerial decisions. Such a data set might transpire because of failure in the flowmeters, pressure gauges, and/or unrecorded shut-in periods. This study proposes and evaluates unified physics and data-based analytics for “learning” the underlying behavior of a reservoir and reconstructing missing gas, oil, and water flow rates. The proposed workflow is evaluated using real field data obtained from a North Sea reservoir. Validation is done by using a whiteness test, a goodness-of-fit test, and a novel physics-based validation using material balance and pressure back-calculation. The outcome has shown the capability and flexibility of the selected machine-learning techniques in estimating the missing flow rate on the basis of pressure responses. The features that are extracted and expanded on the basis of physics have resulted in a high-fidelity model with less computation time.

流量和压力的不完整数据集不利于储层的管理和运行。它有可能增加不确定性，并有可能不利地影响运营和管理决策。由于流量计，压力表的故障和/或未记录的关闭时间，此类数据集可能会丢失。这项研究提出并评估了统一的物理和基于数据的分析方法，以“学习”储层的基本行为并重建缺失的天然气，石油和水的流速。拟议的工作流程是使用从北海水库获得的实际数据进行评估的。通过使用白度测试，拟合优度测试以及使用材料平衡和反算压力的新颖的基于物理的验证来完成验证。结果显示了所选机器学习技术在基于压力响应估算缺失流量方面的能力和灵活性。在物理基础上提取和扩展的特征导致了具有更少计算时间的高保真模型。