准确、连续的生产和压力测量不仅对现场监控至关重要，而且对有效的生产优化也至关重要。虚拟流量计 (VFM) 是传统物理仪表的替代品，可以以基于物理和基于机器学习的形式存在。完整论文中提出的算法是一种融合两种类型的混合虚拟流量和压力计 (VFPM)。该算法的预测减少了使用基于物理的 VFM 生成数据所需的时间，同时提高了基于机器学习的 VFM 的准确性。
VFM 涉及物理模型，使用热力学、流体动力学、流体建模和优化技术来预测每个井的生产流量。
Accurate and continuous production and pressure measurements are crucial not only for field surveillance but also for effective production optimization. Virtual flowmeters (VFM) are an alternative to conventional physical meters and can exist in physics-based and machine‑learning-based forms. The algorithm proposed in the complete paper is a hybrid virtual flow and pressure meter (VFPM) that merges both types. The algorithm’s prediction reduces the time needed to generate data with physics-based VFM while increasing the accuracy of machine-learning-based VFM.
VFMs involve physics models to predict production flow rates in each well using thermodynamics, fluid dynamics, fluid modeling, and optimization techniques.
