In oil industries with SAGD facilities, continuous and fast-rate produced fluids flow rate measurement at each well pair is essential for implementing advanced process control and optimization methods. However, it is not always available due to expensive instrumentation and unreliable measurement. Soft sensors have become a popular alternative to the hardware sensors owing to the availability of a large amount of data archived during production. This paper presents a comprehensive data-driven approach for developing and implementing soft sensors for fluid flow rates of SAGD wells. The steps taken in offline model training are first discussed. For online implementation, a robust layer and a data reconciliation-based bias correction step are proposed to enhance the accuracy and reliability of the developed soft sensor. The effectiveness of the soft sensor is demonstrated through successful applications to various SAGD wells.

在具有SAGD设施的石油工业中，对每口井进行连续且快速的采出液流量测量对于实施先进的过程控制和优化方法至关重要。然而，由于昂贵的仪器和不可靠的测量，它并不总是可用。由于在生产过程中可以存储大量数据，因此软传感器已成为硬件传感器的流行替代方案。本文提出了一种综合的数据驱动方法，用于开发和实施SAGD井流体流速的软传感器。首先讨论离线模型训练中采取的步骤。对于在线实施，提出了鲁棒层和基于数据对账的偏差校正步骤，以提高开发的软传感器的准确性和可靠性。