Data analytics approach for online produced fluid flow rate estimation in SAGD process

Abstract

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.