Dynamic data reconciliation with simultaneous time-varying parameter estimation in real time: application to an electric submersible pump lift oil production
摘要
Data reconciliation techniques have been the subject of many classic studies in the data conditioning process. By reconciling the measurements, accurate estimation of the system output and unmeasured variables is provided. However, accurately determining measurement noise and parameter uncertainty in real time remains a significant challenge. How to simultaneously estimate parameters in the system has been attracting considerable interest. So far, very little attention has been paid to time-varying parameter estimation in oil production systems. In particular, estimation of parameter dynamics and the corresponding uncertainty without prior knowledge remains challenging. This work extends a previous study on dynamic parameter estimation by considering scenarios where parameters change both gradually and abruptly. To address these dynamics, nonlinear filtering methods are employed and compared. A comparative analysis was conducted using both quantitative metrics and visualization plots to evaluate the performance of various approaches. Under the same abrupt parameter change scenario, nonlinear filter-based methods demonstrated superior performance in parameter estimation, achieving a root mean square error of