Dynamic Fitting Method for Wellbore Multiphase Flow with Exponentially Weighted Parameter Updating

Accurate dynamic characterization of wellbore multiphase flow is fundamental for production optimization and real-time control in oil and gas wells. Addressing technical constraints of existing dynamic fitting methods, this study proposes a novel dynamic fitting methodology integrating physical mechanisms with exponentially weighted parameter updating. The approach leverages multiphase flow theory to target the liquid holdup factor and friction factor as correction parameters for dynamic fitting. It incorporates Particle Swarm Optimization to achieve rapid and accurate fitting and introduces an Exponentially Weighted Moving Average mechanism to dynamically update parameters. By fusing historical data with real-time data, the Exponentially Weighted Moving Average method balances instantaneous responsiveness with long-term stability. Empirical validation using a dataset from Block XX of a Southern China oilfield demonstrates the superior accuracy of the fitting method under low-to-medium frequency data conditions. During data interruptions or anomalous disturbances, the method maintains high accuracy while exhibiting a low mean relative change percentage; it effectively suppressed the non-physical jumps of the fitting coefficients and maintained stable and accurate fitting.