.png){kind=icon}
.png){kind=icon}
Return
Dynamic Fitting Method for Wellbore Multiphase Flow with Exponentially Weighted Parameter Updating
.png){kind=icon}
OA
AI
Y
X
M
Y
T
Z
G
X
DOI：10.3390/pr13092894.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
wellbore multiphase flow
dynamic fitting
exponentially weighted moving average
particle swarm optimization
low-frequency data adaptation
petroleum
natural gas
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.8
Papers:
6.6K
Citations:
3.7W