A Comprehensive Mechanistic Model for Upward Two-Phase Flow in Wellbores

1994 ◽  
Vol 9 (02) ◽  
pp. 143-151 ◽  
Author(s):  
A.M. Ansari ◽  
N.D. Sylvester ◽  
C. Sarica ◽  
O. Shoham ◽  
J.P. Brill
2019 ◽  
Author(s):  
Zurwa Khan ◽  
Reza Tafreshi ◽  
Matthew Franchek ◽  
Karolos Grigoriadis

Abstract Pressure drop estimation across orifices for two-phase liquid-gas flow is essential to size valves and pipelines and decrease the probability of unsafe consequences or high costs in petroleum, chemical, and nuclear industries. While numerically modeling flow across orifices is a complex task, it can assess the effect of numerous orifice designs and operation parameters. In this paper, two-phase flow across orifices has been numerically modeled to investigate the effect of different fluid combinations and orifice geometries on pressure drop. The orifice is assumed to be located in a pipe with fully-developed upstream and downstream flow. Two liquid-gas fluid combinations, namely water-air, and gasoil liquid-gas mixture were investigated for different orifice to pipe area ratios ranging from 0.01 to 1 for the superficial velocity of 10 m/s. Volume of Fluid multiphase flow model along with k-epsilon turbulence model were used to estimate the pressure distribution of liquid-gas mixture along the pipe. The numerical model was validated for water-air with mean relative error less than 10.5%. As expected, a decrease in orifice to pipe area ratio resulted in larger pressure drops due to an increase in the contraction coefficients of the orifice assembly. It was also found that water-air had larger pressure drops relative to gasoil mixture due to larger vortex formation downstream of orifices. In parallel, a mechanistic model to directly estimate the local two-phase pressure drop across orifices was developed. The gas void fraction was predicted using a correlation by Woldesemayat and Ghajar, and applied to separated two-phase flow undergoing contraction and expansion due to an orifice. The model results were validated for different orifices and velocities, with the overall relative error of less than 40%, which is acceptable due to the uncertainties associated with measuring experimental pressure drop. Comparison of the developed numerical and mechanistic model showed that the numerical model is able to achieve a higher accuracy, while the mechanistic model requires minimal computation.


2021 ◽  
Author(s):  
Zurwa Khan ◽  
Reza Tafreshi ◽  
MD Ferdous Wahid ◽  
Albertus Retnanto

Abstract Mechanistic models are necessary for understanding and predicting the behavior of liquid-liquid flow for multiple pipe dimensions, mixture properties, and flow patterns. In this paper, a mechanistic model is proposed to calculate pressure drop across circular channels for liquid-liquid two-phase flow. The developed model considers several key aspects of liquid-liquid flow, such as mixed and wavy liquid-liquid interfaces and dispersion within each liquid’s layers. Unique identifiers, such as height, turbulence, and dispersion, are calculated for each phase, using an augmented separated flow model and nonlinear optimization. Comparison of the proposed model with experimental data, comprising of multiple inclination angles and flow patterns, shows accurate predictions for a variety of liquid-liquid flow patterns, including double- and triple-layered flow.


Fluids ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 300
Author(s):  
Taoufik Wassar ◽  
Matthew A. Franchek ◽  
Hamdi Mnasri ◽  
Yingjie Tang

Due to the complex nonlinearity characteristics, analytical modeling of compressible flow in inclined transmission lines remains a challenge. This paper proposes an analytical model for one-dimensional flow of a two-phase gas-liquid fluid in inclined transmission lines. The proposed model is comprised of a steady-state two-phase flow mechanistic model in-series with a dynamic single-phase flow model. The two-phase mechanistic model captures the steady-state pressure drop and liquid holdup properties of the gas-liquid fluid. The developed dynamic single-phase flow model is an analytical model comprised of rational polynomial transfer functions that are explicitly functions of fluid properties, line geometry, and inclination angle. The accuracy of the fluid resonant frequencies predicted by the transient flow model is precise and not a function of transmission line spatial discretization. Therefore, model complexity is solely a function of the number of desired modes. The dynamic single-phase model is applicable for under-damped and over-damped systems, laminar, and turbulent flow conditions. The accuracy of the overall two-phase flow model is investigated using the commercial multiphase flow dynamic code OLGA. The mean absolute error between the two models in step response overshoot and settling time is less than 8% and 2 s, respectively.


1999 ◽  
Author(s):  
L.E. Gomez ◽  
O. Shoham ◽  
Z. Schmidt ◽  
R.N. Chokshi ◽  
A. Brown ◽  
...  

Author(s):  
Clement C. Tang ◽  
Afshin J. Ghajar

A mechanistic heat transfer correlation is proposed to estimate heat transfer coefficient for non-boiling two-phase flow in horizontal, slightly inclined, and vertical pipes using the analogy between friction factor and heat transfer. Local heat transfer coefficients, pressure drops and flow parameters were measured for air-water flow in a 27.9 mm stainless steel pipe. The heat transfer and pressure drop data were collected by carefully coordinating the gas and liquid superficial Reynolds numbers. The proposed mechanistic correlation is validated by using experimentally measured heat transfer data. Evaluation of the mechanistic correlation with the measured heat transfer data indicated that the analogy between friction factor and heat transfer can be used with reasonable accuracy for heat transfer predictions in non-boiling two-phase pipe flow. Comparison with experimental results showed that the bulk of the data points were predicted within ±30% by the mechanistic model.


SPE Journal ◽  
2000 ◽  
Vol 5 (03) ◽  
pp. 339-350 ◽  
Author(s):  
L.E. Gomez ◽  
Ovadia Shoham ◽  
Zelimir Schmidt ◽  
R.N. Chokshi ◽  
Tor Northug

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