Experimental and Modeling Investigation on Gas-Liquid Two-Phase Flow in Horizontal Gas Wells

Luo, Chengcheng; Cao, Yong; Liu, Yonghui; Zhong, Sicun; Zhao, Suhui; Liu, Zhongbo; Liu, Yaxin; Zheng, Danzhu

doi:10.1115/1.4055223

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…As reflected in this figure, the highest impact is related to pipe diameter with relevancy values of 0.8793 for the ANN model and 0.8995 for the SVM model, which indicates that the drift velocity will increase largely as the pipe diameter increased. On the other hand, the liquid density, liquid viscosity, and surface tension showed inverse relationships and similar relative importance (Luo et al 2023). This could be the fact that density, viscosity and surface tension are not truly independent variables.…”

Section: Resultsmentioning

confidence: 98%

Prediction of Rise Velocity of Taylor Bubbles in Pipes Using an Artificial Neural Network

Liu,

Upchurch,

Ozbayoglu

et al. 2024

Preprint

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Most of the drift velocity models that exist have limitations, as they were developed based on experiments that hardly considered the conjunction of liquid properties and pipe geometry effects.Additionally, some of the models are formed complexly and several parameters require to be optimized. This study focuses on the application of the artificial neural network (ANN) model in predicting the drift velocity of Taylor bubbles rising in stagnant fluids through horizontal and inclined pipes. A comprehensive experimental database including 364 data points from the open literature was applied to develop the ANN model. Inclination angle, pipe diameter, liquid density, liquid viscosity, and surface tension were used as input variables. Finally, a multilayer perceptron (MLP) feed-forward backpropagation neural network having 12 neurons in the hidden layer was selected as the optimized ANN model that showed the best performance for the prediction of drift velocity. The obtained ANN model showed superior performance in comparison with the support vector machine (SVM) model and four drift velocity correlations, with high accuracy for the training data set (MSE=0.000127, R2=0.9985, and MAPE= 5.85%), testing data set (MSE=0.00028, R2=0.9983, and MAPE= 5.45%), and all data (MSE=0.000137, R2=0.9982, and MAPE= 5.69%).

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Section: Resultsmentioning

confidence: 98%

Prediction of Rise Velocity of Taylor Bubbles in Pipes Using an Artificial Neural Network

Liu,

Upchurch,

Ozbayoglu

et al. 2024

Preprint

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“…Numerical simulations of fluid flow in labyrinth seals have been conducted to understand the transportation mechanism in complex structures [21][22][23]. Stoff [24] proposed a computer model to simulate the flow of incompressible fluid in a labyrinth seal, which was found to be in good agreement with experimental measurements.…”

Section: Introductionmentioning

confidence: 90%

A Numerical Study on Labyrinth Screw Pump (LSP) Performance under Viscous Fluid Flow

Zeng

Wang

et al. 2023

Energies

Self Cite

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In this study, fluid viscosity effects on LSP performance in terms of boosting pressure were numerically investigated. A water–glycerin mixture with different concentrations corresponding to varying apparent viscosities was flowed through an in-house manufactured LSP under various flow conditions, e.g., changing flow rates, rotational speeds, and fluid viscosities. The pressure increment between the intake and discharge of the LSP was recorded using the differential pressure transducer. The same pump geometries, fluid properties and flow conditions were incorporated into the numerical configurations, where three-dimensional (3D), steady-state, Reynolds-averaged Navier–Stokes (RANS) equations with a standard SST (shear stress transport) turbulence model were solved by a commercial CFD code. With the high-quality poly-hexcore grids, the simulated pressure increment was compared with the corresponding experimental measurement. The internal flow structures and characteristics within the cavities contained by the LSP impeller and diffuser were also analyzed. The good agreement between the numerical results against the experimental data verified the methodology adopted in this study.

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“…The gas-liquid velocity ratio is a more intuitive reaction gas-liquid ratio. In addition to the gas-liquid velocity ratio, the Froude number and Reynolds number can also characterize the effect of fluid flows from the side [22][23][24][25][26]. In order to improve the calculation accuracy for the pressure drop calculation formula, the three parameters of the Froude number, Reynolds number, and gas-liquid velocity ratio were brought into the model for correlation verification, and the most appropriate parameters representing the gas-liquid ratio were selected for the establishment of a new model.…”

Section: Pressure Calculation Formulamentioning

confidence: 99%

Study on Calculation Method for Wellbore Pressure in Gas Wells with Large Liquid Production

Cheng

Wu²,

Liao

et al. 2022

Processes

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In order to solve the inaccuracy in the calculation of the wellbore pressure distribution caused by large liquid production in the Donghai gas field, the gas–liquid production conditions of the Donghai gas field were simulated with indoor experiments, and the flow patterns for different pipe diameters, different inclinations, and different flow patterns were systematically analyzed using a flow pattern discrimination method, liquid holdup calculation method, and pressure drop calculation method. Using the experimental data, the division methods for different flow patterns were screened. Finally, based on the fact that the change trend for the flow patterns was consistent, the Kaya–Sarica–Brill method was selected to establish the flow pattern discrimination formula. According to the calculation method for the Mukherjee and Brill (M–B) liquid holdup, the M–B model was re-established according to a 75 mm pipe diameter and 60 mm pipe diameter using the instantaneous liquid holdup measured in the laboratory. Through the comparison and analysis of the measured data and the calculated data for the Beggs and Brill (B–B_ pressure drop model under the same working conditions, it was found that when the B–B model was applied to different angles and different gas–liquid ratios, the error decreased with an increase in the angle and increased with a decrease in the gas–liquid ratio. After verifying the correlation of different dimensionless numbers that can characterize the gas–liquid ratio, it was considered that the introduction of the Reynolds number into the original model could greatly improve the accuracy of the calculation, so a new pressure drop calculation model was established. The new pressure drop calculation model takes into account the two parameters of the well deviation angle and gas–liquid ratio. The accuracy was greatly improved, as verified by field measurements in four wells.

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Experimental and Modeling Investigation on Gas-Liquid Two-Phase Flow in Horizontal Gas Wells

Cited by 16 publications

References 24 publications

Prediction of Rise Velocity of Taylor Bubbles in Pipes Using an Artificial Neural Network

Prediction of Rise Velocity of Taylor Bubbles in Pipes Using an Artificial Neural Network

A Numerical Study on Labyrinth Screw Pump (LSP) Performance under Viscous Fluid Flow

Study on Calculation Method for Wellbore Pressure in Gas Wells with Large Liquid Production

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