Onset of Liquid-Film Reversal In Upward-Inclined Pipes

SPE Journal ◽  
2018 ◽  
Vol 23 (05) ◽  
pp. 1630-1647 ◽  
Author(s):  
Yilin Fan ◽  
Eduardo Pereyra ◽  
Cem Sarica
Keyword(s):  
Liquid Film ◽  
Prediction Models ◽  
Mechanistic Model ◽  
Absolute Error ◽  
Gas Velocity ◽  
Liquid Loading ◽  
New Model ◽  
Velocity Prediction ◽  
Inclined Pipe ◽  
Inclined Pipes

Summary Accumulation of oil and/or water at the bottom of an upward-inclined pipe is known to be the source of many industrial problems, such as corrosion and terrain slugging. Therefore, accurate prediction of the critical gas velocity that can avoid the liquid accumulation is of great importance. An experimental study of onset of liquid-film reversal, which is believed to be the main cause of liquid accumulation, was conducted in a hilly-valley test section at low-liquid-loading condition. A new, easily implemented mechanistic model to predict critical gas velocity, which is specifically developed based on the liquid-film reversal in stratified flow, is proposed in this work. The new model was verified with the data acquired in the study and other studies from the open literature, showing a fair agreement. This work also reviewed and evaluated other critical-gas-velocity-prediction models. The new model performs best compared with other models, especially in terms of the inclination angle and liquid-flow-rate effect on critical gas velocity. The total average absolute error was reduced 6.0% compared with the current best-prediction model (Zhang et al. 2003), and 38.2% for the widely used Turner et al. (1969) droplet-removal model.

scholarly journals Research of friction characteristics on the hydraulic transportation in inclined slurry-pipe

2020 ◽  
Vol 198 ◽  
pp. 01030
Author(s):  
Wang Tieli
Keyword(s):  
Experimental Data ◽  
Momentum Transfer ◽  
Interaction Mechanism ◽  
Hydraulic Gradient ◽  
Solid Particles ◽  
Friction Loss ◽  
Acceleration Time ◽  
New Model ◽  
Inclined Pipe ◽  
Inclined Pipes

By analyzing the momentum transfer and velocity both of solid particles and water over the acceleration time of solid particles, as well as interaction mechanism between water and solid particals, a new model is proposed to predict friction loss for setting slurry flow in inclined pipe. The hydraulic gradient formula for inclined pipes summarized by the author is confirmed by a large amount of experimental data. The results show that the deviation between the theoretical value of the model proposed by the author and the measured value is not more than 13.33%, which is the smallest among all reports.

scholarly journals A New Approach for Accurate Prediction of Liquid Loading of Directional Gas Wells in Transition Flow or Turbulent Flow

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ruiqing Ming ◽  
Huiqun He
Keyword(s):  
Reynolds Number ◽  
Turbulent Flow ◽  
Correction Coefficient ◽  
Calculation Error ◽  
Gas Velocity ◽  
Transition Flow ◽  
Deviation Angle ◽  
Vertical Wells ◽  
Liquid Loading ◽  
New Model

Current common models for calculating continuous liquid-carrying critical gas velocity are established based on vertical wells and laminar flow without considering the influence of deviation angle and Reynolds number on liquid-carrying. With the increase of the directional well in transition flow or turbulent flow, the current common models cannot accurately predict the critical gas velocity of these wells. So we built a new model to predict continuous liquid-carrying critical gas velocity for directional well in transition flow or turbulent flow. It is shown from sensitivity analysis that the correction coefficient is mainly influenced by Reynolds number and deviation angle. With the increase of Reynolds number, the critical liquid-carrying gas velocity increases first and then decreases. And with the increase of deviation angle, the critical liquid-carrying gas velocity gradually decreases. It is indicated from the case calculation analysis that the calculation error of this new model is less than 10%, where accuracy is much higher than those of current common models. It is demonstrated that the continuous liquid-carrying critical gas velocity of directional well in transition flow or turbulent flow can be predicted accurately by using this new model.

Effect of Drag-Reducing Agent on Slug Characteristics in Multiphase Flow in Inclined Pipes

1999 ◽  
Vol 121 (2) ◽  
pp. 86-90 ◽  
Author(s):  
C. Kang ◽  
W. P. Jepson ◽  
M. Gopal
Keyword(s):  
Pressure Drop ◽  
Multiphase Flow ◽  
Liquid Film ◽  
Froude Number ◽  
Liquid Velocity ◽  
Translational Velocity ◽  
Gas Velocity ◽  
Superficial Gas Velocity ◽  
Inclined Pipes ◽  
Superficial Liquid Velocity

The effect of drag-reducing agent (DRA) on multiphase flow in upward and downward inclined pipes has been studied. The effect of DRA on pressure drop and slug characteristics such as slug translational velocity, the height of the liquid film, slug frequency, and Froude number have been determined. Experiments were performed in 10-cm i.d., 18-m long plexiglass pipes at inclinations of 2 and 15 deg for 50 percent oil-50 percent water-gas. The DRA effect was examined for concentrations ranging from 0 to 50 ppm. Studies were done for superficial liquid velocities between 0.5 and 3 m/s and superficial gas velocities between 2 and 10 m/s. The results indicate that the DRA was effective in reducing the pressure drop for both upflow and downflow in inclined pipes. Pressure gradient reduction of up to 92 percent for stratified flow with a concentration of 50 ppm DRA was achieved in ±2 deg downward inclined flow. The effectiveness of DRA for slug flow was 67 percent at a superficial liquid velocity of 0.5 m/s and superficial gas velocity of 2 m/s in 15 deg upward inclined pipes. Slug translational velocity does not change with DRA concentrations. The slug frequency decreases from 68 to 54 slugs/min at superficial liquid velocity of 1 m/s and superficial gas velocity of 4 m/s in 15 deg upward inclined pipes as the concentration of 50 ppm was added. The height of the liquid film decreased with the addition of DRA, which leads to an increase in Froude number.

scholarly journals Research on Critical Liquid-Carrying Model in Wellbore and Laboratory Experimental Verification

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 923
Author(s):  
Wenqi Ke ◽  
Lintong Hou ◽  
Lisong Wang ◽  
Jun Niu ◽  
Jingyu Xu
Keyword(s):  
Liquid Film ◽  
Shear Force ◽  
Liquid Interface ◽  
Force Balance ◽  
Theoretical Research ◽  
Vertical Position ◽  
Gas Velocity ◽  
Gas Wells ◽  
Liquid Loading ◽  
Interface Shear

Liquid loading in gas wells may slash production rates, shorten production life, or even stop production. In order to reveal the mechanism of liquid loading in gas wells and predict its critical flowrates, theoretical research and laboratory experiments were conducted in this work. A new model of liquid-film reversal was established based on Newton’s law of internal friction and gas–liquid two-phase force balance, with the critical reverse point obtained using the minimum gas–liquid interface shear force method. In this model, the influences of the pipe angle on the liquid film thickness were considered, and the friction coefficient of the gas–liquid interface was refined based on the experimental data. The results showed that the interfacial shear force increases by increasing the liquid superficial velocity, which leads first to an increase of the critical liquid-carrying gas velocity and then to a decrease, and the critical production also decreases. With 0° as the vertical position of the pipeline and an increase of the inclination angle, the critical liquid-carrying velocity first increases and then decreases, and the maximum liquid-carrying velocity appears in the range of 30–40°. In addition, the critical liquid-carrying gas velocity is positively correlated with the pipe diameter. Compared with the previous model, the model in this work performed better considering its prediction discrepancy with experiment data was less than 10%, which shows that the model can be used to calculate the critical liquid-carrying flow rate of gas wells. The outcome of this work provides better understanding of the liquid-loading mechanism. Furthermore, the prediction model proposed can provide guidance in field design to prevent liquid loading.

A new model for predicting critical gas velocity ofliquid loading in horizontal well

2018 ◽  
Vol 35 (6) ◽  
pp. 551
Author(s):  
Yonghui LIU ◽  
Xianting AI ◽  
Chengcheng LUO ◽  
Fengwei LIU ◽  
Pengbo WU
Keyword(s):  
Horizontal Well ◽  
Gas Velocity ◽  
New Model

scholarly journals Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang
Keyword(s):  
Feedback Control ◽  
Prediction Models ◽  
Feedforward Control ◽  
Absolute Error ◽  
Dynamic Responses ◽  
Control Schemes ◽  
Online Measurements ◽  
Measurement Delay ◽  
Water Feed ◽  
Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

2021 ◽  
Vol 2 (104) ◽  
pp. 57-76
Author(s):  
P. Sihag ◽  
M.R. Sadikhani ◽  
V. Vambol ◽  
S. Vambol ◽  
A.K. Prabhakar ◽  
...  
Keyword(s):  
Random Forest ◽  
Soft Computing ◽  
Sediment Load ◽  
Prediction Models ◽  
Absolute Error ◽  
Load Prediction ◽  
Statistical Parameters ◽  
Hydro Power ◽  
Input Combination ◽  
Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

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