scholarly journals Correction to: Experimental investigation of high-viscosity oil–water flow in vertical pipes: flow patterns and pressure gradient

2019 ◽  
Vol 9 (4) ◽  
pp. 2919-2919
Author(s):  
Tarek Ganat ◽  
Syahrir Ridha ◽  
Meftah Hrairi ◽  
Juhairi Arisa ◽  
Raoof Gholami
Keyword(s):  
Experimental Investigation ◽  
Pressure Gradient ◽  
Water Flow ◽  
High Viscosity ◽  
Flow Patterns ◽  
Oil Water ◽  
High Viscosity Oil

A Machine Learning Approach to Predict the Pressure Gradient of Different Oil-Water Flow Patterns in a Horizontal Wellbore

2021 ◽  
Author(s):  
MD Ferdous Wahid ◽  
Reza Tafreshi ◽  
Zurwa Khan ◽  
Albertus Retnanto
Keyword(s):  
Machine Learning ◽  
Surface Tension ◽  
Surface Roughness ◽  
Pressure Gradient ◽  
Water Flow ◽  
Flow Patterns ◽  
Data Driven ◽  
Pressure Gradients ◽  
Horizontal Wellbore ◽  
Oil Water

Abstract Fluid pressure gradient in a wellbore plays a significant role to efficiently transport between source and separator facilities. The mixture of two immiscible fluids manifests in various flow patterns such as stratified, dispersed, intermittent, and annular flow, which can significantly influence the fluid’s pressure gradient. However, previous studies have only used limited flow patterns when developing their data-driven model. The aim of this study is to develop a uniform data-driven model using machine-learning (ML) algorithms that can accurately predict the pressure gradient for the oil-water flow with two stratified and seven dispersed flow patterns in a horizontal wellbore. Two different machine-learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were employed to predict the pressure gradients. A total of 662 experimental points from nine different flow patterns were extracted from five sources that include twelve variables for different physical properties of oil-water, wellbore’s surface roughness, and input diameter. The variables are entrance length to diameter ratio, oil and water viscosity, density, velocity, and surface tension, between oil and water surface tension, surface roughness, input diameter, and flow pattern. The algorithms’ performance was evaluated using median absolute percentage error (MdAPE) and root mean squared error (RMSE). A repeated train-test split strategy was used where the final MdAPE and RMSE were computed from the average of all repetitions. The MdAPE and RMSE for the prediction of pressure gradients are 13.89% and 0.138 kPa/m using RF and 12.17% and 0.088 kPa/m using ANN, respectively. The ML algorithms’ ability to model the pressure gradient is demonstrated using measured vs. predicted analysis where the experimental data points are mostly located in close proximity of the diagonal line, indicating a suitable generalization of the models. Comparing the performance between RF and ANN shows that the latter algorithm’s prediction accuracy is significantly better (p<0.01).

Experimental Study for Characterization of Oil-Water Flow Patterns in Horizontal Pipes Based on Visualization and Pressure-Gradient Analysis

2020 ◽  
Author(s):  
Hermes Vazzoler Junior ◽  
Daiane Mieko Iceri ◽  
Juliana Cenzi ◽  
Carlos Keiichi Tanikawa da Silva ◽  
Charlie van der Geest ◽  
...  
Keyword(s):  
Experimental Study ◽  
Pressure Gradient ◽  
Water Flow ◽  
Gradient Analysis ◽  
Flow Patterns ◽  
Horizontal Pipes ◽  
Oil Water

Experimental Study on High Viscosity Oil/Water Flow in Horizontal and Vertical Pipes

2009 ◽  
Author(s):  
Duc Huu Vuong ◽  
Hong-Quan Zhang ◽  
Cem Sarica ◽  
Mingxiu Li
Keyword(s):  
Experimental Study ◽  
Water Flow ◽  
High Viscosity ◽  
Oil Water ◽  
High Viscosity Oil

Modeling High-Viscosity Oil/Water Cocurrent Flows in Horizontal and Vertical Pipes

SPE Journal ◽  
2012 ◽  
Vol 17 (01) ◽  
pp. 243-250 ◽  
Author(s):  
H.Q.. Q. Zhang ◽  
D.H.. H. Vuong ◽  
C.. Sarica
Keyword(s):  
Water Flow ◽  
Oil Recovery ◽  
Heavy Oil ◽  
Flow Behavior ◽  
Model Performance ◽  
High Viscosity ◽  
Low Viscosity ◽  
Oil Water ◽  
Mixing Length Theory ◽  
High Viscosity Oil

Summary Water is produced along with heavy oil either during the primary production or during enhanced oil recovery. Therefore, cocurrent oil/water flow is a common occurrence in heavy-oil production and transportation. Production-system design is strongly dependent on accurate predictions of the oil-/water-flow behavior. The predictions of previous mechanistic models for pressure gradient and water holdup are tested with the data acquired, and significant discrepancies are identified, especially for horizontal flow (Vuong 2009). The model performance is largely dependent on the predictions of phase inversion, distribution, and interaction. On the basis of the new understandings from experimental observations, the Zhang and Sarica (2006) unified model is modified by adding a new closure relationship for water-wetted-wall fraction in stratified flow and a new interfacial shear model based on mixing-length theory. The new model is compared with both high-viscosity and low-viscosity oil-/water-flow experimental results, and significant improvements are observed.

Experimental investigation on flow patterns and pressure gradient through two pipe diameters in horizontal oil–water flows

2014 ◽  
Vol 122 ◽  
pp. 266-273 ◽  
Author(s):  
T. Al-Wahaibi ◽  
Y. Al-Wahaibi ◽  
A. Al-Ajmi ◽  
R. Al-Hajri ◽  
N. Yusuf ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Pressure Gradient ◽  
Flow Patterns ◽  
Water Flows ◽  
Oil Water

Identification and Classification of New Three-Phase Gas/Oil/Water Flow Patterns

2007 ◽  
Author(s):  
Cengizhan Keskin ◽  
Hong-Quan Zhang ◽  
Cem Sarica
Keyword(s):  
Water Flow ◽  
Flow Patterns ◽  
Gas Oil ◽  
Three Phase ◽  
Oil Water

Effect of oil viscosity on the flow structure and pressure gradient in horizontal oil–water flow

2012 ◽  
Vol 90 (8) ◽  
pp. 1019-1030 ◽  
Author(s):  
N. Yusuf ◽  
Y. Al-Wahaibi ◽  
T. Al-Wahaibi ◽  
A. Al-Ajmi ◽  
A.S. Olawale ◽  
...  
Keyword(s):  
Pressure Gradient ◽  
Water Flow ◽  
Flow Structure ◽  
Oil Viscosity ◽  
Oil Water

scholarly journals A CFD study on horizontal oil-water flow with high viscosity ratio

2021 ◽  
Vol 229 ◽  
pp. 116097
Author(s):  
Jing Shi ◽  
Mustapha Gourma ◽  
Hoi Yeung
Keyword(s):  
Water Flow ◽  
Viscosity Ratio ◽  
High Viscosity ◽  
Oil Water
Sign in / Sign up

Export Citation Format

Share Document