Improvement of the well performance optimization methodology based on integrated modeling

2015 ◽  
Author(s):  
S. M. Bikbulatov ◽  
D. S. Vorobyev ◽  
A. Y. Smirnov ◽  
I. R. Mukminiov ◽  
S. V. Romashkin
Keyword(s):  
Performance Optimization ◽  
Integrated Modeling ◽  
Well Performance ◽  
Optimization Methodology

Improvement of the well performance optimization methodology based on integrated modeling (Russian)

2015 ◽  
Author(s):  
S. M. Bikbulatov ◽  
D. S. Vorobyev ◽  
A. Y. Smirnov ◽  
I. R. Mukminiov ◽  
S. V. Romashkin
Keyword(s):  
Performance Optimization ◽  
Integrated Modeling ◽  
Well Performance ◽  
Optimization Methodology

Coupled Completion and Reservoir Simulation Technology for Well Performance Optimization

2009 ◽  
Author(s):  
Marcel A. Grubert ◽  
Jing Wan ◽  
Sartaj S. Ghai ◽  
Silviu Livescu ◽  
William P. Brown ◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
Performance Optimization ◽  
Well Performance ◽  
Simulation Technology

scholarly journals A Study of Pressure Gradient in Multiphase Flow in Vertical Pipes

2019 ◽  
Vol 4 (1) ◽  
pp. 54-59
Author(s):  
David Nwobisi Wordu ◽  
Felix J. K. Ideriah ◽  
Barinyima Nkoi
Keyword(s):  
Pressure Drop ◽  
Multiphase Flow ◽  
Pressure Gradient ◽  
Performance Optimization ◽  
Field Data ◽  
Oil And Gas ◽  
Liquid Velocity ◽  
Petroleum Industry ◽  
Well Performance ◽  
Flowing Fluid

The study of multiphase flow in vertical pipes is aimed at effective and accurate design of tubing, surface facilities and well performance optimization for the production of oil and gas in the petroleum industry by developing a better approach for predicting pressure gradient. In this study, field data was analyzed using mathematical model, multiphase flow correlations, statistical model, and computer programming to predict accurately the flow regime, liquid holdup and pressure drop gradient which are important in the optimization of well. A Computer programme was used to prediction pressure drop gradient. Four dimensionless parameters liquid velocity number (Nlv), gas velocity number (Ngv), pipe diameter number (Nd), liquid viscosity number (Nl), were chosen because they represent an integration of the two dominant components that influence pressure drop in pipes. These dominant component are flow channel/media and the flowing fluid. The model was found to give a fit of 100% to the selected data points. Hagedorn & Brown, Griffith &Wallis correlations and model were compared with field data and the overall pressure gradient for a total depth of 10000ft was predicted. The predicted pressure gradient measured was found to be 0.320778psi/ft, Graffith& Wallis gave 0.382649Psi/ft, Hagedorn & Brown gave 0.382649Psi/ft; whereas generated model gave 0.271514Psi/ft. These results indicate that the model equation generated is better and leads to a reasonably accurate prediction of pressure drop gradient according to measured pressure gradient.

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