Transcript Foam Flow Project - University of Tulsa

Foam Flow Projects
Mohan Kelkar and Cem Sarica
The University of Tulsa
Foam Flow Projects: January 23, 2014
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Outline
• Introduction
• Background
• Objectives
• Current Status
• Future Work
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Introduction
• Foam flow is the most suitable artificial lift
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method for many tight, deep, gas wells
No correlation exists for pressure drop
prediction in foam flow
Need a model to correctly predict the ratepressure drop relationship under foam flow
conditions
Need to know the limits of foam flow
application
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Background
• This project is a joint effort between
funding by RPSEA and six companies
• The six member companies are:
Chevron, Marathon, Shell,
ConocoPhillips, Nalco, and MultiChem
• The Project got officially kicked off in
December, 2010 and have been
extended through December of 2014
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Objectives of the Project
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Collect experimental foam flow data under controlled
conditions for two different tubing sizes and five
different foams
Measure surface tension, viscosity and foam stability
Develop comprehensive correlation to predict
pressure drop under foam flow conditions
Develop guidelines for using appropriate
concentration of surfactant as well as minimum gasliquid ratios
Validate the model by comparing the results with field
data provided by participating companies
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Current Status
• Three graduate students are
working or have worked on the
project
Shu Luo – PhD Student, (finished
Dec 2013)
Ayantayo Ajani – PhD Student
Anton Skopich – MS Student (finished
Dec 2012)
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Current Status …
• The project is divided into three
phases
Experimental data in large scale
facility
Experimental data in small scale
facility
Modeling of the data
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Current Status …
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We have collected data in large scale facility for three
different surfactants and at various concentrations
We have collected data in small scale facilities for
three surfactants at various concentrations. In
addition, we have also collected data including
effects of brine concentration and temperature
A new definition of liquid loading is introduced and a
new method to predict liquid loading has been
proposed. The method is validated with large amount
of field data.
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Future Work
• Data will be collected on two additional
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surfactants in both small scale and large
scale facilities
Model will be built to estimate gas holdup
in large scale facility based on small scale
data
Model will be built to understand the
inception of liquid loading under foam flow
Model will be built to estimate pressure
drop for foam flow conditions
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