mechanistic flow modelling in pipes
TRANSCRIPT
September 11, 2012Pablo Adames
in pipesMechanistic flow modelling
Mechanistic flow modelling in pipes
Problem context
Steady
state
software
Hydro-dynamicmodels
empirical
corre-
lations
mechanis-
tic
models
inclination
range-10 to
+30◦
90◦
90 to
0◦
uni�ed
Network
wells
tie ins
trunk
lines
gathering
systems
Mechanistic flow modelling in pipes
The problem
What difference does it make
to select one type of hydrodynamic model
over the other?
Mechanistic flow modelling in pipes
Basic concepts
To understand how gas-liquid flow models were developed:
Flow patterns
Slip and holdup
Angle of inclination
Model types: Analytical ⇔ mechanistic ⇔ empirical
Fluid properties
Mechanistic flow modelling in pipes
Horizontal gas-liquid flow patterns
Mechanistic flow modelling in pipes
Flow pattern observation
In the following slides
we will look at early
flow pattern observations
Mechanistic flow modelling in pipes
Horizontal dispersed bubbleUofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal stratified wavyUofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flowUofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flow IIHigher liquid loading, more frequent slugs
Mechanistic flow modelling in pipes
Horizontal annular mistUofC, circa 1970
Mechanistic flow modelling in pipes
Slip and holdup in multiphase pipe flow
Fundamental to understanding multiphase flowHoldup: phase fractionSlip: relative phase velocity
Holdup and slip change in a flowing system
Their changes are interrelated
There is no equivalent concept in single phase flow
Mechanistic flow modelling in pipes
Holdup as a fraction
Stratified flow
Concept applicable to all flow patterns
Gas phase flows on top
Liquid flows in the bottom
Area fraction of liquid, EL:EL = AL
AG +AL
Mechanistic flow modelling in pipes
The Hydrodynamic Slip
vslip = vG − vL
The lighter phase will generally use energy more effectively totravel along faster. . .
vslip > 0
But sometimes when descending. . .
vslip < 0
Mechanistic flow modelling in pipes
Input and in situ phase fractionsWhy does phase fraction change in a pipe?
A recipe for phase fraction change:Ingredients
1 Inmiscible phases (they don’t blend)2 A pipeline3 Inertial forces (phase motion)4 Gravitational forces5 Dissipative forces (friction)6 Residence time
Hydrodynamic phase separation
Input phase fraction changes downstream
Each phase uses energy differently
Mechanistic flow modelling in pipes
Input vs. in situ phase fractionsAn highway analogy
Mechanistic flow modelling in pipes
Input vs. in situ fractionAnalysis and conclusion
Top view of highway:
Road ≈ pipelineTrucks ≈ liquidCars ≈ gas
The in situ fraction of the slower moving vehicle/fluid isgreater than its input fraction
There is hydrodynamic retention in steady state of the heavierphase
This is known as the holdup or slip effect
Mechanistic flow modelling in pipes
The first flow pattern maps
Mandhane et al. horizontal Aziz et al. vertical upReturn
Source: Engineering Data Book, Gas Processors Suppliers Association, 2004. 12th Edition FPS,Tulsa, Oklahoma
Mechanistic flow modelling in pipes
Types of flow models
Analytical: built with first principles
Empirical: built from observations alone
Mechanistic: built from general laws and observations
Mechanistic flow modelling in pipes
Empirical versus mechanisticWhy bother?
Empirical flow correlations
Developed by correlatingdimensionless numbers
Extrapolation is uncertain
Interpolation can be verygood
Can be simple to solve byhand
Mechanistic flow models
Developed from physicallaws
Closed with empiricalcorrelations
Reduced dependence onrange of data
Usually computers needed tosolve
Mechanistic flow modelling in pipes
Empirical versus mechanisticWhy bother?
Empirical flow correlations
Developed by correlatingdimensionless numbers
Extrapolation is uncertain
Interpolation can be verygood
Can be simple to solve byhand
Mechanistic flow models
Developed from physicallaws
Closed with empiricalcorrelations
Reduced dependence onrange of data
Usually computers needed tosolve
Mechanistic flow modelling in pipes
Empirical versus mechanisticWhy bother?
Empirical flow correlations
Developed by correlatingdimensionless numbers
Extrapolation is uncertain
Interpolation can be verygood
Can be simple to solve byhand
Mechanistic flow models
Developed from physicallaws
Closed with empiricalcorrelations
Reduced dependence onrange of data
Usually computers needed tosolve
Mechanistic flow modelling in pipes
History of the multiphase flow models
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspectiveon Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Next Slide
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspectiveon Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Next Slide
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspectiveon Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Next Slide
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspectiveon Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Next Slide
Mechanistic flow modelling in pipes
Mechanistic flow pattern mapsAngle of inclination dependency
Remember the Mandhane and Aziz et al flow pattern maps?
First Flow Maps
What happens between horizontal and vertical?Only the mechanistic flow pattern maps answer that question
Let’s see an example using the Xiao et al Mechanistic modelBetween -10 and +10 degrees of inclination with the horizontal
Mechanistic flow modelling in pipes
The angle sensitivity of flow patterns
Mechanistic flow modelling in pipes
The operating line and the flow pattern map(s)?
Mechanistic flow modelling in pipes
Concluding on angle dependency
Would you use a single empirical flow patternmap for the whole pipeline again?
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Flow model challenge
What is the nature of the modelling challenge?Too many variables for a simple cause effect analysis
There are good models but the search for understanding is stillon. . .
Three-phase flow patterns
Heavy oils
Sand transport
Non Newtonian rheologies
Transient real time simulation
Complex fluids
Mechanistic flow modelling in pipes
Effect of increasing water cutInclination +0.25◦, VSL = 0.05m/s, VsG = 1.3m/s, WC=1%
Mechanistic flow modelling in pipes
Effect of increasing water cutInclination +0.25◦, VSL = 0.05m/s, VsG = 1.3m/s, WC=5%
Mechanistic flow modelling in pipes
Effect of increasing water cutInclination +0.25◦, VSL = 0.05m/s, VsG = 1.3m/s, WC=10%
Mechanistic flow modelling in pipes
Effect of increasing inclination to +1◦
Inclination +1.0◦, VSL = 0.1m/s, VsG = 1.4m/s, WC=10%
Mechanistic flow modelling in pipes
Effect of increasing the gas superficial velocityInclination +1.0◦, VSL = 0.1m/s, VsG = 1.7m/s, WC=10%
Mechanistic flow modelling in pipes
Effect of increasing the gas superficial velocityInclination +1.0◦, VSL = 0.1m/s, VsG = 2.5m/s, WC=10%
Mechanistic flow modelling in pipes
Main idea of unit cell modelFixed frame of reference
Mechanistic flow modelling in pipes
Main idea of unit cell modelMoving frame of reference
Mechanistic flow modelling in pipes
A case Study
Now let us consider a real life case. . .
Comparing published field measurementsversus different model predictions.
Frigg to St. FergusUK
Return
http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Frigg to St. FergusSlug catcher
http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Mechanistic flow modelling in pipes
Frigg to St. FergusDescription
d i =774 mm (30.5 inch)
Frigg to MCP01: 188.4km, 15 point elevation profile
MCP01 to St. Fergus: 175km, 15 point elevation profile
Detailed gas composition
Specified variables: Pdowstream, Tupstream
Calculated variable: Pupstream
Mechanistic flow modelling in pipes
Frigg to St. FergusDescription
d i =774 mm (30.5 inch)
Frigg to MCP01: 188.4km, 15 point elevation profile
MCP01 to St. Fergus: 175km, 15 point elevation profile
Detailed gas composition
Specified variables: Pdowstream, Tupstream
Calculated variable: Pupstream
Mechanistic flow modelling in pipes
Frigg to St. FergusDescription
d i =774 mm (30.5 inch)
Frigg to MCP01: 188.4km, 15 point elevation profile
MCP01 to St. Fergus: 175km, 15 point elevation profile
Detailed gas composition
Specified variables: Pdowstream, Tupstream
Calculated variable: Pupstream
Mechanistic flow modelling in pipes
Frigg to St. FergusDescription
d i =774 mm (30.5 inch)
Frigg to MCP01: 188.4km, 15 point elevation profile
MCP01 to St. Fergus: 175km, 15 point elevation profile
Detailed gas composition
Specified variables: Pdowstream, Tupstream
Calculated variable: Pupstream
Mechanistic flow modelling in pipes
Frigg to St. FergusDescription
d i =774 mm (30.5 inch)
Frigg to MCP01: 188.4km, 15 point elevation profile
MCP01 to St. Fergus: 175km, 15 point elevation profile
Detailed gas composition
Specified variables: Pdowstream, Tupstream
Calculated variable: Pupstream
Frigg to ST. FergusMeasured values Measured values
Eq-gas ΔP Pup Pdown Tup
MMsm3d bar bara bara C
Group 1
15.679 25.0 114.0 88.9 47.0
20.001 42.0 108.0 66.0 47.0
21.308 55.0 105.0 50.0 47.0
22.614 43.0 131.0 88.0 47.0
27.438 60.5 145.0 84.5 47.0
28.846 82.0 132.0 50.0 47.0
31.760 93.0 143.0 50.0 47.0
33.569 100.4 149.1 48.7 28.0
Group 2
33.400 39.7 149.1 109.4 28.0
38.900 59.4 148.4 88.9 29.3
40.900 118.4 148.4 30.0 32.6
43.800 131.0 147.9 16.9 27.9
Group 3
32.400 58.1 109.2 51.1 5.6
33.400 60.6 109.3 48.7 2.0
36.100 69.8 117.5 47.7 5.1
38.400 74.5 122.9 48.5 5.1
38.900 77.1 125.1 48.0 23.3
40.900 82.9 131.4 48.5 33.3
43.800 91.2 140.3 49.1 45.6
Group 1: Frigg to St. Fergus; Group 2: Frigg to MCP01; Group 3: MCP01 to St. Fergus Field view
Results for pressure dropRelative error
EatOli B&B rev B&B OliMec Xiao XiaoMod OLGAS2P
-11.86 0.93 -19.85 -10.26 1.73 5.72 -9.46
-1.59 -10.41 -19.15 2.46 6.74 9.84 -0.16
-2.56 -12.80 -19.28 1.62 3.80 6.71 -1.11
-5.59 -9.77 -19.53 -2.33 2.32 5.11 -4.42
-5.95 -18.85 -24.80 -3.30 -0.66 1.49 -5.45
-1.55 -14.48 -8.50 1.50 2.36 4.19 -0.82
-1.72 -14.94 -19.35 1.07 1.50 3.23 -1.40
-1.40 -14.84 -33.96 1.19 0.30 1.79 -1.20
-4.95 -22.83 -28.36 -2.43 -2.93 -1.17 -4.44
-5.17 -22.67 -26.88 -2.48 0.21 1.56 -5.51
-17.98 -28.28 -27.27 -15.53 -16.88 -15.95 -18.23
-11.60 -21.37 -20.69 -9.24 1.30 2.06 -11.76
-3.82 -16.56 -18.45 -0.21 -1.59 0.30 -3.65
-1.82 -14.53 -16.01 1.81 0.49 2.14 -1.66
-4.26 -16.59 -17.74 -0.97 -2.40 -0.97 -4.26
-3.44 -15.93 -17.01 -0.35 -1.83 -0.49 -3.71
-3.22 -15.42 -16.45 -0.11 -1.66 -0.37 -3.61
-4.51 -16.45 -17.53 -0.78 -2.34 -1.14 -4.27
-3.97 -15.70 -16.90 -1.33 -2.76 -1.77 -4.73
-5.10 -15.87 -20.41 -2.09 -0.65 1.17 -4.73
Group 1
Group 2
Group 3
ei, %
Mechanistic flow modelling in pipes
Results for pressure dropSummary
EatOli B&B rev B&B OliMec Xiao XiaoMod OLGAS2P
% % % % % % %
ei -5.10 -15.87 -20.41 -2.09 -0.65 1.17 -4.73
|ei| 5.10 15.96 20.41 3.10 2.83 3.47 4.73
Mechanistic flow modelling in pipes
Results for holdupSummary (only two measurements)
Measured EatOli B&B rev B&B OliMec Xiao XiaoMod OLGAS2P
Holdup
630 1624.0 21271.0 4953.0 308.0 321.0 432.0 540.0
418 1504.0 18945.0 4323.0 294.0 317.0 424.0 520.0
Measured EatOli B&B rev B&B OliMec Xiao XiaoMod OLGAS2P
Holdup
630 157.78 3276.35 686.19 -51.11 -49.05 -31.43 -14.29
418 288.98 4994.85 1086.35 -26.23 -23.11 3.47 29.34
m3
ei, %
Mechanistic flow modelling in pipes
Conclusions
1 The angle of inclination is very important for gas-liquid flow
2 Flow pattern maps provide insight into pipeline gas-liquidsimulations
3 Mechanistic flow models are safer general purpose options
4 Mechanistic flow models are better holdup predictors
Thank you