Mechanistic flow modelling in pipes - What difference does it make?
Mechanistic flow modelling in pipes Pablo Adames September 11, 2012
Mechanistic flow modelling in pipes
Problem context
gathering systems trunk lines Network
tie ins
empirical correlations
Steady state software
mechanistic models Hydrodynamic models
inclination range
wells uni ed
90 to 0◦
-10 to +30◦ 90◦
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 bubble UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal stratified wavy UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flow UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flow II Higher liquid loading, more frequent slugs
Mechanistic flow modelling in pipes
Horizontal annular mist UofC, circa 1970
Mechanistic flow modelling in pipes
Slip and holdup in multiphase pipe flow
Fundamental to understanding multiphase flow Holdup: phase fraction Slip: 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 : L EL = AGA+A L
Mechanistic flow modelling in pipes
The Hydrodynamic Slip vslip = vG − vL The lighter phase will generally use energy more effectively to travel along faster. . . vslip > 0 But sometimes when descending. . . vslip < 0
Mechanistic flow modelling in pipes
Input and in situ phase fractions Why does phase fraction change in a pipe?
A recipe for phase fraction change: Ingredients 1 2 3 4 5 6
Inmiscible phases (they don’t blend) A pipeline Inertial forces (phase motion) Gravitational forces Dissipative forces (friction) 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 fractions An highway analogy
INPUT SECTION: Input ratio of trucks to cars:
3 3
= 1.0
Input fraction of trucks to trafic:
3 3+3
= 0.5
in situ SECTION: in situ ratio of trucks to cars:
6 3.5
in situ fraction of trucks to trafic:
= 1.71 6 6+3.5
= 0.63
Mechanistic flow modelling in pipes
Input vs. in situ fraction Analysis and conclusion
Top view of highway: Road ≈ pipeline Trucks ≈ liquid Cars ≈ gas
The in situ fraction of the slower moving vehicle/fluid is greater than its input fraction There is hydrodynamic retention in steady state of the heavier phase 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 up
Return 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 mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
Empirical versus mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
Empirical versus mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
History of the multiphase flow models
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspective on Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Mechanistic flow modelling in pipes
Mechanistic flow pattern maps Angle 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 model Between -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 pattern map 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 cut Inclination +0.25◦ , VSL = 0.05m/s, VsG = 1.3m/s, WC=1%
Mechanistic flow modelling in pipes
Effect of increasing water cut Inclination +0.25◦ , VSL = 0.05m/s, VsG = 1.3m/s, WC=5%
Mechanistic flow modelling in pipes
Effect of increasing water cut Inclination +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 velocity Inclination +1.0◦ , VSL = 0.1m/s, VsG = 1.7m/s, WC=10%
Mechanistic flow modelling in pipes
Effect of increasing the gas superficial velocity Inclination +1.0◦ , VSL = 0.1m/s, VsG = 2.5m/s, WC=10%
Mechanistic flow modelling in pipes
Main idea of unit cell model Fixed frame of reference
Mechanistic flow modelling in pipes
Main idea of unit cell model Moving frame of reference
Mechanistic flow modelling in pipes
A case Study
Now let us consider a real life case. . . Comparing published field measurements versus different model predictions.
Frigg to St. Fergus UK
Return http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Frigg to St. Fergus Slug catcher
http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Mechanistic flow modelling in pipes
Frigg to St. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Measured values
Group 1
Group 2
Group 3
Eq-gas MMsm3d 15.679 20.001 21.308 22.614 27.438 28.846 31.760 33.569 33.400 38.900 40.900 43.800 32.400 33.400 36.100 38.400 38.900 40.900 43.800
Measured values Pup Pdown bara bara 25.0 114.0 88.9 42.0 108.0 66.0 55.0 105.0 50.0 43.0 131.0 88.0 60.5 145.0 84.5 82.0 132.0 50.0 93.0 143.0 50.0 100.4 149.1 48.7 39.7 149.1 109.4 59.4 148.4 88.9 118.4 148.4 30.0 131.0 147.9 16.9 58.1 109.2 51.1 60.6 109.3 48.7 69.8 117.5 47.7 74.5 122.9 48.5 77.1 125.1 48.0 82.9 131.4 48.5 91.2 140.3 49.1
ΔP bar
Group 1: Frigg to St. Fergus; Group 2: Frigg to MCP01; Group 3: MCP01 to St. Fergus
Tup C 47.0 47.0 47.0 47.0 47.0 47.0 47.0 28.0 28.0 29.3 32.6 27.9 5.6 2.0 5.1 5.1 23.3 33.3 45.6
Field view
Results for pressure drop Relative error
Group 1
Group 2
Group 3
EatOli
B&B rev
B&B
-11.86 -1.59 -2.56 -5.59 -5.95 -1.55 -1.72 -1.40 -4.95 -5.17 -17.98 -11.60 -3.82 -1.82 -4.26 -3.44 -3.22 -4.51 -3.97 -5.10
0.93 -10.41 -12.80 -9.77 -18.85 -14.48 -14.94 -14.84 -22.83 -22.67 -28.28 -21.37 -16.56 -14.53 -16.59 -15.93 -15.42 -16.45 -15.70 -15.87
-19.85 -19.15 -19.28 -19.53 -24.80 -8.50 -19.35 -33.96 -28.36 -26.88 -27.27 -20.69 -18.45 -16.01 -17.74 -17.01 -16.45 -17.53 -16.90 -20.41
OliMec ei, % -10.26 2.46 1.62 -2.33 -3.30 1.50 1.07 1.19 -2.43 -2.48 -15.53 -9.24 -0.21 1.81 -0.97 -0.35 -0.11 -0.78 -1.33 -2.09
Xiao XiaoMod OLGAS2P 1.73 6.74 3.80 2.32 -0.66 2.36 1.50 0.30 -2.93 0.21 -16.88 1.30 -1.59 0.49 -2.40 -1.83 -1.66 -2.34 -2.76 -0.65
5.72 9.84 6.71 5.11 1.49 4.19 3.23 1.79 -1.17 1.56 -15.95 2.06 0.30 2.14 -0.97 -0.49 -0.37 -1.14 -1.77 1.17
-9.46 -0.16 -1.11 -4.42 -5.45 -0.82 -1.40 -1.20 -4.44 -5.51 -18.23 -11.76 -3.65 -1.66 -4.26 -3.71 -3.61 -4.27 -4.73 -4.73
Mechanistic flow modelling in pipes
Results for pressure drop Summary
ei |ei|
EatOli % -5.10 5.10
B&B rev % -15.87 15.96
B&B % -20.41 20.41
OliMec % -2.09 3.10
Xiao % -0.65 2.83
XiaoMod OLGAS2P % % 1.17 -4.73 3.47 4.73
Mechanistic flow modelling in pipes
Results for holdup Summary (only two measurements)
Measured Holdup 630 418
EatOli
Measured Holdup 630 418
EatOli
1624.0 1504.0
157.78 288.98
B&B rev 21271.0 18945.0
B&B rev 3276.35 4994.85
B&B 4953.0 4323.0
B&B
OliMec m3 308.0 294.0
OliMec ei, % 686.19 -51.11 1086.35 -26.23
Xiao 321.0 317.0
Xiao -49.05 -23.11
XiaoMod OLGAS2P 432.0 424.0
540.0 520.0
XiaoMod OLGAS2P -31.43 3.47
-14.29 29.34
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-liquid simulations
3
Mechanistic flow models are safer general purpose options
4
Mechanistic flow models are better holdup predictors
Thank you
Mechanistic flow modelling in pipes
Problem context
gathering systems trunk lines Network
tie ins
empirical correlations
Steady state software
mechanistic models Hydrodynamic models
inclination range
wells uni ed
90 to 0◦
-10 to +30◦ 90◦
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 bubble UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal stratified wavy UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flow UofC, circa 1970
Mechanistic flow modelling in pipes
Horizontal slug flow II Higher liquid loading, more frequent slugs
Mechanistic flow modelling in pipes
Horizontal annular mist UofC, circa 1970
Mechanistic flow modelling in pipes
Slip and holdup in multiphase pipe flow
Fundamental to understanding multiphase flow Holdup: phase fraction Slip: 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 : L EL = AGA+A L
Mechanistic flow modelling in pipes
The Hydrodynamic Slip vslip = vG − vL The lighter phase will generally use energy more effectively to travel along faster. . . vslip > 0 But sometimes when descending. . . vslip < 0
Mechanistic flow modelling in pipes
Input and in situ phase fractions Why does phase fraction change in a pipe?
A recipe for phase fraction change: Ingredients 1 2 3 4 5 6
Inmiscible phases (they don’t blend) A pipeline Inertial forces (phase motion) Gravitational forces Dissipative forces (friction) 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 fractions An highway analogy
INPUT SECTION: Input ratio of trucks to cars:
3 3
= 1.0
Input fraction of trucks to trafic:
3 3+3
= 0.5
in situ SECTION: in situ ratio of trucks to cars:
6 3.5
in situ fraction of trucks to trafic:
= 1.71 6 6+3.5
= 0.63
Mechanistic flow modelling in pipes
Input vs. in situ fraction Analysis and conclusion
Top view of highway: Road ≈ pipeline Trucks ≈ liquid Cars ≈ gas
The in situ fraction of the slower moving vehicle/fluid is greater than its input fraction There is hydrodynamic retention in steady state of the heavier phase 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 up
Return 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 mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
Empirical versus mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
Empirical versus mechanistic Why bother?
Mechanistic flow models Empirical flow correlations Developed by correlating dimensionless numbers
Developed from physical laws
Extrapolation is uncertain
Closed with empirical correlations
Interpolation can be very good
Reduced dependence on range of data
Can be simple to solve by hand
Usually computers needed to solve
Mechanistic flow modelling in pipes
History of the multiphase flow models
Source: Shippen, M., Steady-State Multiphase Flow -Past, Present, and Future, with a Perspective on Flow Assurance. Energy & Fuels, 2012. 26: p. 4145-4157.
Mechanistic flow modelling in pipes
Mechanistic flow pattern maps Angle 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 model Between -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 pattern map 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 still on. . . 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 cut Inclination +0.25◦ , VSL = 0.05m/s, VsG = 1.3m/s, WC=1%
Mechanistic flow modelling in pipes
Effect of increasing water cut Inclination +0.25◦ , VSL = 0.05m/s, VsG = 1.3m/s, WC=5%
Mechanistic flow modelling in pipes
Effect of increasing water cut Inclination +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 velocity Inclination +1.0◦ , VSL = 0.1m/s, VsG = 1.7m/s, WC=10%
Mechanistic flow modelling in pipes
Effect of increasing the gas superficial velocity Inclination +1.0◦ , VSL = 0.1m/s, VsG = 2.5m/s, WC=10%
Mechanistic flow modelling in pipes
Main idea of unit cell model Fixed frame of reference
Mechanistic flow modelling in pipes
Main idea of unit cell model Moving frame of reference
Mechanistic flow modelling in pipes
A case Study
Now let us consider a real life case. . . Comparing published field measurements versus different model predictions.
Frigg to St. Fergus UK
Return http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Frigg to St. Fergus Slug catcher
http://www.uk.total.com/pdf/Library/PUBLICATIONS/Library-StFergusBrochure.pdf
Mechanistic flow modelling in pipes
Frigg to St. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Description
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. Fergus Measured values
Group 1
Group 2
Group 3
Eq-gas MMsm3d 15.679 20.001 21.308 22.614 27.438 28.846 31.760 33.569 33.400 38.900 40.900 43.800 32.400 33.400 36.100 38.400 38.900 40.900 43.800
Measured values Pup Pdown bara bara 25.0 114.0 88.9 42.0 108.0 66.0 55.0 105.0 50.0 43.0 131.0 88.0 60.5 145.0 84.5 82.0 132.0 50.0 93.0 143.0 50.0 100.4 149.1 48.7 39.7 149.1 109.4 59.4 148.4 88.9 118.4 148.4 30.0 131.0 147.9 16.9 58.1 109.2 51.1 60.6 109.3 48.7 69.8 117.5 47.7 74.5 122.9 48.5 77.1 125.1 48.0 82.9 131.4 48.5 91.2 140.3 49.1
ΔP bar
Group 1: Frigg to St. Fergus; Group 2: Frigg to MCP01; Group 3: MCP01 to St. Fergus
Tup C 47.0 47.0 47.0 47.0 47.0 47.0 47.0 28.0 28.0 29.3 32.6 27.9 5.6 2.0 5.1 5.1 23.3 33.3 45.6
Field view
Results for pressure drop Relative error
Group 1
Group 2
Group 3
EatOli
B&B rev
B&B
-11.86 -1.59 -2.56 -5.59 -5.95 -1.55 -1.72 -1.40 -4.95 -5.17 -17.98 -11.60 -3.82 -1.82 -4.26 -3.44 -3.22 -4.51 -3.97 -5.10
0.93 -10.41 -12.80 -9.77 -18.85 -14.48 -14.94 -14.84 -22.83 -22.67 -28.28 -21.37 -16.56 -14.53 -16.59 -15.93 -15.42 -16.45 -15.70 -15.87
-19.85 -19.15 -19.28 -19.53 -24.80 -8.50 -19.35 -33.96 -28.36 -26.88 -27.27 -20.69 -18.45 -16.01 -17.74 -17.01 -16.45 -17.53 -16.90 -20.41
OliMec ei, % -10.26 2.46 1.62 -2.33 -3.30 1.50 1.07 1.19 -2.43 -2.48 -15.53 -9.24 -0.21 1.81 -0.97 -0.35 -0.11 -0.78 -1.33 -2.09
Xiao XiaoMod OLGAS2P 1.73 6.74 3.80 2.32 -0.66 2.36 1.50 0.30 -2.93 0.21 -16.88 1.30 -1.59 0.49 -2.40 -1.83 -1.66 -2.34 -2.76 -0.65
5.72 9.84 6.71 5.11 1.49 4.19 3.23 1.79 -1.17 1.56 -15.95 2.06 0.30 2.14 -0.97 -0.49 -0.37 -1.14 -1.77 1.17
-9.46 -0.16 -1.11 -4.42 -5.45 -0.82 -1.40 -1.20 -4.44 -5.51 -18.23 -11.76 -3.65 -1.66 -4.26 -3.71 -3.61 -4.27 -4.73 -4.73
Mechanistic flow modelling in pipes
Results for pressure drop Summary
ei |ei|
EatOli % -5.10 5.10
B&B rev % -15.87 15.96
B&B % -20.41 20.41
OliMec % -2.09 3.10
Xiao % -0.65 2.83
XiaoMod OLGAS2P % % 1.17 -4.73 3.47 4.73
Mechanistic flow modelling in pipes
Results for holdup Summary (only two measurements)
Measured Holdup 630 418
EatOli
Measured Holdup 630 418
EatOli
1624.0 1504.0
157.78 288.98
B&B rev 21271.0 18945.0
B&B rev 3276.35 4994.85
B&B 4953.0 4323.0
B&B
OliMec m3 308.0 294.0
OliMec ei, % 686.19 -51.11 1086.35 -26.23
Xiao 321.0 317.0
Xiao -49.05 -23.11
XiaoMod OLGAS2P 432.0 424.0
540.0 520.0
XiaoMod OLGAS2P -31.43 3.47
-14.29 29.34
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-liquid simulations
3
Mechanistic flow models are safer general purpose options
4
Mechanistic flow models are better holdup predictors
Thank you
