Erosion Modeling and Sand
Management with ANSYS CFD
Madhusuden Agrawal
ANSYS Houston
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© 2011 ANSYS, Inc.
June 21, 2012
OUTLINE
Particulate modeling in ANSYS CFD
Sand Control and Sand Management
• Sand Filtration
• Sand Transport in pipelines
• Proppant Placement
Erosion Modeling
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Challenges in Erosion Modeling
Key components of erosion modeling
ANSYS solution for erosion modeling
Erosion Module
Examples
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June 21, 2012
Recap: Particulate Modeling
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© 2011 ANSYS, Inc.
June 21, 2012
Challenges in Particulate Modeling
Spans wide range of
• Length scales
• Time scales
Physics
• Particulate physics
• Fluid particle interaction
• Particle size distribution
• Homogenous and
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heterogeneous reaction
Particle structure interaction
© 2011 ANSYS, Inc.
June 21, 2012
From: Fundamental of Multiphase Flow, C. E. Brennen
Particulate Flows Regimes
Diluted vs. Dense Flow
t12/tcol
t12/teddy
102
100
10-2
104
102
1-way
coupling
2-way
coupling
Particles
enhance
turbulence
negligible
effect on
turbulence
Particles
reduce
turbulence
100
10-7
10-2
10-5
102
dilute
Relative motion between
particles
Particle-particle interaction
Apparent viscosity of the
solid phase
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© 2011 ANSYS, Inc.
June 21, 2012
4-way
coupling
10-3
10-1
101
(x1-x2)/dp
dense
Dilute
Dense
Large
Small
Weak
Strong
Particle-fluid
interactions
Particle-particle
interaction
es
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Modeling Particulate Flow
Lagrangian
Eulerian
Particle Phase
Hybrid
Sub grid scale
Particle size
Super grid scale
Resolved
P-P Interaction
Modeled
Resolved
Fluid-P Interaction
Modeled
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© 2011 ANSYS, Inc.
June 21, 2012
Platform for Simulating Particulate Systems
ANSYS CFD provides a platform which
can adapt to the multi-physics, multicomponents and multi-scale
configurations of particulate flows and
their industrial applications
Eulerian Granular
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© 2011 ANSYS, Inc.
DPM
June 21, 2012
DDPM-DEM
MPM
Models for Particulate Flows
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Model
Numerical
approach
Particle fluid
interaction
Particle-Particle
interaction
Particle size
distribution
DPM
Fluid – Eulerian
Particles – Lagrangian
Empirical models for
sub-grid particles
Particles are treated
as points
Easy to include PSD
because of Lagrangian
description
DDPM - KTGF
Fluid – Eulerian
Particles – Lagrangian
Empirical; sub-grid
particles
Approximate P-P
interactions
determined by
granular models
Easy to include PSD
because of Lagrangian
description
DDPM - DEM
Fluid – Eulerian
Particles – Lagrangian
Empirical; sub-grid
particles
Accurate
determination of PP interactions.
Can account for all PSD
physics accurately
including geometric
effects
Euler Granular
model
Fluid – Eulerian
Particles – Eulerian
Empirical; sub-grid
particles
P-P interactions
modeled by fluid
properties, such as
granular pressure,
viscosity, drag etc.
Different phases to
account for a PSD; when
size change operations
happen use population
balance models
Macroscopic
Particle Model
Fluid – Eulerian
Particles – Lagrangian
Interactions
determined as part
of solution; particles
span many fluid cells
Accurate
determination of PP interactions.
Easy to include PSD; if
particles become
smaller than the mesh,
uses an empiricial model
© 2011 ANSYS, Inc.
June 21, 2012
Sand Control
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© 2011 ANSYS, Inc.
June 21, 2012
Sedimentation in Oil & Gas
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Sand is often produced in both onshore and offshore
production systems
• Sand production may be continuous, or sudden
•
The sediment consists mud, sand and scale picked up during
the transport of the oil
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Sand Management is important in oil production to ensure
system integrity and efficiency
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Excessive sand leads to
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Partial or complete blockage of flowlines
Enhanced pipe bottom corrosion and erosion
Trapping of pigs
Reduced production time and increased
maintenance and operating costs
© 2011 ANSYS, Inc.
June 21, 2012
Internal flow of natural gas containing sand particles. particle trajectories are
colored in grey. The erosive wear hotspots on the piping is colored out in red.
Sand Control
Sand control strategies
• Preventing formation failure
• Sand exclusion techniques
• Sand management
Key areas to understand fundamental nature of sand in the
reservoir and the wellbore
Hydraulic fracturing (Proppant transport)
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June 21, 2012
Sand Exclusion
Techniques
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June 21, 2012
Example: Sand Filtering Systems in O&G
Sand control screen systems
• Screens
• Gravel and frac packing
Bulk process
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June 21, 2012
Surface process
Modeling Filtration with ANSYS
Euler Granular Model
• Porous media model with physical velocity formulation
• Low permeability for the particulate phase
• May not be able to simulate particle size dependent filtering
Particulate Models
• DDPM model with DEM closure for particle-particle interaction
• Particles can be stopped by reflect or trap boundary conditions
• Can model particle size effects.
• Macro Particle Model will physically filter particles through
pores
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© 2011 ANSYS, Inc.
June 21, 2012
Euler Granular Model for Filtration
t = 16 sec.
t = 60 sec.
t = 100 sec.
t = 135 sec.
Solid Phase Volume Faction Contours
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© 2011 ANSYS, Inc.
June 21, 2012
Velocity Vectors of Solid Phase
Filter Cake Formation in Vertical Wells…
Journal of Petroleum and Gas Engineering Vol. 2(7), pp. 146-164, November 2011
Mohd. A. Kabir and Isaac K. Gamwo
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© 2011 ANSYS, Inc.
June 21, 2012
Filtration Modeling Using DDPM/DEM
Outlet
Inlet
Filter: Allows particles below a threshold
to pass through, Filter represented by a
internal boundary condition.
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June 21, 2012
Filtration Modeling using MPM
Particle separation through a
filter element at three
instances in time. The flow is
from left to right. The small
particles flow through the
holes in the perforated plate
and exit the pipe on the right.
The plate blocks the bigger
particles.
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© 2011 ANSYS, Inc.
June 21, 2012
Particulate Migration in Gravel Pack
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Micro scale Simulation for fine particles
transport through pores in gravel pack
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Study Permeability alterations in the gravel
pack due to fine particles entrainments,
transport and deposition
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Filtration of fine particles
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June 21, 2012
Sand Transport
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© 2011 ANSYS, Inc.
June 21, 2012
Example: Sand Transport in Pipelines
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Sand-Water slurry flow in
horizontal pipe
• Pipe diameter D = 0.0505m
• Pipe length L = 4m
Gravity
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30% volume loading
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Four Different Slurry Flow Rates
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Expected Results
DDPM with DEM Collision
• Particle staggering for surface
injection
• Low value of Spring Constant as
buoyancy force is important.
dp/dx
• Almost 3 millions parcels
(Pa/m)
To be published in collaboration with Shell
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© 2011 ANSYS, Inc.
June 21, 2012
SRC: Saskatchewan Research Council
Slurry Velocity (m/s)
Results: Pressure Gradient
•
Mean Static Pressure is plotted on the line coinciding with the
axis of the pipe.
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dp/dx is calculated between z=3m to z=4m as it varies linearly
in this range for all the cases.
dp/dx
pipe length
dp/dx
slurry velocity
dp/dx
(Pa/m)
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© 2011 ANSYS, Inc.
June 21, 2012
Slurry Velocity (m/s)
Effect of particle time step size
Mixture Velocity (m/s)
Baseline Particle Time
Step Size (s)
Smaller Particle Time
Step Size (s)
0.7
2.50E-04
1.0E-04
1.42
1.00E-04
4.00E-05
3
5.00E-05
2.50E-05
• Reduced particle time step
size to more accurately
model collisions.
– Little difference in predicted
pressure gradient.
– Considerable increase in
simulation time.
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© 2011 ANSYS, Inc.
June 21, 2012
dp/dx
slurry velocity
dp/dx
(Pa/m)
Slurry Velocity (m/s)
Sand Transport in Pipelines
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It is important to keep particles suspended
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Critical flow velocity which keeps sand particles moving along
the pipe depends on
• Liquid holdup and flow rates, Pipe diameter, Fluids properties, Sand
properties, Pipe inclination angle
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Many correlations exists
for solids transportation
in multiphase flow
• Based on experiments for
single phase flow on small pipes
• Lot of variability in measurements
Hjulstrom Diagram
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June 21, 2012
Sand Transport in Pipelines
Transport paths
• Traction or full contact
– sand rolling or sliding across bottom
• Saltation
– sand hop/ bounce along bottom
• Bedload
– combined traction and saltation
• Suspended load
– sand carried without settling
– upward forces > downwarde
All these paths for sand transport can be addressed by
Particulate modeling in ANSYS CFD.
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© 2011 ANSYS, Inc.
June 21, 2012
Particle Transport – MPM Simulation
Demonstrate Case of Particle Lift Off using MPM
• Geometry of a long narrow channel
• Steady state periodic flow profile applied at Inlet
• A 200 microns diameter particle was placed on bottom of the
•
channel
Advanced Turbulence Model
Fine mesh
(about 4 fluid cells
across particle diameter)
Initial Location of the Particle
Flow Direction
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© 2011 ANSYS, Inc.
June 21, 2012
Particle Transport – MPM Simulation
Lift Force Va lida tion in MPM
Distance from Wall
75
70
65
60
Distance from Wall (microns)
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Particle Trajectory
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45
40
35
30
25
20
15
10
5
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Axial Dis tance (m icr ons )
Axial Distance (in microns)
MPM is a DNS technique which calculates particle forces directly from
pressure and flow field
MPM automatically predicts particle lift force without including any lift force
correlation (Saffman etc)
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June 21, 2012
Proppant Placement
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© 2011 ANSYS, Inc.
June 21, 2012
Example: Proppant Transport
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Complex multiphase flow problem
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Proppant settles to the bottom – Mound develops –
Reaches an equilibrium height
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Until the equilibrium height – Proppant bed gets higher
and then it spreads laterally
Reference:
Patankar, N.A., Joseph, D.D., Wang, J., Barree, R.D., Conway, M., Asadi, M., 2002. Power law correlations for sediment transport in
pressure driven channel flows. International Journal of Multiphase Flow. 28. 1269–1292.
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© 2011 ANSYS, Inc.
June 21, 2012
Proppant Transport: Granular Model
• Drag Force Modified for Dense system
– Single particle drag + Concentration effect + Hindered settling
effect
• Collisional and frictional effects (becomes important
near packing limit) are considered
Full 3D – Wall Effects and Leak Off – Modeled
Slurry flow: Mixture of Frac-Fluid and Proppant
Fracture Width = 0.5 cm
40 ft
300 ft
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June 21, 2012
Proppant Transport: Granular Model
300 µm
500 µm
time
More settling was observed in 500 micron
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June 21, 2012
Proppant Transport: Wash Out Process
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300 µm– Proppant
100 µm - Proppant
The mound created a re-circulating zone
upstream and allowed settling in this zone
The mound grew over a period of time
The mound started loosing proppant and
the height decreased
© 2011 ANSYS, Inc.
June 21, 2012
Proppant Transport: DEM Analysis
• The proppant transport process using DDPM-DEM
• Lagrangian tracking process
• Collision and frictional terms are modeled discretely
• Problem description
• Domain with dimensions: 3 X 0.3 X 0.01 m
• Proppants of 0.5mm size particles, 1 kg/s
– 1 Parcel = 10 particles
– 1.8 million parcels at pseudo-steady state
• Water at 4.5 kg/s
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© 2011 ANSYS, Inc.
June 21, 2012
Proppant Transport – DDPM DEM
Volume fraction of proppant
Velocity of proppant
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© 2011 ANSYS, Inc.
June 21, 2012
Summary: Sand Management
• Sand Management is critical in oil production to
ensure system integrity and efficiency
• It is important to predict various phenomena
involved in sand transport and sedimentation
• ANSYS CFD provides a platform for comprehensive
particulate modeling
• Few examples of sand filtration, sand transport and
proppant placement were demonstrated
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© 2011 ANSYS, Inc.
June 21, 2012
Erosion Modeling
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© 2011 ANSYS, Inc.
June 21, 2012
Sand Erosion
• Sand Erosion of pipelines and equipment is a major problem
• Solids entrained in the fluid impinge the walls of piping and
equipment causing in removal of wall material, reducing the
service life.
• Erosion limits the expected life time of piping details, and is
vital in risk management studies
• It is critical to predict the erosion damages in a flow system
accurately
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© 2011 ANSYS, Inc.
June 21, 2012
Challenges in Erosion Modeling
• Erosion is Complex Phenomena, depends on
– Particle properties and particle tracks
– Local Flow and turbulence field
– Surface conditioning
– Multiphase effects
• Erosion shield due to solid accumulation
• Damping effect due to liquid film
– Effect of local cavities due to material removal
• Nearly imposition to have a universal erosion model
– Different models for different flow regimes
– Always need experimental data to tune model parameters
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© 2011 ANSYS, Inc.
June 21, 2012
Erosion Modeling – Traditional approach
• Physical testing of new prototype designs
– Time consuming
– Degree of trial and error
• Semi-empirical models and correlations of erosive wear
– Limited to predicting peak values of wear
– Usually exist only for simple standard geometries
– API RP 14E
• Ad-hoc methods that are independent of the sand production rate
• “erosional velocity”
– Based on an empirical constant
(C-factor) and the fluid mixture density
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© 2011 ANSYS, Inc.
June 21, 2012
Erosion Modeling – CFD approach
• CFD modeling provides the user with detailed
information on the exact location and magnitude of
the erosive wear.
• Single phase Computational Fluid Dynamics
simulations
– Applicable for dilute particle phase
– Based on Eulerian-Lagrangian methodology
• Single phase simulation + DPM
– Lots of literature and many erosion models
– Provides detailed information on the exact location and
magnitude of the erosive wear
– Potential to allow design to be optimized prior to testing
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© 2011 ANSYS, Inc.
June 21, 2012
Erosion Modeling – CFD approach
• Multiphase CFD Simulations
– More realistic for full particle loading from low, medium to
high range
– Based on Eulerian-Granular multi-fluid approach
– Captures four-way couplings including fluid-particle,
particle-fluid, particle-particle, and turbulence interactions
– Capture particle shielding and liquid damping effects
– Lacks proper erosion models for abrasive erosion
CFD Modeling Complement Experimental testing for
Erosion Predictions
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© 2011 ANSYS, Inc.
June 21, 2012
ANSYS Solution for Erosion Modeling
• Different particulate modeling options
• DPM, DDPM, DEM, Eulerian-Granular
• Wide Varieties of Erosion Models are available in
ANSYS FLUENT
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FLUENT’s Default Erosion Model
Mclaury et. Al Erosion Model
Salama & Venkatesh Erosion Model
Tulsa Erosion Model
DNV Erosion Model
Erosion Model based on Wall Shear Stress
Flexibility to incorporate any erosion model
Contours of Erosion Rate
• Erosion pattern in complex flows and geometries can
be predicted with a good accuracy
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© 2011 ANSYS, Inc.
June 21, 2012
ANSYS Solution for Erosion Modeling
• Typical variables affecting Erosion rate
• Angle of impingement
• Impact velocity
• Particle diameter
• Particle mass
• Collision frequency between particles and solid walls
• Material properties for particle and solid surface
• Coefficients of restitution for particle-wall collision
Incoming particulate
Erosion caused by particle impact
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June 21, 2012
m : Mass flow rate of the particles
f(a) : Impingement angle function
V : Particle impact velocity
b : Velocity exponent
C(Dp): Particle diameter function
Erosion Model for Dense System
Dense DPM accounts for particle-particle interaction and solid
volume effect on fluid phase
ABRASIVE EROSION: Erosive due to relative motion of solid
particles moving nearly parallel to a solid surface
Erosion Model Based on Wall Shear Stress
ERw  A V  SS
n
Overall Erosion Rate
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June 21, 2012
A = Constant (diameter function)
n = Velocity Exponent
SS = Wall Shear Stress
ER  ERsp  ERw
Coupling Erosion with MDM
• Removal of solid surface material due to Erosion
creates localized cavities which affect the flow field,
particle tracking and hence the erosion.
• Such dynamically changing eroded curvature effect
needs to be incorporated for more accurate erosion
calculation
• ANSYS FLUENT has developed Erosion-MDM
connectivity using a User-defined Function (UDF) to
dynamically deform the solid wall surface based on
local erosion rate
• Similar workflow has been developed for ANSYS CFX
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© 2011 ANSYS, Inc.
June 21, 2012
Erosion Modeling – Coupled Simulation
•
The erosion rate is averaged and smoothed according to the
equation:
n
L
Ei  
Ei

ri 
i , ri  R

ERnode 
ri
n
L
R
 

i , ri  R  ri 
ERnode
L 
 fL
Wall _ density
A value of 0 for “n” will result in equal
weighting for all nodes within “R”. A
very large value of “n” will render the
smoothing algorithm negligible.
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June 21, 2012
Ei = Erosion rate for ith face
ri = Distance of ith face center from the node
L = Minimum cell length connected to the node
R = Radius of region considered for averaging
(user input is R/L)
n = Rate of decay (user input)
f = Maximum mesh move limit (user input)
Erosion Module
• Easy to use template to perform an Erosion
Simulation through a single GUI Panel
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User inputs drive the UDF and journal file in the background
Varieties of Erosion Models to choose
Built-in Smart defaults for DPM settings
Customized post processing for erosion rate
• Complete Automation of Erosion-MDM coupled
simulation
• Including postprocessing and animation
• Ability to allow multiphase erosion simulations
• Choose secondary phase for particle tracking
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© 2011 ANSYS, Inc.
June 21, 2012
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Option to run erosion-only or
erosion-MDM coupled simulation
Various erosion
models to choose from
Option to start a new ErosionMDM simulation or restart from the
existing data file at previous time
interval
Opens Fluent’s panel to read the
case file for the flow field
Opens Fluent’s DPM injection
panel to define particle injections
Option to choose
secondary phase flow
velocities for DPM
particle tracking
Opens Fluent’s boundary
condition panel to set DPM BCs
for wall zones
Opens Fluent’s DPM panel to set
parameters for particle tracking
Opens panel to define
required parameters for
Erosion-MDM coupling
Opens Fluent’s panel to read the
data file for the flow field
Opens Fluent’s panel to start
iterating for erosion-only analysis
Opens panel to start
erosion-MDM
simulation
Display erosion rate on all wall
zones
Erosion Module
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© 2011 ANSYS, Inc.
June 21, 2012
Display cumulative eroded
distance at wall zones
Example – Control and Delay Erosion
Problem
• Particle impact at the small area with
high velocity causing excessive erosion
Area of high erosion
Solution
• Modify exit flow from chock without
causing additional pressure drop.
• ANSYS multiphase flow solutions to
understand and change particulate
flow patterns
Result
Flow Inlet
• Modified chock geometry leads to flow
streamlines parallel to exit pipe.
• Increase particle impact area while
reducing particle impact velocity
• Reduce chock maintenance and
replacement cost
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© 2011 ANSYS, Inc.
June 21, 2012
Particle trajectories
colored by velocity and
associated erosion area
for two chokes
Courtesy of DNV
Example: Single Phase vs Multiphase Erosion
Double Elbow Geometry
Relatively Low solid loading (~8% volume loading)
DPM vs DDPM Simulation and same Erosion Settings
Particle shielding effect captured in multiphase simulation
Single phase predicts conservative erosion
Single Phase Erosion
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June 21, 2012
Multiphase Erosion
Sand Volume Fraction
Example: Erosion in Gas-Liquid-Solid System
Low Erosion due
to liquid cushion
and particle
shielding
Liquid Volume Fraction
Contours
Solid Volume Fraction
Contours
Vapor Velocity Contours
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© 2011 ANSYS, Inc.
June 21, 2012
Contours of Erosion Rate
Erosion in a Pipe Assembly
Courtesy of Suncor
Tool Erosion in Gravel Pack:
(OTC 17452 – Halliburton)
CFD Simulation to analyze flow field and erosion pattern
in frac pack tools
Calibration of erosion model based on lab tests and
Erosion pattern compared with large scale tests.
Fluid Velocity
Proppant VOF
Turbulent Slurry flow with
high proppant concentrations
Non-newtonian fluids
Calibration of Impact angle
function
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June 21, 2012
Erosion pattern on the inside
surface of upper extension sleeve
Contour of Total Eroded Distance
Erosion - MDM
Contour of Erosion Rate
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June 21, 2012
Erosion - MDM
Eroded Material is Removed ->
Better Material Thickness Prediction
FLOW
Larger ID
After 42 hr
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June 21, 2012
Plots of erosion contours in a 4 inch test case
Summary: Erosion Modeling
• It is important to predict erosion rate accurately
• Erosion is a complex phenomena
• Semi-empirical models and correlations are not enough
• Need for CFD in erosion modeling
• CFD can provide valuable information for erosion
predictions
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•
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•
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Multiphase flow modeling for dense slurry
Erosion-MDM coupling
ANSYS CFD equipped with all required modeling needs
ANSYS CFD - Proven approach for many erosion studies for
oil & gas industries
© 2011 ANSYS, Inc.
June 21, 2012
THANK YOU
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© 2011 ANSYS, Inc.
June 21, 2012