2011 ANSYS, Inc. June 21, 2012 1

Erosion Modeling and Sand Management with ANSYS CFD

Madhusuden Agrawal

ANSYS Houston

2011 ANSYS, Inc. June 21, 2012 2

Particulate modeling in ANSYS CFD

Sand Control and Sand Management

Sand Filtration Sand Transport in pipelines Proppant Placement

Erosion Modeling

Challenges in Erosion Modeling Key components of erosion modeling ANSYS solution for erosion modeling Erosion Module Examples

OUTLINE

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Recap: Particulate Modeling

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Spans wide range of

Length scales Time scales

Physics

Particulate physics Fluid particle interaction Particle size distribution Homogenous and

heterogeneous reaction

Particle structure interaction

Challenges in Particulate Modeling

From: Fundamental of Multiphase Flow, C. E. Brennen

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Particulate Flows Regimes Diluted vs. Dense Flow

es

t12/tcol

10-3 10-1 10-5 10-7

104

102

100

10-2

dilute dense

101 102 100 (x1-x2)/dp

4-way coupling

2-way coupling

1-way coupling

Particles reduce

turbulence

Particles enhance

turbulence negligible effect on

turbulence

102

100

10-2

t12/teddy

Dilute Dense Relative motion between particles Large Small

Particle-particle interaction Weak Strong

Apparent viscosity of the solid phase

Particle-fluid

interactions

Particle-particle

interaction

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Modeling Particulate Flow

Particle Phase

Particle size

P-P Interaction

Fluid-P Interaction

Eulerian

Lagrangian

Sub grid scale

Super grid scale

Hybrid

Resolved

Modeled

Resolved

Modeled

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Platform for Simulating Particulate Systems

ANSYS CFD provides a platform which can adapt to the multi-physics, multi-components and multi-scale configurations of particulate flows and their industrial applications

Eulerian Granular

MPM DDPM-DEM DPM

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Models for Particulate Flows 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 P-P 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 P-P interactions.

Easy to include PSD; if particles become smaller than the mesh, uses an empiricial model

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Sand Control

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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

Sand Management is important in oil production to ensure system integrity and efficiency

Excessive sand leads to Partial or complete blockage of flowlines Enhanced pipe bottom corrosion and erosion Trapping of pigs Reduced production time and increased maintenance and operating costs

Sedimentation in Oil & Gas

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.

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Sand control strategies

Preventing formation failure Sand exclusion techniques Sand management

Sand Control

Key areas to understand fundamental nature of sand in the reservoir and the wellbore

Hydraulic fracturing (Proppant transport)

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Sand Exclusion Techniques

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Sand control screen systems

Screens Gravel and frac packing

Example: Sand Filtering Systems in O&G

Bulk process Surface process

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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

Modeling Filtration with ANSYS

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Euler Granular Model for Filtration

t = 16 sec.

t = 100 sec.

t = 135 sec.

t = 60 sec.

Solid Phase Volume Faction Contours Velocity Vectors of Solid Phase

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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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Filtration Modeling Using DDPM/DEM

Filter: Allows particles below a threshold to pass through, Filter represented by a internal boundary condition.

Inlet Outlet

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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.

Filtration Modeling using MPM

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Particulate Migration in Gravel Pack

Micro scale Simulation for fine particles transport through pores in gravel pack

Study Permeability alterations in the gravel pack due to fine particles entrainments, transport and deposition

Filtration of fine particles

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Sand Transport

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Example: Sand Transport in Pipelines

Sand-Water slurry flow in horizontal pipe Pipe diameter D = 0.0505m Pipe length L = 4m

30% volume loading Four Different Slurry Flow Rates DDPM with DEM Collision

Particle staggering for surface injection

Low value of Spring Constant as buoyancy force is important.

Almost 3 millions parcels

Gravity

Slurry Velocity (m/s)

dp/dx (Pa/m)

SRC: Saskatchewan Research Council

Expected Results

To be published in collaboration with Shell

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Mean Static Pressure is plotted on the line coinciding with the axis of the pipe.

dp/dx is calculated between z=3m to z=4m as it varies linearly in this range for all the cases.

Results: Pressure Gradient

Slurry Velocity (m/s)

dp/dx (Pa/m)

dp/dx pipe length dp/dx slurry velocity

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Reduced particle time step size to more accurately model collisions.

Little difference in predicted pressure gradient.

Considerable increase in simulation time.

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

Slurry Velocity (m/s)

dp/dx (Pa/m)

dp/dx slurry velocity

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It is important to keep particles suspended 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

Many correlations exists for solids transportation in multiphase flow

Based on experiments for single phase flow on small pipes

Lot of variability in measurements

Sand Transport in Pipelines

Hjulstrom Diagram

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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

Sand Transport in Pipelines

All these paths for sand transport can be addressed by Particulate modeling in ANSYS CFD.