SPE-169317-MS

Applications of Computational Fluid Dynamics To Study: Slurry Flow inPipeline for Heavy and Extraheavy oil

H.J. Zambrano Meza, A. Brito, and J. G. Márquez, PDVSA Intevep

Copyright 2014, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Latin American and Caribbean Petroleum Engineering Conference held in Maracaibo, Venezuela, 21–23 May2014.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contentsof the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflectany position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the writtenconsent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations maynot be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

The Faja Petrolifera del Orinoco (FPO), located in the southern part of the Eastern Basin of Venezuela,has the largest reserves of heavy and extra heavy oil in the world. These oils are highly viscous and theirAPI gravities values are between 7 and 15, these properties together with the existence of multiphase flowmake the production and transportation of these oil is highly complex. Therefore, it is necessary to useheavy oil transportation methods focused on reducing the viscosity of oil, this is achieved be removingor modifying the oil compounds that have been pointed out as the main cause of the high viscosity of theseoils, such as: partial or total upgrading or slurry transportation.

This study presents a flow modeling of a solid-liquid dispersion through horizontal pipes, whichrepresents a slurry transportation, using a software of computational fluid dynamics (CFD) calledFLUENT 6.3. For the simulation methodology was selected Euler-mixture in a three dimensional pipemodel, with different concentrations of solids. The simulations were validated with experimental datadevelopment by PDVSA Intevep that contains pressure drops, temperature, solid characterizations andreological behavior of the slurry.

The performance evaluation by the CFD model showed an acceptable fit when it is compared againstexperimental data, this results allow to understand the hydraulic behavior of heavy oil slurry transportthrough pipeline.

Keywords: Computational Fluid Dynamics · CFD · Slurry · Fluid Flow · Multiphase Flow

INTRODUCTIONSuspended solid transportantion through liquid streams (slurry) is a typical process in dfferent industry,such as: pharmaceutical, food, oil and other industries, however this type of flow is very complex, forwhich reason fluid dynamics have been studied for many researchers in recent years; the prediction ofvelocity profiles, concentration profiles and pressure drops have a great impact from the economic pointof view since it allows to optimize energy consumption and select the most suitable pump for transportingslurry.

Nowadays, computational fluid dynamic (CFD) has become a tool of major importance to theengineering of the fluid, as it allows using a computational algorithm to solve the complex mathematicalequations for fluid flow in addition to study processes mass transfer and chemical reactions.

Recent studies (Kumar and Ghanta, 2010) and CFD simulations have been performed using slurry flowin pipeline as well as the study by Kausal et al to study the slurry with high solids concentration by CFD.

This study presents a flow modeling of a solid-liquid dispersion through horizontal pipes, whichrepresents a slurry transportation. To obtain modeling solution a commercial CFD code FLUENT 6.3 wasused, Simulations were agreement with experimental data generated by PDVSA Intevep.

TEST SETUPTo evaluate the slurry behavior a new facility was built at PDVSA INTEVEP. Experiments were carriedout in a horizontal test loop of 0.025 m internal diameter (ID) and roughness of the pipe of 4.6�10–5.Figure 1 shows the experimental facility diagram, the total length of the loop is 6 m of carbon steelpipeline and is conformed by two sections: the first one is 3.5 m length section for flow development; thesecond is the test section of 2.5 m length, equipped with transmitter for: pressure, temperature anddiferential pressure; moreover, the total length of the horizontal pipe circuit was 236 times the nominaldiameter, in accordance with the information presented by Falcone et al (2008) in that most of themultiphase flow circuits worldwide have length/diameter ratios between 40 and 300; the ratio of 236 usedin the circuit built for the present work, ensure that allowed the measurement section of a pattern of flowis fully developed had.

Diferential pressure traducers are used to measure the pressure drop between pressure laps. Downstramto the test section, the dispersion returns to the tank in which the slurry is continuosly agitated. The slurryis pumped using a gear pump with a variable speed control regulate the flow rate, the maximum dispersionflow rate is 3.6 m3/h and it is measured by deviating the flow to a small tank with a level transmitter sensor(Brito, 2011)

FLUID SYSTEMThe slurry is prepared with de-asphalted oil (also known as maltenes) as the liquid phase and the asphalticresidue correspond to the solid phase with particle size up to 500 �m, both phase obtained from ade-asphalting process at relatively low pressure and temperature. Properties and SARA composition ofboth the solid and liquid hydrocarbons that were mixed to prepare the slurries are presented in Table 1.The solid phase is compose mainly by asphaltenes and resins, and is prepared and sieved with a specialprocedure to keep particle size up to 500 �m, this solid are incorporated in the liquid under an appropriatemechanical stirring for 20 min.

Figure 1—Experimental loop diagram

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TEST MATRIXFor each experimental point the dispersion properties are analyzed for each solid concentration andmixture velocities between 0.2–2.33 m/s. the test condition are presented in Table 3.

COMPUTATIONAL FLUID DYNAMICS (CFD)Simulation models using Computational Fluid Dynamics (CFD) provide an economical means ofunderstanding the complex dynamics of flow within teams and how they are affected by changes in theoriginal design or operating conditions (Garcia et al, 2009) these calculations can be used in a wide rangeof flows reducing the need for experimental tests, allowing for predictions in the design process andevaluation of industrial processes, reducing factors such as cost, risk and time thus achieve informaldecision-making that lead to the design of systems with better performance.

The solution methodology for models of CFD is subdivide in domain into a large number of controlvolumes and convert partial differential equations by integration over these control volumes in theiralgebraic equivalents (Zambrano, 2011). The result is a set of simultaneous algebraic equations that canbe solved using iterative methods to obtain the field distributions of dependent variables on the boundaryconditions that define the specific, such as velocity and pressure problem.

The role of CFD in engineering predictions has become so strong, that today a rewiew of the literatureshows little progress in simulations slurry flow in pipelines using CFD (Kumar, 2010), however the studyof complex fluid using CFD is very important for further research and development.

Table 1—Properties and S.A.R.A of liquid and solid phases

Fluid Saturates (%p/p) Aromatics (% p/p) Resins (% p/p) Asphaltenes (% p/p)

De-asphalted oil 15 56 29 0

Asphaltic Residue 14 15.4 31.6 39

Table 2—Properties Fluid

Fluid Density @ 15 °C (Kg/m3) Viscosity @ 135 °C (Cst)

De-asphalted oil 907 20.57

Asphaltic Residue 1120 —

Table 3—Test matrix for the slurry transportation test

Number of experiments Average mixture velocities (m/s) Concentration of solids by weight (%)

36 0.2–2.33 0, 1, 3, 6, 8 y 12

Figure 2—Three-dimensional meshing of slurry pipeline

Table 4—Number of cells and Pressure drop

Number of Cells �P (Pa)

16.128 2697

25.160 2719

73.836 2775

341.606 2810

1,519.242 2813

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MATHEMATICAL MODELFor simulation is very important the use an specific multiphase flow model, the Euler-Mixture model wasadopted here, with laminar flow, this model solves the momentum, continuity and energy conservationequations for mixture, the volume fraction equation for the secondary phase and algebraic expression forthe relative velocities. The mixture model allows the phase to move at different velocities, using theconcept of slip velocities or be assumed to move at same velocity and the mixture model is then reducedto homogeneous multiphase flow model (Kaushal and Tomita, 2012).

Continuity equation

(1)

Where is the mass average velocity is given by

(2)

And �m is the mixture density is given by:

(3)

Where �k is the volume fraction of pfase k, subscripts k represents f (fluid) or s (solid), and mrepresents mixture.

Momentum equation

(4)

Where n is the number phases, is a body force and �m is the viscosity of the mixture given by:(5)

Drift velocity for secondary phase k, takes the following form in Mixture model:

(6)

Figure 3—Pressure drop for mesh independency study

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The relative velocity (also referred to as the slip velocity) is defined as the velocity of a secondaryphase (p) relative to the velocity of primary phase (q):

(7)

The mass fraction for any phase (k) is defined as

(8)

The drift velocity and relative velocity ( ) are connected by following expression:

(9)

Table 5—Comparison of experimental and predicted pressure drop by simulation CFD and Darcy equations for different velocities at 6% ofsolid concentration with mixture density of 915.705 Kg/m3 and mixture viscosity 0.0687 Kg/ms

Solid concentration Average Velocity (m/s) Reynolds number (Re) �P(Experimental) (Pa) �P(Darcy) (Pa) �P (CFD) (Pa)

0.20 68 1688 2092 1713

0.33 112 2795 3462 2916

6% 0.63 213 5345 6598 5695

1.10 372 9266 11179 10046

1.42 481 11529 13172 12343

1.93 653 15813 17071 16522

Table 6—Pressure drop absolute error for 6 % of solid concentration

Solid concentration Average Velocity (m/s) Reynolds number (Re) �P(Darcy) (%) �P (CFD) (%)

0.20 68 24 1

0.33 112 24 4

6% 0.63 213 23 7

1.10 372 21 8

1.42 481 14 7

1.93 653 8 4

Figure 4—Comparison of experimental and predicted pressure drop by Euler-mixture model and Darcy equations at 6% of solid concentration

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The theoretical pressure drop is determined with the Darcy equation, it was used because in experi-mental study the flow pattern was homogeneous flow, for this the mixture is considered as a homogeneoussingle phase (Brito et al, 2013).

(10)

Where �P is the pressure drop, L is length of pipeline, �m is density of mixture, D is diameter, Vm isvelocity of mixture, and fm is the friction is calculed by:

Table 7—Comparison of experimental and predicted pressure drop by simulation CFD and Darcy equations for different velocities at 8% ofsolid concentration with mixture density of 918.185 Kg/m3and mixture viscosity 0.0873 Kg/ms

Solid concentration Average Velocity (m/s) Reynolds number (Re) �P(Experimental) (Pa) �P(Darcy) (Pa) �P (CFD) (Pa)

0.20 53 2380 2783 2173

0.33 88 3894 4493 3698

8% 0.63 168 7310 8435 7113

1.10 294 12352 14319 12674

1.42 379 15095 17559 16490

1.93 516 20150 21971 21824

Table 8—Pressure drop absolute error for 8 % of solid concentration

Solid concentration Average Velocity (m/s) Reynolds number (Re) �P(Darcy) (%) �P (CFD) (%)

0.20 53 17 9

0.33 88 15 5

8% 0.63 168 15 3

1.10 294 16 3

1.42 379 16 9

1.93 516 9 8

Figure 5—Comparison of experimental and predicted pressure drop by Euler-mixture model and Darcy equations at 8% of solid concentration

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(11)

Where fm is the friction factor for solid-liquid mixture, fw is the friction factor of water, K, �, �, �, �are constants

IMPLEMENTATION OF CFD MODELFor this study the geometry consist in a pipe with a diameter of 0.025 m and length of 2.5 m, the geometrywas meshed in to approximately 341.606 cells in GAMBIT 2.4.6 pre-processor; for Euler-Mixturemultiphase calculations used phase Coupled SIMPLE algorithm, the velocities are solved coupled byphases. For the initial condition and uniform fully developed velocity profile was used at the pipe inlet,

Table 9—Comparison of experimental and predicted pressure drop by simulation CFD and Darcy equations for different velocities at 12% ofsolid concentration with mixture density of 923.95 Kg/m3 and mixture viscosity 0.113 Kg/ms

Solid concentration Average Velocity (m/s) Reynolds number (Re) �P(Experimental) (Pa) �P(Darcy) (Pa) �P (CFD) (Pa)

0.20 42 3223 3722 2810

0.33 69 5326 6043 4735

12% 0.63 131 9880 11050 8995

1.10 228 16678 17799 15730

1.42 295 19889 21233 19736

1.93 401 25953 29000 25179

Table 10—Pressure drop absolute error for 8% of solid concentration

Solid concentration Velocity (m/s) Reynolds number (Re) �P(Darcy) (%) �P (CFD) (%)

0.20 42 15 13

0.33 69 13 11

12% 0.63 131 12 9

1.10 228 7 6

1.42 295 7 1

1.93 401 12 3

Figure 6—Comparison of experimental and predicted pressure drop by Euler-mixture model and Darcy equations at 12% of solid concentration

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an second order upwind discretization scheme for the volume the fraction. For the simulation first wasnecessary made initial validation for the mesh size and study the independence of the grid. In the regionnear the wall this region has fine mesh along the solid wall surface.

RESULT AND DISCUSSION OF 3D SIMULATIONThis study was made with CFD simulation for pressure drop and validated with PDVSA Intevepexperimental data for different velocities at 6, 8 and 12% of solid concentration and compared with theDarcy equation obtained the following results:

MESH INDEPENDENCE STUDYTo obtain reliable results it is necessary to be sure that the solution is independent of the resolution of themesh, for they make the calculation of the pressure drop to 12% solids concentration at a velocity of 0.20m/s for different meshes, obtaining the following results:

The difference between the pressure drop in the case of the fine mesh of about 341606 cells and 1,519.242 ultrathin cells is less than 10%, so the mesh with 341606 elements is adequate for the presentstudy.

PRESSURE DROPFigure 4–6 show corresponding pressure drop for slurry of 500 �m particles and solid concentration

around 6, 8 and 12%. From this figures it is evident the good agreement between the calculated by CFD

Figure 7—Mixture velocity (m/s) at 0.05 m of inlet at 8% of solid concentration (Re � 53)

Figure 8—Mixture velocity (m/s) to different distance of inlet at 8% of solid concentration (Re � 53)

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and experimental data, it is observed that pressure drop increase when slurry velocity increase for anyconcentration, when increase the slurry velocity the error is lower for pressure drop obtain by CFD, thepresent study coincide with findings of Kumar (Kumar, 2010) and Kaushal (Kaushal et al, 2012).

VELOCITY PROFILEThe Figure 7 show slurry velocity in the pipeline. The increases the solid concentration in the slurryenhanced interference effect between the solid particles in consecuence reduce the asymmetry in theprofile of velocity. The Figure 8 show slurry velocity profile into pipe for different distances from the pipeinlet.

CONCLUSIONThe capability of Computational Fluid Dynamics (CFD) was explored to model complex solid liquidslurry flow through horizontal pipeline. This study was made using the Fluent Software. The performanceevaluation by the CFD model showed an acceptable fit when it is compared against experimental data;these results allow to predic the pressure gradient drop of heavy oil slurry systems transported throughpipeline.

The Euler Mixture model to predict pressure drop correctly, it was observed that pressure drop increaseas slurry velocity increase.

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