PC-SAFT Crude Oil Characterization for
Modeling of Phase Behavior and
Compositional Grading of Asphaltene
Sai R Panuganti, Anju S Kurup,
Francisco M Vargas, Walter G Chapman
1
Outline
• Asphaltene introduction
• Background of asphaltene thermodynamic
analysis
• Comparison of Cubic and PC-SAFT EoS
• Robustness of PC-SAFT characterization
methodology
• Asphaltene compositional grading
• Future Work
• Conclusion
2
Introduction
Asphaltene
1. Polarizable
2. Polydisperse
3. Heavy fraction in crude oil
•
Operational Definition
1. Soluble in aromatic
solvents
2. Insoluble in light paraffinic
solvents
Modified Yen Model
3
Mullins OC. Energy & Fuels 2010; 24(4):2179-2207
Modeling Asphaltene Stability
Colloidal Model (~1930)
Solubility Model (~1980)
Stability based on polar-polar
interactions.
Asphaltene solubilized by the oil.
• Micelle formation
• Asphaltene particles kept in
solution by resins adsorbed on them
London dispersion dominate phase
behavior.
Approaches: (Less parameters)
•Flory-Huggins-regular solution theory
•EoS
• Limitations of Colloidal Model:
• Negative Hydropilic-Lipophilic Balance for asphaltene [Czarnecki J-2009]
• Impedence Analysis – Resins are unlikely to coat asphaltene [Goual-2009]
• Diffusion coefficient of asphaltene is same in the presence and absence of
resin
4
Nellensteyn FJ. Journal of the Institute of Petroleum Technologist 1928; 14:134-138
Solubility Model Approaches
•
Flory-Huggins type models
Limitation:
1. Effective molar volume significantly lower than actual molar
volume.
2. Cannot account for compressibility
•
Equations of State
1. Cubic-EoS
2. SAFT based models
Hirshberg A. Journal of Petroleum Technology 1988; 40(1):89-94
5
Modeling using Cubic EoS (Crude A)
14000
5% Gas Injection
12000
Pressure (Psia)
Exp AOP
10000
Exp Bu p
AOP (SRK(P))
8000
Bu P (SRK(P))
6000
4000
Crude A
2000
0
0
50
100
150
200
250
300
350
Temperature (F)
• Characterized the crude oil system using PVT-Sim of Calsep
• The Cubic EoS employed was SRK-P
6
Modeling using Cubic EoS (Crude A)
14000
5% Gas Injection
12000
Pressure (Psia)
14000
30% Gas Injection
12000
10000
10000
8000
8000
6000
6000
4000
4000
2000
2000
0
0
0
50
100
150
200
Temperature (F)
250
300
350
0
50
100
150
200
250
300
Temperature (F)
The optimized Cubic EoS parameters from 5% were used to predict
the phase behavior for 30% injected gas
Limitations of cubic equation of state:
• Asphaltene critical properties are not well known
• Results are very sensitive to parameters
7
Larry GC et al. Advances in Thermodynamics (Volume 1): C7+ Fraction Characterization. Taylor & Francis; 1989
Introduction to SAFT
res
seg
association
chain
A
A
A
A



RT RT
RT
RT
Parameters represent the physical system directly
500
7.0
n-alkanes
6.0
n-alkanes
400
alkylbenzenes
g = 0.0
alkylbenzenes
g = 0.0
5.0
m*σ3, A3
alkylnaphthalenes
m 4.0
3.0
PNA
g= 1.0
2.0
300
PNA
g= 1.0
200
100
1.0
0
0
100
200
300
400
0
100
MW
200
300
400
MW
• PC-SAFT EOS is be used
• Parameters for most compounds are known
Chapman WG et al. Industrial Engineering and Chemistry Research 1990; 29(8):1709-1721.
Gonzalez DL et al. Energy & Fuels 2005; 19(4):1230-1234.
8
PC-SAFT Characterization
Developed a standardized characterization procedure based on:
16000
SARA analysis
Molecular Weights
Liquid Density
Bubble Pressure
AOP
Crude B
12000
Pressure (Psia)
Flashed Liquid and Gas
compositions (C9+)
8000
Stable
4000
Unstable
VLE
0
50
100
150
200
250
300
350
Temperature (F)
Methodology:
• Composition data up to C9+ is sufficient.
• Few parameters were needed
• Temperature independent binary interaction parameters for all
compounds are very small
Panuganti SR et al. “PC-SAFT Characterization of Crude Oils and Modeling of Asphaltene Phase Behavior” Fuel - Submitted
9
Comparison of PC-SAFT and Cubic EoS
5% injected gas
Pressure (Psia)
12000
(A)
SRK-P
9000
PC-SAFT
(Crude A)
6000
3000
0
0
100 Temperature
200 (F) 300
400
Characterized using PC-SAFT and SRK-P EoS
• Will PC-SAFT work better than Cubic EOS?
• Will a specific set of PC-SAFT parameters be sufficient to capture
the phase behavior of the system at a different condition?
10
Comparison of PC-SAFT and Cubic EoS
5% injected gas
Pressure (Psia)
12000
(A)
No gas injection
(B)
SRK-P
9000
PC-SAFT
6000
3000
0
0
100 Temperature
200 (F) 300
400
0
100 Temperature
200 (F) 300
400
Crude B
Better performance of PC-SAFT is visible
• Will PC-SAFT with proposed characterization procedure be able to
predict phase behavior for higher amounts of gas injected?
11
Comparison of PC-SAFT and Cubic EoS
5% injected gas
Pressure (Psia)
12000
(A)
No gas injection
(B)
SRK-P
9000
PC-SAFT
6000
3000
0
0
100
200
300
15% injected gas
400
100 Temperature
200 (F) 300
400
Crude B
(C)
12000
Pressure (Psia)
0
9000
PC-SAFT holds upper hand over C EoS
6000
What about for even higher gas
injection?
3000
0
0
100
200
300
400
Temperature (F)
12
PC-SAFT vs. Optimized Cubic EOS
5% injected gas
Pressure (Psia)
12000
(A)
No gas injection
(B)
SRK-P
9000
PC-SAFT
6000
3000
0
0
100
200
300
15% injected gas
400
0
100
200
300
400
30% injected gas (D)
(C)
Crude B
Pressure (Psia)
12000
9000
6000
3000
0
0
100
200
300
Temperature (F)
400
0
100
200
300
400
Temperture (F)
13
Prediction of Effect of Gas Injection
15000
15% injected gas
(A)
Pressure (Psia)
12000
Crude C
9000
6000
3000
0
0
100
200
300
400
• A different crude, exhibiting different physical properties.
• Characterized using standardized methodology
14
Robust Methodology
15000
15% injected gas
(A)
No gas injection
(B)
Pressure (Psia)
12000
9000
6000
3000
0
0
100
200
300
10% injected gas
400
0
100
200
300
400
30% injected gas (D)
(C)
Pressure (Psia)
12000
Crude C
1. Robust Methodology
2. Good parameter
estimation
9000
6000
3000
0
0
100
200
300
Temperature (F)
400
0
100
200
300
400
Temperature (F)
Any property of the precipitate phase can be calculated
15
Compositional Grading Introduction
Used for:
1. To predict oil properties with
depth
2. Find out gas-oil contact
compositional
How is asphaltene
grading useful?
Reservoir connectivity
A M Schulte. SPE Conference; September 21-25, 1980
Høier L, Whitson CH. SPE 74714; 2001; 4(6)525-535
16
Compositional Grading Algorithm
Whitson C H & Belery P; SPE 28000 1994 443-459
Reservoir Compartmentalization
Optical Density (@ 1000nm)
0
0.5
1
1.5
2
2.5
3
24000
PC-SAFT (M21B)
24500
M21B
M21A
25000
Depth (ft)
PC-SAFT (M21A)
25500
M21A North
PC-SAFT (M21A North)
26000
M21A South
26500
27000
27500
• All zones belong to the same reservoir as the gradient slopes
are nearly the same.
• The curves do not overlap meaning each of them belong to
different zone.
18
Approximate Analytical Solution
 i (h1 ) ( M i  Vi   ) g
ln

(h2  h1 )
 i (h2 )
RT
ρ= Molar density; h=Depth; Vi = Partial Molar Volume
Mi =Mol wt
Assumptions:
1. Changes in density of oil with depth can be neglected
2. At infinite dilution partial molar volume is independent of
composition
3. System is far away from critical point such that partial molar
volume is independent of pressure changes
Sage BH, Lacey WN. Los Angeles Meeting, AIME; October 1938
Morris Muskat. Physical Review 1930; 35(1):1384-1392
19
Approximate Analytical Solution
Optical Density (@ 1000nm)
0
24000
0.5
1
1.5
2
2.5
3
PC-SAFT (M21B)
M21B
24500
Model (M21B)
M21A
25000
PC-SAFT (M21A)
Depth (ft)
Model (M21A)
25500
M21A North
PC-SAFT (M21A North)
26000
Model (M21A North)
M21A South
26500
27000
27500
• We have Partial molar volume of asphaltene = 1934 cm3/mol. It
corresponds to a particle size of 1.83 nm
• Analytical solution can be used for sensitivity analysis and
approximate estimate.
20
Future Work
• Tar mat occurrence due to compositional grading
of asphaltene.
• QCM-D experiments for determination of
asphaltene deposition rates and aging effects.
• Micro fluidic studies to understand the asphaltene
deposition mechanism.
21
Conclusion
• Solubility model using PC-SAFT EoS
• PC-SAFT characterization methodology proposed
• Robustness of PC-SAFT characterization methodology
• Evaluate reservoir compartmentalization through
asphaltene compositional grading.
22
Acknowledgement
•
•
•
•
Walter G Chapman
Francisco Vargas
Anju S Kurup
Jeff Creek
23
24
Characterization of T Oil
18000
16000
14000
C1 Bu P
Pressure (Psia)
12000
C1 AOP
10000
C2 Bu P
C2 AOP
8000
C3 Bu P
6000
C3 AOP
Exp Bu P
4000
Exp AOP
2000
0
0
10
20
30
40
50
60
70
80
Amount of precipitating agent added (Mole %)
90
100
25
Derivation of Thermodynamic Model
i1  M i gh1  i 2  M i gh2
di  M i g (h 2  h1 )
ˆi1
 i1
Z1
RT ln 2  RT ln 2  RT ln 2  M i g (h 2  h1 )
Z

ˆ
i
i
i

1
2
RT ln 1  Vi ( P  P )  RT ln 2  M i g (h 2  h1 )

i
2

1

 i1
RT ln 2  [ M i g  Vi g ](h 2  h1 )
i
26
Algorithm
n
Q
 

  Zi fi
P
P 
i 1

 f i ' 


n
n
Z i f i f i '
1 

P







Y


i
2
fi ' 
P
fi '
i 1 ( f i ' )
i 1

n
 (ln f i ' )
Vi
  Yi
  Yi
P
RT
i 1
i 1
n
27