Understanding Reservoir Connectivity and
Tar Mat Using Gravity-Induced Asphaltene
Compositional Grading
Sai R. Panuganti – Rice University, Houston
Advisor: Prof. Walter G. Chapman – Rice University, Houston
Co-advisor: Prof. Francisco M. Vargas – The Petroleum
Institute, Abu Dhabi
1
Outline
• Introduction
• Motivation
• PC-SAFT asphaltene phase behavior modeling
• Predicting asphaltene compositional gradient
• Prediction of tar-mat occurrence depth
• Conclusion
• Future release
2
Fast Facts about Asphaltene
 Polydisperse mixture of the heaviest and most polarizable fraction of the oil
 Defined in terms of its solubility
 Miscible in aromatic solvents, but insoluble in light paraffin solvents
 Molecular structure is not completely understood
 Behavior depends strongly on P, T and {xi}
(a) n-C5 asphaltenes
(b) n-C7 asphaltenes
Jill Buckley, NMT
http://www.gasandoilresearch.com/asph.html
3
Compositional Grading Introduction
First theoretical explanation –
Morris Muskat, 1930
Used for:
Used for:
1. To predict oil properties
with depth
2. Find out gas-oil contact
Muskat M., Physical Review, 1930; 35:1384:1393
4
Schulte, A.M., SPE Conference, 1980; September 21-25, SPE 9235
Motivation
Reservoir Connectivity
Understanding reservoir
connectivity helps in
effective sweep of oil for a
given number of wells
Pressure communication
can be used only to
understand
compartmentalization
Tar Mat
“ The presence of a tar mat could not be inferred from the
PVT behavior of the reservoir oil in the upper part of the
reservoir “ – Hirschberg, A. JPT 1988; 40(1):89-94
5
Zao, J.Y., et al., Journal of Chemical & Engineering Data, 2011; 56(4):1047-1058
PC-SAFT Modeling of Asphaltene PVT Behavior
14000
STO + Precipitant
12000
Pressure (Psia)
10000
Precipitant – C1
8000
6000
Precipitant – C2
4000
Tahiti Field Black Oil,
Offshore,
Gulf of Mexico
Precipitant – C3
2000
0
0
20
40
60
80
100
Amount of asphaltene precipitating agent added (Mole %)
Live Oil
8000
exp Bu. P
Pressure (Psia)
exp. AOP
6000
S Field –
Light Oil,
Onshore,
Middle East
Asphaltene Onset Pressure
4000
2000
Bubble Pressure
0
50
100
150
200
Temperature (F)
250
300
350
6
Panuganti, S.R. et al., Fuel, 2012; 93:658-669
Isothermal Compositional Grading Algorithm
7
Whitson, C.H., Belery, P., SPE 28000; 1994, 443-459
Verifying the Compositional Grading Algorithm
800
Field Data
GOR (scf/stb)
600
400
200
0
24000
24500
25000
25500
26000
26500
27000
27500
Depth (ft)
Tahiti Field
8
Verifying the Compositional Grading Algorithm
Field Data
800
PC-SAFT Prediction
GOR (scf/stb)
600
400
200
0
24000
24500
25000
25500
26000
26500
27000
27500
Depth (ft)
Tahiti Field
PC-SAFT prediction matches the field data, verifying the
successful working of the compositional grading
algorithm
9
Asphaltene Grading
Optical Density (@1000 nm)
0
24000
0.5
1
1.5
2
2.5
Field Data (M21B)
24500
Depth (ft)
25000
Field Data (M21A Central)
25500
26000
Field Data (M21A North)
26500
27000
27500
Tahiti field, Offshore in Gulf of Mexico
Black oil, isothermal reservoir at equilibrium
Optical density measured using infra red wavelength during
down-hole fluid analysis
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Freed, D.E. et al., Energy and Fuels, 2011; 24:3942-3949
Predicting Asphaltene Compositional Grading
Optical Density (@1000 nm)
0
0.5
1
1.5
2
2.5
PC-SAFT (M21B)
24000
Field Data (M21B)
24500
PC-SAFT (M21A Central)
Depth (ft)
25000
25500
26000
26500
Field Data (M21A Central)
PC-SAFT (M21A North)
Field Data (M21A North)
27000
27500
• All continuous lines are PC-SAFT predictions
• All zones belong to the same reservoir as the gradient slopes
are nearly the same
• The curves do not overlap implying each zone belongs to
different compartment
11
PC-SAFT Asphaltene Compositional Grading
Asphaltene Weight % in STO
Tahiti
field
2
4
6
8
10
12
14
24000
26000
Depth (ft)
28000
Reference Depth
30000
32000
34000
36000
• PC-SAFT asphaltene compositional grading extended to further
depths
• Field observations did not report any tar mat
12
Predicting Asphaltene Compositional Grading
Dimensionless Optical Density (OD/ODo)
0.5
0.7
0.9
1.1
1.3
1.5
7500
Zone A1
S field
Well Z
Zone B1
Depth (ft)
7700
Field Data
Well X
7900
Well Y
8100
• All continuous lines are PC-SAFT predictions
• All zones belong to the same reservoir as the gradient slopes are
nearly the same
• The curves do not overlap implying each zone belongs to
different compartment
•Wells X and Y are connected because they lie on the same
asphaltene grading curve
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Tar-mat
Onshore
S field
Tar-mat formation mechanism of S field
• Asphaltene compositional grading
Other tar-mat formation mechanisms
•
•
•
•
•
•
Settling of precipitated asphaltene
Asphaltene can adsorption onto mineral surfaces
Oil-water contact
Biodegradation
Maturity between the oil leg and tar-mat
Oil cracking
Carpentier, B. et al. Abu Dhabi International Petroleum Exhibition and Conference 1998; November 11-14
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Predicting Tar-mat Occurrence
Asphaltene weight percentage in STO
0
10
20
30
40
50
60
7800
S field
Depth (ft)
8100
Zone 1
8400
8700
Crude-Tar Transition
9000
Zone 2
Zone 3
• Matches field observations and tar-mat’s asphaltene content in SARA
• Zone 1 – Liquid 1 (Asphaltene lean phase)
Zone 2 – Liquid 1 + Liquid 2
Zone 3 – Liquid 2 (Asphaltene rich phase)
• Such a prediction is possible only with an equation of state
• Predicted tar-mat formation depth matching the field data, from PVT
behavior in the upper parts of the reservoir
Panuganti, S.R. et al., Energy and Fuels, 2011; dx.doi.org/10.1021/ef201280d
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Tar-mat Analysis
Asphaltene Weight % in STO
2
7
Asphaltene Weight % in STO
12
0
24000
10
20
30
40
50
60
7800
26000
8100
Depth (ft)
Depth (ft)
28000
30000
32000
34000
8400
8700
9000
36000
Tahiti field
S field
Can the T field have an S field situation and vice versa ?
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Asphaltene Compositional Gradient Isotherms
Asphaltene weight % in STO
0
10
20
30
40
50
60
70
80
90
7800
S
field
Liquid 1 + Liquid 2
8800
P = 3500 Psia
P = 4000 Psia
Depth (ft)
9800
P = 5500 Psia
P = 7500 Psia
P = 10000 Psia
10800
P = 15000 Psia
Phase Boundary
11800
12800
Thus any field can show large or low asphaltene gradients
without a need of asphaltene precipitation
17
Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance
Conclusion
• Successful capture of asphaltene PVT behavior in the
upper parts of the reservoir
• Evaluated reservoir connectivity through asphaltene
compositional grading
• Predicted tar-mat occurrence depth because of
asphaltene compositional grading
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Future Release
Input Parameters
Property
Density Mol. Weight
Boiling Point
Function of
Temperature
Mixtures
Critical
Temperature
Y
Y
Y
N/A
Y
Critical
Pressure
Y
Y
Y
N/A
Y
Surface
Tension
Y
Y
Y
Y
N
Molecular
Polarizability
N
Y
N
N/A
N/A
Dielectric
Constant
Y
N
N
Y
Y
Basis : Quantum and Statistical
Mechanics
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Predicted vs Experiment
1100
Critical Temperature (K) for 77
Nonolar Hydrocarbons
700
X=Y
50
500
40
X=Y
300
300
500
700
Experiment
900
1100
Predicted
Predicted
900
30
20
10
Mean Polarizability of 53 Nonpolar
Hydrocarbons
(cc, 10^-24)
0
0
10
20
30
Experiment
40
50
20
Predicted vs Experiment
1100
Critical Temperature (K) for 77
Nonolar Hydrocarbons
700
n-Alkanes
50
Cyclo-Alkanes
500
Branched-Alkanes
40
Aromatics
Polynuclear Aromatics
300
300
500
700
Experiment
900
1100
Predicted
Predicted
900
Alkenes
30
Alkynes
X=Y
20
10
Mean Polarizability of 53 Nonpolar
Hydrocarbons
(cc, 10^-24)
0
0
10
20
30
Experiment
40
50
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Acknowledgement
ADNOC OPCO’s R&D
DeepStar
Chevron ETC
Schlumberger
New Mexico Tech
Infochem
VLXE
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