sai r. panuganti – rice university, houston advisor: prof. walter g. chapman – rice university,...
TRANSCRIPT
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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
Understanding Reservoir Connectivity and Tar Mat Using Gravity-Induced Asphaltene
Compositional Grading
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Outline
• Introduction
• Motivation
• PC-SAFT asphaltene phase behavior modeling
• Predicting asphaltene compositional gradient
• Prediction of tar-mat occurrence depth
• Conclusion
• Future release
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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 asphalteneshttp://www.gasandoilresearch.com/asph.html
Jill Buckley, NMT
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Compositional Grading Introduction
Used for:
First theoretical explanation – Morris Muskat, 1930
Schulte, A.M., SPE Conference, 1980; September 21-25, SPE 9235
Used for:1. To predict oil properties
with depth
2. Find out gas-oil contact
Muskat M., Physical Review, 1930; 35:1384:1393
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MotivationReservoir Connectivity
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
Understanding reservoir connectivity helps in
effective sweep of oil for a given number of wells
Pressure communication can be used only to
understand compartmentalization
Zao, J.Y., et al., Journal of Chemical & Engineering Data, 2011; 56(4):1047-1058
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PC-SAFT Modeling of Asphaltene PVT Behavior
0 10 20 30 40 50 60 70 80 90 1000
2000
4000
6000
8000
10000
12000
14000STO + Precipitant
Amount of asphaltene precipitating agent added (Mole %)
Pre
ssur
e (P
sia)
50 100 150 200 250 300 3500
2000
4000
6000
8000 Live Oil exp Bu. P
Temperature (F)
Pre
ssur
e (P
sia)
Tahiti Field - Black Oil, Offshore, Gulf of Mexico
S Field –Light Oil,Onshore,Middle East
Asphaltene Onset Pressure
Bubble Pressure
Precipitant – C1
Precipitant – C2
Precipitant – C3
Panuganti, S.R. et al., Fuel, 2012; 93:658-669
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Isothermal Compositional Grading Algorithm
Whitson, C.H., Belery, P., SPE 28000; 1994, 443-459
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Verifying the Compositional Grading Algorithm
24000 24500 25000 25500 26000 26500 27000 275000
200
400
600
800Field Data
Depth (ft)
GO
R (
scf/
stb)
Tahiti Field
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Verifying the Compositional Grading Algorithm
24000 24500 25000 25500 26000 26500 27000 275000
200
400
600
800 Field Data
PC-SAFT Prediction
Depth (ft)
GO
R (
scf/
stb)
Tahiti FieldPC-SAFT prediction matches the field data, verifying the successful working of the compositional grading algorithm
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Asphaltene Grading
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
0 0.5 1 1.5 2 2.524000
24500
25000
25500
26000
26500
27000
27500
Field Data (M21B)
Field Data (M21A Central)
Field Data (M21A North)
Optical Density (@1000 nm)
Dep
th (
ft)
Freed, D.E. et al., Energy and Fuels, 2011; 24:3942-3949
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Predicting Asphaltene Compositional Grading
• 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
0 0.5 1 1.5 2 2.524000
24500
25000
25500
26000
26500
27000
27500
PC-SAFT (M21B)
Field Data (M21B)
PC-SAFT (M21A Central)
Field Data (M21A Central)
PC-SAFT (M21A North)
Field Data (M21A North)
Optical Density (@1000 nm)
Dep
th (
ft)
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PC-SAFT Asphaltene Compositional Grading
2 4 6 8 10 12 1424000
26000
28000
30000
32000
34000
36000
Reference Depth
Asphaltene Weight % in STO
Dep
th (ft
)
• PC-SAFT asphaltene compositional grading extended to further depths
• Field observations did not report any tar mat
Tahiti field
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Predicting Asphaltene Compositional Grading 0.5 0.7 0.9 1.1 1.3 1.5
7500
7700
7900
8100
Zone A1
Zone B1
Field Data
Dimensionless Optical Density (OD/ODo)
Dep
th (
ft)
Well Z
Well X
Well Y
• 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
S field
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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
• 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
0 10 20 30 40 50 607800
8100
8400
8700
9000
Asphaltene weight percentage in STO
Dep
th (ft
)Crude-Tar Transition
Zone 1
Zone 2 Zone 3
Panuganti, S.R. et al., Energy and Fuels, 2011; dx.doi.org/10.1021/ef201280d
S field
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Tar-mat Analysis
0 10 20 30 40 50 607800
8100
8400
8700
9000
Asphaltene Weight % in STO
Dep
th (ft
)
2 4 6 8 10 12 1424000
26000
28000
30000
32000
34000
36000 Asphaltene Weight % in STO
Dep
th (ft
)
S fieldTahiti field
Can the T field have an S field situation and vice versa ?
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Asphaltene Compositional Gradient Isotherms
Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation
0 10 20 30 40 50 60 70 80 907800
8800
9800
10800
11800
12800
P = 3500 PsiaP = 4000 PsiaP = 5500 PsiaP = 7500 PsiaP = 10000 PsiaP = 15000 PsiaPhase Boundary
Asphaltene weight % in STO
Dep
th (ft
)
Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance
Liquid 1 + Liquid 2S
field
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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 ReleaseInput 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
300 400 500 600 700 800 900 1000 1100300
500
700
900
1100 Critical Temperature (K) for 77 Nono-lar Hydrocarbons
X = Y
Experiment
Pred
icte
d
0 10 20 30 40 500
10
20
30
40
50
Mean Polarizability of 53 Nonpolar Hydrocarbons (cc, 10^-24)
X=Y
Experiment
Pre
dict
ed
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Predicted vs Experiment
300 400 500 600 700 800 900 1000 1100300
500
700
900
1100 Critical Temperature (K) for 77 Nono-lar Hydrocarbons
Experiment
Pred
icte
d
0 10 20 30 40 500
10
20
30
40
50
Mean Polarizability of 53 Nonpolar Hydrocarbons (cc, 10^-24)
n-Alkanes
Cyclo-Alkanes
Branched-Alkanes
Aromatics
Polynuclear Aromatics
Alkenes
Alkynes
X=Y
Experiment
Pre
dict
ed
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AcknowledgementADNOC OPCO’s R&D
DeepStar
Chevron ETC
Schlumberger
New Mexico Tech
Infochem
VLXE