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Estimating Reservoir Connectivity and Tar-Mat Occurrence Using Gravity-Induced
Asphaltene Compositional Grading
Sameer Punnapala1, Sai Panuganti2, Francisco Vargas1 and Walter Chapman2
1 Department of Chemical Engineering, The Petroleum Institute, Abu Dhabi 2 Department of Chemical and Biomolecular Engineering, Rice University, Houston
Third EAGE/SPE Workshop on Tar Mats
Abu Dhabi, UAE 21 May 2012
Motivation
Understanding reservoir connectivity helps in effective sweep of oil for a
given number of wells
Pressure communication can only be used to understand
compartmentalization
“ 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
Zao, J.Y., et al., Journal of Chemical & Engineering Data, 2011; 56(4):1047-1058
Fast Facts about Asphaltenes
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
Deposition mechanism and molecular structure are not completely understood
Behavior depends strongly on P, T and {xi}
(a) n-C5 asphaltenes (b) n-C7 asphaltenes
Buckley, J. To be published.
Outline
Introduction
PC-SAFT Asphaltene Phase Behavior Modeling Asphaltene Compositional Grading
Prediction of tar-mat occurrence
Conclusion
Accurate Model for Asphaltene Precipitation
Advanced EOS Modeling
Case Study: Fluid B, Comparison SRK Vs PC-SAFT
-
2,000
4,000
6,000
8,000
10,000
0 100 200 300 400
Temperature, °F
Pres
sure
, psi
a
+ 5% gas (fit) + 30% gas (prediction)
PC-SAFT SRK+P
SAFT Equation of State
Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988) Gross & Sadowski, Ind. Eng. Chem. Res., 40, 1244-1260 (2001)
RTA
RTA
RTA
RTA assocchainsegres ∆
+∆
+=σ m
ε/k
Gonzalez. PhD Thesis. Rice University, 2008
PC-SAFT Modeling of Asphaltene PVT Behavior
7
Tahiti Field - Black Oil, Offshore, Gulf of Mexico GOR: 510 scf/stb API: ~30o
S Field – Light Oil, Onshore, Middle East GOR: 787 scf/stb API: ~40o
Panuganti, S.R. et al., Fuel, 2012; 93:658-669
Compositional Grading
Compositional Grading can be a result of: 1. Gravity segregation 2. Thermal diffusion 3. Incomplete hydrocarbon migration/mixing 4. Natural convection 5. Asphaltene precipitation 6. Biodegradation 7. Reservoir compartmentalization
Used to:
1. Predict oil properties with depth
2. Find out gas-oil contact
Schulte, A.M., SPE Conference, 1980; September 21-25, SPE 9235
)(),,(),,( oi
ooii hhgMTZPTZP −+= µµ
AOP
−=
RThhgM
ffo
ioii
)(expˆˆ
Compositional Grading
Tahiti Field
PC-SAFT prediction matches the field data
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
24000
24500
25000
25500
26000
26500
27000
27500
0 0.5 1 1.5 2 2.5
Dep
th (f
t)
Optical Density (@1000 nm) PC-SAFT (M21B)
Field Data (M21B)
PC-SAFT (M21A Central)
Field Data (M21A Central)
PC-SAFT (M21A North)
Field Data (M21A North)
Tahiti Field
Approximate Analytical Solution
)()()()(ln 12
2
1 hhRT
gVMhh ii
i
i −−
=ρ
ρρ
ρi= Molar density; h=Depth; = Partial Molar Volume; ρ=Mass density; Mi = Molecular Weight Assumptions: 1. Incompressible oil 2. Asphaltene is present in the oil at infinite dilution 3. System is far away from critical point 4. Isothermal System
iV
Sage, B. H.; Lacey, W. N. Los Angeles Meeting, AIME; October 1938
Approximate Analytical Solution
• Broken lines are the analytical solution predictions
• Analytical solution can be used for sensitivity analysis and approximate estimate
24000
24500
25000
25500
26000
26500
27000
27500
0 0.5 1 1.5 2 2.5
Dep
th (f
t)
Optical Density (@1000 nm) PC-SAFT (M21B)
Analytical Solution (M21B)
Field Data (M21B)
PC-SAFT (M21A Central)
Analytical Solution (M21ACentral)Field Data (M21A Central)
PC-SAFT (M21A North)
Analytical Solution (M21A North)
Field Data (M21A North)
Field Data (M21A South)
Tahiti Field
PC-SAFT Asphaltene Compositional Grading
24000
26000
28000
30000
32000
34000
36000
2 7 12
Dep
th (f
t)
Asphaltene Weight % in STO
Reference Depth
• PC-SAFT asphaltene compositional grading extended to further depths
• Field observations did not report any tar mat
Predicting Asphaltene Compositional Grading
7500
7700
7900
8100
0.5 0.7 0.9 1.1 1.3 1.5
Dep
th (f
t)
Dimensionless Optical Density (OD/ODo)
Zone A1
Zone B1
Field Data
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
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 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
Predicting Tar-mat Occurrence
7800
8100
8400
8700
9000
0 10 20 30 40 50 60
Dep
th (f
t)
Asphaltene weight percentage in STO
Crude-Tar Transition
Zone 1
Zone 2 Zone 3
Panuganti, S.R. et al., Energy and Fuels, 2011; dx.doi.org/10.1021/ef201280d
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 the tar-mat formation depth matching field data, from PVT
behavior in the upper parts of the reservoir
Tar-mat Analysis
7800
8100
8400
8700
9000
0 10 20 30 40 50 60
Dep
th (f
t)
Asphaltene Weight % in STO 24000
26000
28000
30000
32000
34000
36000
2 7 12
Dep
th (f
t)
Asphaltene Weight % in STO
S field T field
Can the T field have an S field situation and vice versa ?
Asphaltene Compositional Gradient Isotherms
Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation
7800
8800
9800
10800
11800
12800
0 10 20 30 40 50 60 70 80 90
Dep
th (f
t)
Asphaltene weight % in STO
P = 3500 PsiaP = 4000 PsiaP = 5500 PsiaP = 7500 PsiaP = 10000 PsiaP = 15000 PsiaPhase Boundary
Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance
Liquid 1 + Liquid 2 S
field
Asphaltene Compositional Gradient Isotherms
Thus any field can show large or low asphaltene gradients without a need of asphaltene precipitation
Panuganti, S.R. et al., Energy and Fuels, 2012; The 1st International Conference on Upstream Engineering and Flow Assurance
S field
7800
8800
9800
10800
11800
12800
0102030405060708090
Dep
th (f
t)
Asphaltene weight % in STO
P = 3500 Psia
P = 4000 Psia
P = 5500 Psia
P = 7500 Psia
P = 10000 Psia
P = 15000 Psia
Phase Boundary
Liquid 1 + Liquid 2
Conclusion
PC-SAFT is a highly useful EoS for modeling
asphaltenes.
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.
Acknowledgment ADNOC Oil R&D Subcommittee
EOR and FA TC.
Anju Kurup (BP), Jeff Creek (CVX), Jianxin Wang (CVX), Hari Subramani (CVX), Jill Buckley (NMT), Oliver Mullins (SLB), Dalia Abdullah (ADCO), Sanjay Misra (ADCO), Shahin Negahban (ADCO).
PI Research Team
Rice University Research Team
Structure of Asphaltene Molecule?
Modified Yen Model
Mullins OC. Energy & Fuels 2010; 24(4):2179-2207
Accurate Model for Asphaltene Precipitation ??
Colloidal Model (~1930)
Stability based on polar-polar interactions.
• Micelle formation
• Asphaltene particles kept in solution by resins adsorbed on them
Solubility Model (~1980)
Asphaltenes solubilized by the oil. Resins are in the solvent fraction
van der Waal’s interactions (London dispersion) dominate phase behavior. (Induced molecular polarizability) Polar-polar interactions: negligible
Approaches: •Flory-Huggins-regular solution theory •EOS
Two approaches for modeling asphaltene
stability:
never proven recent exp findings support this approach over the colloidal model
General Background
Parameter Estimation
Gonzalez. PhD Thesis. Rice University, 2008 Chapman, Jackson, and Gubbins, Mol. Phys. 65, 1057 (1988)
Gross & Sadowski, Ind. Eng. Chem. Res., 40, 1244-1260 (2001)
Pure Component Parameters taken from the works of Gross and Sadowski.
SAFT Parameters for Saturates calculated from correlations based on MW.
Correlations are also available for Aromatics+Resins fraction, with an adjustable parameter called Aromaticity (γ) fit to describe their tendency to behave as a benzene derivative (γ=0) or as a PNA (γ=1).
Asphaltenes parameters are fit to AOP data.
PC-SAFT Characterization Methodology
PVT Data
Fluid Characterization
Model Oil Properties (gas & liquid)
Tuning of PC-SAFT EoS to experimental data
Plot Asphaltene Precipitation Envelope
Modeling using PC-SAFT
Advanced EOS Modeling: Asphaltene Instability
-
2,000
4,000
6,000
8,000
10,000
Pres
sure
, psi
a
-
2,000
4,000
6,000
8,000
10,000
Pres
sure
, psi
a
0 100 200 300 400
Temperature, °F
+ 10% gas
100 200 300 400
Temperature, °F
predicted + 5% gas
fitted
+ 15% gas predicted
+ 30% gas predicted
Cas
e St
udy:
Flu
id B
Effect of Pressure
Z
Reservoir
0
2,000
4,000
6,000
8,000
10,000
12,000
0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26
Separator Gas, Mass Fraction
Pres
sure
, psi
aGOR = 152 m3/m3
Bubble Point Curve
Asphaltene Instability Curve
Recombined Oil
GOR = 212 m3/m3
GOR = 212 m3/m3
GOR = 152 m3/m3
A
A
C
C B
B
Ting, Hirasaki & Chapman. Pet. Sci. & Tech. 21, 647 – 661 (2003)