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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 %)

Pres

sure

(Psia

)

50 100 150 200 250 300 3500

2000

4000

6000

8000 Live Oil exp Bu. P

Temperature (F)

Pres

sure

(Psia

)

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 (s

cf/st

b)

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 (s

cf/st

b)

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 (f

t)

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 (f

t)

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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 (f

t)

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

Dept

h (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

Dept

h (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

icted

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

icted

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