Prediction of wettability variationand its impact on flow using pore- to

reservoir-scale simulations

Matthew Jackson, Per Valvatne and Martin Blunt

Centre for Petroleum StudiesDepartment of Earth Science and Engineering

Imperial College of Science, Technology and MedicineLondon U.K.

Impact of wettability variations

• Aim of this study is to investigate and predict the effect of wettability variations on flow at the pore- and reservoir-scales

• Use a pore-scale network model in conjunction with conventional reservoir-scale simulations

• Predict experimental relative permeability and waterflood recoveries for water-wet and mixed-wet Berea sandstone assuming wettability variations result from variations in Swi

• Predict the impact on recovery of wettability variations associated with a transition zone above the oil-water contact (variations in Swi)

The network model: detailed geometry

• 9mm3 cube containing 12349 pores and 26146 throats• Reconstructed directly from a sample of Berea sandstone

—more likely to be truly predictive

The network model: detailed physics

• Two-phase flow in layers and corners• Snap-off, piston-type displacement and co-operative pore body

filling• Allow wettability alteration after drainage by changing advancing

contact angle allocated to each oil-filled pore and throat

Drainage0r

Waterflooding 90a 90a

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ativ

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Prediction of relative permeability:Water-wet Berea data (Oak, 1990)

Drainage r = 0°)

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Imbibition a = 50-80°)Uniform distribution

Prediction of relative permeability:Water-wet Berea data (Oak, 1990)

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Water saturation

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Water saturation

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Prediction of waterflood recovery:Mixed-wet Berea data (Jadhunandan and Morrow, 1990)

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0 1 2 3 4 5Pore volumes injected

Fra

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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0 1 2 3 4 5Pore volumes injected

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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0 1 2 3 4 5

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°0.3

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0 1 2 3 4 5Pore volumes injected

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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0 1 2 3 4 5Pore volumes injected

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

Prediction of waterflood recovery:Mixed-wet Berea data (Jadhunandan and Morrow, 1990)

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°0.3

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0 1 2 3 4 5Pore volumes injected

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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0 1 2 3 4 5Pore volumes injected

Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°

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Fra

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Water-Wet Swi = 0.31 Swi = 0.24

Swi = 0.18 Swi = 0.08

a=110-180°, a=130-180°,

a=85-180°, a=110-180°

Wettability variations above OWC

z

Sw

More oil wet

Water wet

Differentinitial watersaturations

Hysteresis: Killough modelR

ela

tiv

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erm

ea

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ity

W e tt in g p h a s e s a t u r a ti o n

D ra in a g e

Im b ib i ti o n

N o n -w e tti n g

p h a s e h y s te r e s is

Re

lati

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pe

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ilit

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W e tt in g p h a s e s a tu r a ti o n

D ra in a g eIm b ib i ti o n

W e tt in g

p h a se h y s ter es is

Network model: Water-wet

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a = 50-80°

Network model: Oil-wet

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a = 110-180°

Oil-wet: Killough vs. network

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Killough modelNetwork model

Effect of varying initial water saturation

Swi = 0.00Sw = 0.40

Swi = 0.05Sw = 0.40

Pores contacted by oil remain water-wet

Effect of varying initial water saturation

Pores contacted by oil become oil-wet

Swi = 0.00Sw = 0.40

Swi = 0.05Sw = 0.40

Hysteresis: Effect at reservoir-scale

• Use conventional simulation to investigate effect of wettability variations on reservoir-scale flow within transition zone

• Simulate four cases:— assume reservoir is uniformly water-wet— assume reservoir is uniformly oil-wet— recognise wettability variation

— use Killough hysteresis model with oil-wet bounding curve (measured at top of reservoir)

— use relative permeability curves derived from network model

Maureen Field Simulation Model

Simulation results

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Network modelWater-wet; no hysteresis

Killough modelOil-wet; no hysteresis

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Conclusions

• Predicted experimental relative permeability and waterflood data for water-wet and mixed-wet Berea sandstone

• Emperical hysteresis models do not capture variations in relative permeability if wettability varies with height due to variations in Swi associated with capillary rise

• Relative permeabilities predicted by network model reflect pore-scale displacement mechanisms which yield low water relative permeabilities for moderate Swi

• Wettability variation has a significant effect on predicted recovery at the reservoir-scale

• Demonstrate that network models of real rocks may be used as a tool to predict wettability variations and their impact on flow at the reservoir-scale

Acknowledgements

• BHP• Enterprise Oil• Department of Trade and Industry• Gaz de France• Japan National Oil Corporation• PDVSA-Intevep• Schlumberger• Shell• Statoil