Prediction of wettability variation and its impact on flow using pore- to reservoir-scale...
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Prediction of wettability variation and its impact on flow using pore- to reservoir- scale simulations Matthew Jackson, Per Valvatne and Martin Blunt Centre for Petroleum Studies Department of Earth Science and Engineering Imperial College of Science, Technology and Medicine
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Transcript of Prediction of wettability variation and its impact on flow using pore- to reservoir-scale...
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
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.0
0.2 0.4 0.6 0.8 1.0Water Saturation
Rel
ativ
e pe
rmea
bilit
y
Prediction of relative permeability:Water-wet Berea data (Oak, 1990)
Drainage r = 0°)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.2 0.4 0.6 0.8 1.0Water Saturation
Rel
ativ
e pe
rmea
bilit
y
Imbibition a = 50-80°)Uniform distribution
Prediction of relative permeability:Water-wet Berea data (Oak, 1990)
0.0
0.1
0.2
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0.4
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0.7
0.8
0.9
1.0
0.2 0.4 0.6 0.8
Water saturation
Rel
ativ
e pe
rmea
bilit
y
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.0
0.2 0.4 0.6 0.8
Water saturation
Rel
ativ
e pe
rmea
bilit
y
Prediction of waterflood recovery:Mixed-wet Berea data (Jadhunandan and Morrow, 1990)
0.3
0.4
0.5
0.6
0.7
0.8
0 1 2 3 4 5Pore volumes injected
Fra
ctio
n o
f ori
gin
al o
il re
cove
red
Water-Wet Swi = 0.31 Swi = 0.24
Swi = 0.18 Swi = 0.08
a=110-180°
0.3
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
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
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
0 1 2 3 4 5
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)
0.3
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
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
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
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°
0.3
0.4
0.5
0.6
0.7
0.8
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
0.4
0.5
0.6
0.7
0.8
0 1 2 3 4 5Pore volumes injected
Fra
ctio
n o
f ori
gin
al o
il re
cove
red
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
e p
erm
ea
bil
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
ve
pe
rme
ab
ilit
y
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
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1Water saturation
Rel
ativ
e pe
rmea
bilit
y___
a = 50-80°
Network model: Oil-wet
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1Water saturation
Rel
ativ
e pe
rmea
bilit
y
a = 110-180°
Oil-wet: Killough vs. network
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1Water saturation
Rel
ativ
e pe
rmea
bilit
y
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1Water saturation
Rel
ativ
e pe
rmea
bilit
y
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
0
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0 0.2 0.4 0.6 0.8 1PV water injected
PV
of o
il pr
oduc
ed
Network modelWater-wet; no hysteresis
Killough modelOil-wet; no hysteresis
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1PV water injected
Wat
ercu
t
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
