The Stanford VI-E Reservoir:
A Synthetic Data Set for Joint Seismic-EM Timelapse Monitoring Algorithms
Jaehoon Lee, Tapan Mukerji
Stanford University
Stanford Center for Reservoir Forecasting
Why an updated synthetic data set?
• Time-lapse (4D) seismic methods and electromagnetic (EM) imaging
techniques have been used for reservoir monitoring.
• Joint time-lapse monitoring using seismic and EM data can provide
a powerful means of reservoir management.
• Stanford V & VI are not enough for testing joint seismic-EM timelapse monitoring algorithms.
Stanford V (Mao & Journel, 1999)
Stanford VI (Castro et al., 2005)
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Workflow to Create the Stanford VI-E
Stanford VI
(Castro et al., 2005)
- Structure
- Stratigraphy (facies)
- Porosity
Permeability
Flow Simulation
- Saturation
Petrophysical Properties
- Density
- P-wave velocity
- S-wave velocity
Electrical Resistivity
Elastic Attributes
- Acoustic impedance
- S-wave impedance
- Elastic impedance
- Lame’s parameters
- Poisson’s ratio
Time-lapse Petrophysical
Properties & Elastic
Attributes
Time-lapse Electrical
Resistivity
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Stanford VI- Structure & Stratigraphy
Boundary
Channel
Point bar
Floodplain
y(m)
• Synthetic reservoir data set
(Castro et al., 2005)
– A 3-layer fluvial channel system
– Asymmetric anticline with axis
N15°E
– 4 facies
– 150×200×200 cells
– Dx = Dy = 25m, Dz =1m
x(m)
Layer 1: Sinuous channels
Layer 2: Meandering channels
Layer 3: Deltaic deposits
Stanford Center for Reservoir Forecasting
Update - Clay Mineralogy
• Elastic moduli of shale cross Hashin-Shtrikman bounds.
• The properties and fraction of clay are changed.
Old bounds
New bounds
New data
Old data
Porosity
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Update - P-wave Velocity
• Sand facies: Constant cement model (Avseth, 2000)
Constant cement model
Contact cement model
Hashin-Shtrickman bound
Porosity
• Shale facies: Gardner’s empirical relation (Gardner et al., 1974)
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Update - P-wave Velocity
Initial State
z(m)
Brine sandstone
Oil sandstone
Shale
y(m)
x(m)
Porosity
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Update - S-wave Velocity
Initial State
z(m)
Oil sandstone
Shale
y(m)
x(m)
Brine sandstone
Porosity
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Update - Elastic Attributes
Acoustic Impedance
S-wave Impedance
Lame’s Parameter l
Lame’s Parameter m
Elastic Impedance (30°)
Poisson’s Ratio n
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Addition - Electrical Resistivity
• Sand facies: Archie’s method (1942)
Rt  f  Rw , Sw , f , F 
‒
‒
‒
‒
‒
Rock mineral
Rt : Electrical resistivity of rock
Rw : Electrical resistivity of water (=0.25W·m)
Sw : Water saturation
f : Porosity
Clay mineral
F : Formation factor
• Shale facies: Waxman-Smits model (1968)
Rt  f  Rw , Sw , f , F , CEC 
‒ CEC : Cation exchange capacity
Water
Water
‒ +
‒
‒
‒
‒
‒
‒
‒
Cation
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Addition - Electrical Resistivity
Initial State
z(m)
Oil sandstone
Brine sandstone
Shale
y(m)
x(m)
Porosity
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Update – Permeability & Flow Simulation
•
•
•
•
Shale permeability is reduced by the factor of 100.
Sand facies (point bar and channel) - oil saturated (Sbrine = 0.15).
Shale facies (floodplain and boundary) - brine saturated (Sbrine = 1).
Net to gross (NTG) of pore volume is introduced; 0.05 and 1 are
assigned to shale and sand facies.
Stanford VI
Stanford VI-E
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Update – Time-lapse Elastic Attributes
Elastic Impedance (30°)
z(m)
z(m)
y(m)
x(m)
y(m)
x(m)
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Addition – Time-lapse Electrical Resistivity
z(m)
z(m)
y(m)
x(m)
y(m)
x(m)
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Ongoing Research – Statistical Integration
Reservoir - Oil Saturation
Acoustic impedance
Well Data
Elastic impedance (30°)
Electrical Resistivity
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Ongoing Research – Statistical Integration
• Assess the probability of hydrocarbon occurrence based on the
conditional probability of facies given data.
Prob.(Facies = 0.5|Data)
Prob.(Oil Sandstone|Data)
Oil sandstone
Shale
Brine sandstone
Acoustic
Impedance
(km/s·g/cc)
Elastic Impedance
(km/s·g/cc)
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Conclusions
• Large-scale data set (6 million cells), Stanford VI-E reservoir
generated for testing algorithms.
• Improved rock physics models in the Stanford VI-E reservoir.
• Generated electrical resistivity for sand and shale facies, will
allow testing EM time-lapse algorithms.
• Joint seismic and EM time-lapse monitoring, is currently being
studied. (Michael J. Tompkins,SLB)
• Value of information of seismic and EM data for reservoir
monitoring.
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