Pressure and saturation estimation from 4D seismic
constrained by reservoir flow simulation
*Alessandra [email protected]
Célio [email protected]
Denis [email protected]
Summer Research Workshop
2012 SEG/SPE/AAPG
Objective
Main research: development of methodology to integrate reservoir simulation and 4D seismic data in a quantitative way.
This work: estimation of pressure and saturation variations from 4D seismic data constrained by flux conditions.
Main idea of the methodology
4D seismic
Optimization
Pre Sw
ConstraintsSmin < Sw < Smax
Pmin < Pre < Pmax
Reservoir simulation
Uncertainty analysis
Model 1 Model nModel 2 …
Pre Sw Pre Sw Pre Sw
Inversionprocedure
Usual constraints
Swc< Sw < 1-Sor
Preb < Pre < Preover
Constraints frommultiple simulations
Smin< Sw < Smax
Premin < Pre < Premax
Methodology: multiple models
For each grid block: minimum (lower among all simulations) and maximum (higher among all simulations) values
Problem Statement(building a synthetic data set to test the methodology)
Reference reservoir
modelP,S maps
Inversion Algorithms
Inverted P,S maps
Base model
P,S maps PEM“Base” seismic
attributes
Multiple realizations
P,S min & max values
PEM“Reference”
seismic attributes
Inverted P,S maps(calibrated)
Simplification: no scale differences between seismic and simulation data!
Assumptions
Seismic is at simulation scale
Water injection case above bubble point pressure (no presence of gas)
PEM – unconsolidated sand model + Gassman equations
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Application: case studied Uncertainties
Porosity and permeability fields Geostatistical realizations
Faults transmissibility Relative permeability Kz/Kx
Example: geostatistical realizations ofporosity (left) and permeability (right)
400 models
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Synthetic seismic 4D difference (without noise)
P impedance S impedance
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Synthetic seismic 4D difference (with random noise)
P impedance S impedance
Application First case
Inversion is run using usual constraints. Second case
Inversion constrained by min and max extracted from 400 models simulations.
Third case Inversion constrained by min and max
extracted from some selected models simulations. Selection based on well pressure data.
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Application: production data to select the best models
2nd case3rd case
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Expected Results
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ConstraintsSwc< Sw < 1-Sor
Pb < Pre < Pover
Inversion results
Errors(estimated – true answer)
Results – First case
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Results – Second case
Inversion results
Errors(estimated – true answer)
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Results – Third case
Inversion results
Errors(estimated – true answer)
Increases number of blocks with low error
Reduces number of blocks with high error
Results
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Conclusion It was presented a methodology that uses uncertainty
analysis with reservoir simulation to constrain the estimation of dynamic properties from 4D seismic Necessary due to problems related do 4DS (noise, resolution,
scale, …) More iterations may be necessary so each technique can
constrain the other Well data (pressure) was used to improve the constraint
applied to the inversion Promising results, specially in the estimation of water
saturation variation Next step: to use seismic information in a probabilistic
approach.
Acknowledgments
UNISIM-UNICAMP
Thank you!
*Alessandra [email protected]
Célio [email protected]
Denis [email protected]
Summer Research Workshop
2012 SEG/SPE/AAPG
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