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History Matching Of A Realistic Stochastic Sub-Seismic Fault Model  Marius Verscheure (IFP) Jean-Paul Chilès (Mines Paris) André Fourno (IFP) [email_address]
[object Object],[object Object],[object Object],[object Object],[object Object]
Construction of fractured reservoir models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Static data Dynamic data Model
Multiscale fracturing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sub seismic faults : issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Objective : History Matching of Subseismic faults Fluid flow simulation Optimization Modify fault positions to reduce the objective function Statistical coherency must be preserved Upscaling Simulated data Field data Objective function Field observations Stochastic fault generator
[object Object],[object Object],[object Object],[object Object],[object Object]
Fractal Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],D f =1.65 D f =1.58
Modeling fault length distribution ,[object Object],[object Object],[object Object],[object Object]
Modeling spatial distribution ,[object Object],[object Object],[object Object],[object Object],[object Object],   Density map characterized by fractal dimension D f
Modeling spatial distribution ,[object Object],[object Object]
Modeling spatial distribution : Conditioning to seismic faults ,[object Object],[object Object]
Flow simulation model : Analytical discretization ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
Gradual deformations ,[object Object],[object Object],[object Object],[object Object],y(t) = y 0 cos(t π ) + y 1 sin(t π ) New realization Realization 0 Realization 1 t : gradual deformation parameter
Gradual deformation of a Poisson point process ,[object Object],[object Object],Y(t) U(t) X(t) gaussian numbers uniform numbers coordinates inverse CDF Sequential algorithm
Gradual deformation of the multifractal map ,[object Object],[object Object]
Reduction of the objective function ,[object Object],[object Object],Initial Model Objective function  History matched Model
[object Object],[object Object],[object Object],[object Object],[object Object]
Example : Synthetic model ,[object Object],[object Object],[object Object],[object Object],[object Object],Reference fault network
Example : Initial model ,[object Object],[object Object],[object Object],[object Object],(a) (b) (c) (d)
Initial hydrodynamic response Response different from reference model    History matching necessary Initial water cut levels for P1, P2, P3 P4. Field production data (red), simulated values (yellow )
History Matching  ,[object Object],[object Object],[object Object],Reservoir divided by zone Objective function evolution
History Matching  History matched water cut levels for P1, P2, P3 P4. Field production data (red), simulated values (yellow )
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future work ,[object Object],[object Object]
[email_address] Thank you for your attention
Some more slides...
Example : Reference model ,[object Object],[object Object],[object Object],[object Object],[object Object],P1 P2 P3
Example : Initial model
Example : Matched Model Objective function evolution
 
Generation of Poisson points ,[object Object],[object Object],[object Object],[object Object],[object Object],1 –  Generate Points 2 – Propagate Faults 3 – Merge Families
Interpolation
Determination of fractal dimension ,[object Object],[object Object],[object Object],N: Number of points, Nd(r): number of pairs of points with distance d < r D f  : fractal dimension small faults are invisible    deviation Modeling objective Add the missing faults without modifying the fractal dimension
3D faults 2D faults are extended and clipped on a corner point grid
Deformation of the network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Marius Verscheure Presentation Ecmor

  • 1. History Matching Of A Realistic Stochastic Sub-Seismic Fault Model Marius Verscheure (IFP) Jean-Paul Chilès (Mines Paris) André Fourno (IFP) [email_address]
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Objective : History Matching of Subseismic faults Fluid flow simulation Optimization Modify fault positions to reduce the objective function Statistical coherency must be preserved Upscaling Simulated data Field data Objective function Field observations Stochastic fault generator
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22. Initial hydrodynamic response Response different from reference model  History matching necessary Initial water cut levels for P1, P2, P3 P4. Field production data (red), simulated values (yellow )
  • 23.
  • 24. History Matching History matched water cut levels for P1, P2, P3 P4. Field production data (red), simulated values (yellow )
  • 25.
  • 26.
  • 27. [email_address] Thank you for your attention
  • 29.
  • 31. Example : Matched Model Objective function evolution
  • 32.  
  • 33.
  • 35.
  • 36. 3D faults 2D faults are extended and clipped on a corner point grid
  • 37.

Editor's Notes

  1. What is a good reservoir model? -predicts the future (economical purpose) -allows to develop strategies to enhance recovery To build a good model: -integrate all available data: -If static or dynamic not integrated, predictivity will be poor. -for sub seismic faults, integrate all available statistical properties
  2. Current trend in fractured reservoir: -