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Upscaling of 4D Seismic Data

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Title: Upscaling of 4D Seismic Data


1
Upscaling of 4D Seismic Data
  • Sigurd I. Aanonsen
  • Centre for Integrated Petroleum Research,
    University of Bergen, Norway

Acknowledgments to
2
OUTLINE
  • Introduction
  • History-matching with 4D seismic data
  • Mapping 4D data to Simulation grid
  • Upscaling
  • Downscaling
  • Uncertainty / data covariance
  • Some conclusions and challenges

3
Conditioning reservoir simulation models to 4D
data
INVERSE MODELLING
FORWARD MODELLING


4
The HUTS project(History-Matching Using
Time-lapse Seismic)

5
Objective function in 4D history matching
  • q model parameters to be determined
  • p(q) simulated production data
  • s(q) simulated seismic data Simulator rock
    mechanics model
  • dp production data
  • ds seismic data Poissons ratio, r, and
    Acoustic Impedance, Zp
  • CP and CS covariance matrices (weight matrices)
  • How should we map the seismic data from seismic
    grid to simulation grid?
  • How should we determine the coefficients of this
    objective function, i.e., uncertainty in the
    seismic data?
  • The dimension of Cs may be large
  • Is it sufficient to use diagonal matrices?

6
Seismic grid vs simulation model grid
  • Horizontal cell size
  • Seismic grid 25 x 37.5 m
  • Simulation grid 100 x 200 m in oil zone
  • up to 1600 x 200 m in aquifer and
  • gas cap
  • Vertical cell size (layer thickness)
  • Seismic grid 10 m (4 layers)
  • Simulation grid 0.20 m 26 m (7 layers)

7
Rescaling of Poissons ratio
AANONSEN ET AL. (2003) SPE 79665
8
Moving average
  • In one dimension
  • Similarly in 2 and 3 dimensions

9
Poissons ratio change
10
Poissons ratio change
11
Role of the covariances
  • Difficult to determine covariances from
    measurement errors.
  • Is it possible to estimate these from the data
    itself?
  • We restrict the estimation to approximately
    rectangular uniform grids.

12
Seismic data covariance
Covariance of AI (1992)
Covariance of normalized AI (1992)
EW
NS
100
50
50
100
Distance (no. of cells)
Distance (no. of cells)
Covariance of DPOIS (99-92)
Covariance of normalized DPOIS (99-92)
100
50
100
50
Distance (no. of cells)
Distance (no. of cells)
Correlation length 200 250 m
13
Effect of data correlations PUNQ-S3 case
  • Generated synthetic seismic data with correlated
    noise. Two regressions
  • Case 1 Correct (non-diagonal) covariance matrix
  • Case 2 Diagonal matrix (correlations neglected)

Convergences to the wrong solution if
correlations are neglected
14
Upscaling of covariance
Arithmetic average
(1)
(2)
15
Upscaling of covariance
  • Horizontal upscaling
  • Upscale standard deviation in RMS (arithmetic
    average)
  • Use Eq. (2) to calculate a modification factor
    for each value of dx

16
Upscaling of covariance
  • Vertical upscaling
  • Downscale to min thickness using the equation for
    variance of averaged quantity (Eq(2)).
  • Upscale again to wanted thickness using the same
    equation.
  • The result is a mapping of covariance for any
    thickness combination.

Seismic grid
Simulation grid
4 layers Approximately constant thickness, dz
10 m
7 layers Highly varying thickness both laterally
and vertically dz min 0.20 m dzmax 26 m
17
Rescaling of Poissons ratio Summary
  • Remove noice in seismic data using moving average
  • Map the averaged data from seismic grid to
    simulation grid using RMS (arithmetic average and
    sampling)
  • Estimate data covariance matrix (uncertainty and
    correlations) on the fine grid from the noise,
    i.e., the difference between original data and
    averaged data. Need to account for
    non-stationarity.
  • Upscale covariance matrix from seismic grid grid
    to simulation grid

18
Poissons ratio change (1999-1992) Bottom layer
INITIAL WOC
INITIAL GOC
DATA
SIMULATED USING BASE CASE MODEL
19
Poissons ratio change (1999-1992) Bottom layer
20
Poissons ratio change (1999-1992) Bottom layer
21
Poissons ratio change
22
Measured vs Simulated DPOIS (top simulation layer)
MEASURED
SIMULATED
23
Some conclusions from history-matching runs
  • Managed to get significant reduction in objective
    function, but not enough to be fully satified.
  • Adding seismic data did not improve production
    data match, which was already very good, but
    hopefully a more correct solution is obtained.
  • History-matching using only seismic data
    destroyed production data match.

24
History Matching with 4D Data Main Challenges
  • Scaling issues
  • Low vertical resolution in seismic data combined
    with very variable resolution in simulated data
  • High areal resolution of seismic data
  • Current geomodelling techniques not flexible
    enough to capture variations seen in the seismic.
  • Handling of uncertainties in seismic data
  • How to handle large uncertainty in absolute
    values of inverted data?
  • Rock mechanics modelling
  • Large differences between absolute values of
    simulated and inverted elastic parameters

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