Title: Upscaling of 4D Seismic Data
1Upscaling of 4D Seismic Data
- Sigurd I. Aanonsen
- Centre for Integrated Petroleum Research,
University of Bergen, Norway
Acknowledgments to
2OUTLINE
- Introduction
- History-matching with 4D seismic data
- Mapping 4D data to Simulation grid
- Upscaling
- Downscaling
- Uncertainty / data covariance
- Some conclusions and challenges
3Conditioning reservoir simulation models to 4D
data
INVERSE MODELLING
FORWARD MODELLING
4The HUTS project(History-Matching Using
Time-lapse Seismic)
5Objective 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?
6Seismic 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)
7Rescaling of Poissons ratio
AANONSEN ET AL. (2003) SPE 79665
8Moving average
- In one dimension
- Similarly in 2 and 3 dimensions
9Poissons ratio change
10Poissons ratio change
11Role 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.
12Seismic 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
13Effect 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
14Upscaling of covariance
Arithmetic average
(1)
(2)
15Upscaling of covariance
- Horizontal upscaling
- Upscale standard deviation in RMS (arithmetic
average) - Use Eq. (2) to calculate a modification factor
for each value of dx
16Upscaling 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
17Rescaling 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
18Poissons ratio change (1999-1992) Bottom layer
INITIAL WOC
INITIAL GOC
DATA
SIMULATED USING BASE CASE MODEL
19Poissons ratio change (1999-1992) Bottom layer
20Poissons ratio change (1999-1992) Bottom layer
21Poissons ratio change
22Measured vs Simulated DPOIS (top simulation layer)
MEASURED
SIMULATED
23Some 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.
24History 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