The quality of seismic volumes is critical in building reliable reservoir models. Seismic data are often polluted by acquisition or processing artifacts which may have strong impact on subsequent seismic processing or interpretation. Geostatistics allows filtering efficiently seismic noise and artifacts without modifying the signal. Geovariances provides solutions from seismic data quality control and filtering to reservoir characterization. This technology is based on geostatistics and all algorithms are available in Isatis, leader in geostatistical software solutions.

1. 1
Seismic QC & Filtering
Using geostatistical methods

2. Introduction
l The quality of seismic volumes is critical in building
reliable reservoir models.
l Seismic data are often polluted by acquisition or
processing artifacts which may have strong impact on
subsequent seismic processing or interpretation
l Geostatistics allows filtering efficiently seismic noise
and artifacts without modifying the true signal
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3. Seismic QC & Filtering
l Several items can impair the seismic image quality:
- Noise (ground roll, surface waves, multiples, environment, …)
- Footprints (acquisition issues)
- Artifacts (processing issues)
l The goal of Seismic QC is to detect these patterns
and to filter them out

4. Why Geostatistics?
l All imprints are spatially
correlated and can be
identified during
variogram analysis
l Example:
- Noise (nugget effect)
- Signal (short range cubic)
- Artifacts (long range spheric
vertically)
Time(mstwt)
m/s
section of velocity residuals

5. Filtering Model Components
l We can decompose a
signal into independent
components
l At each component
corresponds a variogram
structure
l We can extract the desired
component or filter out the
unwanted ones
Z = m + Y1 + Y2 + … + Yn
γ = γ1 + γ2 + … + γn

6. Kriging with Filtering (Example)
l Removes
structured
artifacts,
footprints or
white noise
l Preserves the
seismic
resolution
Raw seismic information
Filtered seismic Raw-filtered attribute
Variogram
Modelling
D1
D2
M1
M2
0. 1000. 2000. 3000. 4000. 5000. 6000. 7000. 8000.
Distance (ft)
0.
100.
200.
300.
400.
500.
Variogramofrawseismic

7. Application Example
l Data:
- 19 lines of stacking
velocities
l Processing:
- Data quality control
- Velocities analysis:
Trend extraction,
structural analysis of
the velocity residuals
and filtering of the
velocity residuals
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2
3
4
5
6
7
8
9
10
11 12
13
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Basemap

8. Application Example: Global Statistics
number 518 400
minimum 2222
maximum 6162
Mean 4992
St-deviation 546
Histogram raw velocities velocity (m/s) m/s
Xplot time/velocities

9. Application Example: Trend Extraction
A trend (large scale component) is computed from the raw
velocities by means of a least square polynomial fit method.
Section of Raw velocity
CDP
Section of Trend velocity
CDP m/s

10. Application Example: Residuals
The difference between the raw velocities and the trend
velocities gives the velocity residuals.
The structural analysis is performed on these residuals:
their scales of variability allow to identify both noise and
geological structures.
Time(mstwt)
m/s
section of velocity residuals

11. Application Example: Residuals
l Structures of the
variogram:
- a nugget effect
- a 700 CDP structure
- a 2000 ms twt structure
along the vertical (vertical
stripes)
N0
D-90
0 500 1000 1500
Distance (m)
0
1000
2000
3000
4000
5000
6000
7000
Variogram:VS:Residus

12. Application Example: Filtering of Residuals
The velocity residuals are filtered by factorial kriging. Nugget
effect and vertical stripes are filtered out. The artifacts are
calculated.
Impossible d’afficher l’image.

13. Application Example: Summary
Velocity Trend
Velocity Residuals
Raw Velocity
N0
D-90
0 500 1000 1500
Distance (m)
0
1000
2000
3000
4000
5000
6000
7000
Variogram:VS:Residus
Filtering out the
nugget and the
spherical.
Velocity Filtered
Velocity residuals filtered
Artefacts

14. Multivariate Filtering
l In addition to univariate technics we can filter
common components between several seismic
records
l This multivariate filtering can be applied to:
- Merge 2D or 3D seismic datasets to get a unique cube
- Merge or compare datasets from different origins (OBC or
streamers)
- Merge datasets of different vintages (4D) to get a single
velocity cube

15. Multivariate Filtering
l We can also mixed univariate and bivariate filtering
techniques to:
- Enhance 4D Signature of 4D seismic datasets

16. Multivariate Filtering Techniques
l To filter common components
between several seismic records
we use an extension of the
previous decomposition method
l Find the common structure(s)
between the two signals and the
remaining residuals
l It can be done automatically
(MAAFK)
γ1 = γ1,1 + γ1,2 + … + γ1,n
γ2 = γ2,1 + γ2,2 + … + γ2,m
γ1 = γs + γres1
γ2 = γs + γres2
γ1,2 = γs

17. 4D Signature Enhancement (1)
First step: Extract the common part and independent residuals
from two seismic vintages using MAAFK residuals (SBGF 2013
paper subject to approval)
6150 6160 6170 6180 6190
X (km)
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Z(m)
Vintage1
N/A
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
6150 6160 6170 6180 6190
X (km)
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Z(m)
Vintage2
N/A
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
6150 6160 6170 6180 6190
X (km)
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Z(m)
Residuals2
N/A
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
Vintage1
Vintage2
Common Part
Residuals 2
6150 6160 6170 6180 6190
X (km)
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Z(m)
Residuals1
N/A
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
6150 6160 6170 6180 6190
X (km)
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Z(m)
Common
N/A
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
Residuals 1(Geol + Fluid1 + Noise1)
(Geol + Fluid2 + Noise2)
(Geol)
(Fluid1 + Noise1)
(Fluid2 + Noise2)

18. 4D Signature Enhancement (2)
Second step: Filter out artifacts and noise for each MAAFK
residuals and compute the 4D signature by subtracting the
residuals (SBGF 2013 paper subject to approval)
615 616 617 618 619
X (km)
6080.5
6081.0
6081.5
6082.0
6082.5
Y(km)
Amplitude
N/A
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
615 616 617 618 619
X (km)
6080.5
6081.0
6081.5
6082.0
6082.5
Y(km)
Amplitude
N/A
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
615 616 617 618 619
X (km)
6080.5
6081.0
6081.5
6082.0
6082.5
Y(km)
Amplitude
N/A
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
615 616 617 618 619
X (km)
6080.5
6081.0
6081.5
6082.0
6082.5
Y(km)
Amplitude
N/A
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
615 616 617 618 619
X (km)
6080.5
6081.0
6081.5
6082.0
6082.5
Y(km)
Diff Amp
N/A
0.10
0.05
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
Residuals 1 Residuals 2
Residuals 1 filtered Residuals 2 filtered
4D Signature
XOY Cross-section
(Fluid1 + Noise1) (Fluid2 + Noise2)
(Fluid1) (Fluid2)
(Fluid2-Fluid1)

19. Benefits
l Filtering provides seismic image of better quality
which speed-up the interpretation process
l Geostatistical methods are beneficial for:
- Independent quality control
- Setting statistical evidence of anomalies
- Filtering based on the characterization of spatial continuity
- Handle non stationarity (global trend, LGS)
- 2D/3D scattered or gridded data
- Data merging
- 4D Identification

20. And more …
l Geostatistics is useful for geophysicists for:
- Time Depth Conversion
- Velocity Analysis
- Seismic data QC
- Merging of Datasets
- Filtering
- …

21. And more …
l Geostatistics is useful for geophysicists for:
- Integration of Different types of Data: Wells and Seismic
- Integration of Different Attributes: Multi-Attribute Analysis
- Uncertainty Analysis
- Elastic Inversion
- Information Extraction from 4D datasets
- …

22. Thank you for your attention
For more information:
Jean-Paul ROUX – Sales Manager
jproux@geovariances.com
www.geovariances.com