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Seismic QC & Filtering with Geostatistics

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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.

Published in: Technology, Business

Seismic QC & Filtering with Geostatistics

  1. 1. 1 Seismic QC & Filtering Using geostatistical methods
  2. 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 Page 2
  3. 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. 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. 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. 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. 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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Basemap
  8. 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. 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. 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. 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. 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. 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. 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. 15. Multivariate Filtering l We can also mixed univariate and bivariate filtering techniques to: - Enhance 4D Signature of 4D seismic datasets
  16. 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. 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. 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. 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. 20. And more … l Geostatistics is useful for geophysicists for: - Time Depth Conversion - Velocity Analysis - Seismic data QC - Merging of Datasets - Filtering - …
  21. 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. 22. Thank you for your attention For more information: Jean-Paul ROUX – Sales Manager jproux@geovariances.com www.geovariances.com

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