1. Jaehoon Lee, Tapan Mukerji, Michael Tompkins
Statistical Integration of Time-lapse Seismic and Electromagnetic Data for Reservoir Monitoring and Management
Jaehoon Lee, Tapan Mukerji, Michael Tompkins

2. Motivation and Objective
Joint integration of multidisciplinary geophysical data can provide complementary information not only in reservoir characterization but also in reservoir monitoring.
Statistical Rock Physics Modeling (Avseth et al., 2001; Mukerji et al., 2001)
Well logs
Rock physics modeling
Multivariate PDF
To develop a statistical workflow to integrate time-lapse seismic and EM data with proper consideration of scale differences.

3. Example: 2-D Cross Section
Stanford VI-E (Lee & Mukerji, 2012)
A 3-layer fluvial channel system.
30 year simulation with 31 producers and 15 injectors .
150×200×200 cells.
Dx = Dy = 25m, Dz =1m.
Oil Saturation (30 years)
Facies

4. Example: Impedances & Resistivity
Acoustic Impedance
Elastic Impedance (30°)
Electrical Resistivity
Born filtering
Geometric moving average

5. Example: Well Logs
Well 1
Well 2

6. Example: Well Log Data Extension
Oil sand
Shale
Oil sand
Brine sand
Shale
Brine sand
Oil sand
Brine sand
Shale

7. Example: Well-scale Data PDF
Well-log Data
(Training Data)
Field-scale Data (Classifying Attributes)

8. Example: Classification by Well-scale PDF
True Facies
Well-scale PDF
Bad classification result caused by scale difference

9. Methodology: Analogous Reservoir Generation
Field-scale Data
Well-scale Data
MP Geostatistics, SNESIM (Strebelle, 2000)
Well-scale PDF

10. Example: Simulated Field-scale PDF
Simulated Field-scale Data
(Training Data)
Field-scale Data (Classifying Attributes)

11. Example: Classification by Field-scale PDF
Well-scale PDF
True Facies
Improved classification
Field-scale PDF

12. Example: Classification by Field-scale PDF
True Facies
Only EM
Seismic & EM
Only seismic

13. Example: 2-D Cross Section (Time-lapse)
Oil Saturation (20 years)
Oil Saturation (30 years)
Facies (20 years)
Facies (30 years)

14. Methodology: Analogous Reservoir Generation
Two different saturation profiles are simulated for each realization. Seismic and EM attributes are related to each other by porosity.
Well-scale PDF

15. Methodology: Hexa-variate Gaussian mixture
Hexa-variate Gaussian mixture models are used to incorporate time-lapse seismic and EM data into statistical integration.
Acoustic Impedance
Elastic Impedance
Electrical Resistivity

16. Example: Classification with Time-lapse Data
Gaussian mixture
(with DAI, DEI, DR)
True Facies
Non-parametric
(with DAI, DEI, DR)
Gaussian mixture
(with AIt-1, EIt-1, Rt-1)

17. Conclusions
Developed analogous reservoir generation method to consider the scale differences between well logs, seismic and EM data.
Consistent way with seismic and EM forward modeling and inversion.
Filtering can be used to reduce computational time.
A few realizations are enough.
Realizations do not have to be as large as the target reservoir.
Applied hexa-variate Gaussian mixture models to incorporate time-lapse data into field-scale joint PDFs with the analogous reservoir generation method.

18. Future Work
The developed workflow will be applied to a three-dimensional synthetic reservoir and a real reservoir.
Stanford VI-E layer 3
Norne field