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Integration of Seismic Data and
     Uncertainties in the Facies Model
     P. Nivlet*, S. Ng, M.A. Hetle, K. Børset, A.B....
Motivation: 3D reservoir modelling



                                                                                    ...
The Snorre field

                                                                            • Location: Blocks 34/4 and ...
Motivations: Data integration




                 seismic amplitudes
                    (angle-stacks)




             ...
Challenges in integrating the data




     • Multi-scale issue

5-   72nd EAGE Conference and Exhibition – Barcelona, Jun...
Challenges in integrating the data


                                                                                     ...
The data uncertainty challenge




     • Random noise
     • Acquisition / Processing footprint
     • Angle Misalignment...
Geological setting




 1,000 m




                  •Reservoir depth: 2-2.7 km

8-   72nd EAGE Conference and Exhibition...
Traditional workflow

                                                                                           Reservoir...
Proposed workflow

                                                                                             Reservoir ...
Workflow from inversion to facies prediction
                                                           Bayesian wavelet
 ...
Geostatistical seismic inversion

       • 1D modelling of seismic amplitudes (Aki&Richards’ model): linear in
         m=...
Advantages/limitations of the technique




                                                                              ...
Inversion result: Elastic properties




14 -   72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
Impact on elastic parameter uncertainties
                                                                                ...
Inversion results QC
                                                                                            Band-pass...
Workflow from inversion to facies prediction
                                                           Bayesian wavelet
 ...
Supervised seismic facies analysis




                                          Kernel
                                  ...
Supervised seismic facies analysis

                                          Different resolution scales

               ...
Cross plots: Inversion filtered well logs



                                                                             ...
Seismic facies analysis: Sand probability results




                                                                    ...
Inversion results QC: Finding optimal well
       position

                                                              ...
Facies probability QC




                                                                               31% of wells: Go...
Inversion results QC

                                      Potential factors impacting mismatch

                        ...
3D confidence index
                                                                              • Measurement of predict...
Proposed workflow

                                                                                             Reservoir ...
Snorre: Average proportion of channel
                                  Average map estimated from 8 realizations




    ...
Concluding remarks

       • Integrated workflow from seismic inversion to consistent seismic constrained
         facies ...
Concluding remarks: Further work


       • How to refine the upscaling of elastic parameters from well log to seismic
   ...
Acknowledgements




          Thanks to Statoil, Norwegian Computing Center and the
                                Snorr...
Thank you
       Integration of Seismic Data and Uncertainties in the
       Facies Model

       Philippe Nivlet
       P...
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Integration of seismic data

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Integration of Seismic

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Integration of seismic data

  1. 1. Integration of Seismic Data and Uncertainties in the Facies Model P. Nivlet*, S. Ng, M.A. Hetle, K. Børset, A.B. Rustad (Statoil ASA), P. Dahle, R. Hauge & O. Kolbjørnsen (Norwegian Computing Center) 1- Classification: Internal 2010-06-10
  2. 2. Motivation: 3D reservoir modelling Reservoir simulations Production data 3D reservoir model 2- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  3. 3. The Snorre field • Location: Blocks 34/4 and 34/7 in the Tampen area, in the northern part of the North Sea (191 km2) • Production start: 1992 • Production (2009): ~180,000 bbl/day 3- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  4. 4. Motivations: Data integration seismic amplitudes (angle-stacks) Well log data 3D reservoir model Structure, stratigraphy 4- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  5. 5. Challenges in integrating the data • Multi-scale issue 5- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  6. 6. Challenges in integrating the data Shale 2.0 Vp/Vs Sand 1.7 6,000 10,000 AI (g/cm3.m/s) • Non-unique relationship between seismic amplitudes and geology • A multivariate problem 6- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  7. 7. The data uncertainty challenge • Random noise • Acquisition / Processing footprint • Angle Misalignments • Imperfect physical model 7- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  8. 8. Geological setting 1,000 m •Reservoir depth: 2-2.7 km 8- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  9. 9. Traditional workflow Reservoir grid (depth) geometry Seismic attribute Well facies+extracted (depth) seismic attribute conditioning integration Facies model 9- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  10. 10. Proposed workflow Reservoir grid (depth) geometry Seismic attribute (depth) Well facies+extracted seismic attribute conditioning integration Facies model 10 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  11. 11. Workflow from inversion to facies prediction Bayesian wavelet extraction Seismic modelling Vp Seismic facies analysis Vs Seismic (partial angle-stacks) Inversion ρ 34/4-1 34/4- m BCU = OWCLunde Increasing probability of shale SN ML SN LL Decreasing probability of shale Lomvi Fm m mBG mS mHF Facies probability 11 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  12. 12. Geostatistical seismic inversion • 1D modelling of seismic amplitudes (Aki&Richards’ model): linear in m=(log(vp), log(vs), log)) d  Gm  n • Normal distribution of elastic properties m mm|d = mBG+mG*(GmG* + e )-1(d - GmBG) m|d = m - mG*(GmG* + e )-1G m • Data (e) stationary uncertainties estimated from analysis of amplitudes • Prior (m) stationary uncertainties estimated from well log analysis 12 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  13. 13. Advantages/limitations of the technique Stationary uncertainty model: Lateral correlations - Global matrix - Different stratigraphy settings - Lateral correlations - Grid built from max. 2 horizons - Vertical correlations 13 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  14. 14. Inversion result: Elastic properties 14 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  15. 15. Impact on elastic parameter uncertainties Seismic bandwidth (Near) 0 10 20 30 AI 0 Vp Rho -50 SI Vs Vp/Vs 0 20 40 60 Frequency (Hz) Prior Posterior uncertainty Prior Posterior uncertainty variation (%) variation AI (%) 15 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  16. 16. Inversion results QC Band-pass filtered AI SI Rhob 100 ms Well Inversion Multivariate correlation (RV) between band- pass well-logs and inversion results  35% of wells RV > 0.8  33% of wells 0.8 > RV > 0.7  32% of wells RV < 0.7 16 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  17. 17. Workflow from inversion to facies prediction Bayesian wavelet extraction Seismic modelling Vp Seismic facies analysis Vs Seismic (partial angle-stacks) Inversion ρ 34/4-1 34/4- m BCU = OWCLunde Increasing probability of shale SN ML SN LL Decreasing probability of shale Lomvi Fm m mBG mS mHF Facies probability 17 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  18. 18. Supervised seismic facies analysis Kernel estimator p(m | Sand) p(Sand | m) 18 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  19. 19. Supervised seismic facies analysis Different resolution scales Raw Well logs Filtered well logs Inversion results at well position Inversion filtered well logs μ m|d = μm+(I- Σm/dΣm-1)(m – μm) + e* 19 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  20. 20. Cross plots: Inversion filtered well logs 1 2.0 2.0 Shale Vp/Vs Vp/Vs Sand 1.7 1.7 0 6,000 10,000 6,000 10,000 AI (g/cm3 m/s) AI (g/cm3 m/s) Inversion frequency filtered Predicted SAND probability 20 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  21. 21. Seismic facies analysis: Sand probability results Sand probability 21 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  22. 22. Inversion results QC: Finding optimal well position Confidence index (khi2): Vertical sand proportion from well 100 ms compared with seismic sand probability Seismic sand probability section 22 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  23. 23. Facies probability QC  31% of wells: Good confidence  61% of wells: Medium  8% of wells: Bad confidence 23 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  24. 24. Inversion results QC Potential factors impacting mismatch Stratigraphic level ++ Position with respect to OWC + Presence of faults + Average shale proportion + 24 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  25. 25. 3D confidence index • Measurement of prediction • Weighting function in facies modelling 1 Well Confidence Inversion result [0,1] 0 25 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  26. 26. Proposed workflow Reservoir grid (depth) geometry Well facies+extracted seismic attribute conditioning integration Facies model 26 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  27. 27. Snorre: Average proportion of channel Average map estimated from 8 realizations 1 1 0 0 27 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  28. 28. Concluding remarks • Integrated workflow from seismic inversion to consistent seismic constrained facies modelling • Fast geostatistical inversion approach and facies prediction • Consistent resolution between inversion results and facies probabilities gives realistic predictions and facies models 28 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  29. 29. Concluding remarks: Further work • How to refine the upscaling of elastic parameters from well log to seismic scales? How to have a more local approach? • Constraining observed 4D signals by using predicted facies sand probability (Ayzenberg and Theune, “Stratigraphically constrained seismic 4D inversion” M017, Room 127/128, Wednesday, 9h30) • Flow simulations of constrained facies models and history matching with 4D for more predictive production prognoses 29 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  30. 30. Acknowledgements Thanks to Statoil, Norwegian Computing Center and the Snorre partners Petoro, ExxonMobil Norge, Idemitsu Petroleum, RWE Dea Norge, Total E&P Norge and Amerada Hess Norge for discussions and permission to publish this work. 30 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010
  31. 31. Thank you Integration of Seismic Data and Uncertainties in the Facies Model Philippe Nivlet Principal Geophysicist –Petek Tyrihans pniv@statoil.com, tel: +47 958 16 589 www.statoil.com 31 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010

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