1. 2010 2010 Center for Subsurface Imaging and Fluid Modeling Shuyu Sun and GT Schuster 8 PhD students, 5 Research Fellows (Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)

3. Bill Bosworth: PhD Colgate, Marathon 21 years, Apache 5 years, senior research advisor Apache Apache 5 years, senior research advisor Apache Mike Zinger: BS Iowa State, Amoco 20 years, 10 years Aramco,Team Leader Red Sea Expl. 10 years Aramco,Team Leader Red Sea Expl. David Keyes: PhD Harvard, Columbia Univ.,Yale Univ., Gordon Bell Prize, VP SIAM Ibrahim Hoteit: PhD J. Fourier, Data assimilation Dinesh Kaushik: PhD, Gordon Bell Prize, algorithms C. Boonyasiriwat: PhD, U of Utah, FWI and simulation Raed Al Huseini: PhD, Economic Development Shuyu Sun: PhD UT Austin, S. Carolina Univ., reservoir simulation simulation

4. Great Appreciation Mara Rovelli, Sabrina Percher, Marielaure Boulot, Antonia Forshaw, Mirna Haydar, Mariam Fouad

5. 2010 2010 Center for Subsurface Imaging and Fluid Modeling Shuyu Sun and GT Schuster 8 PhD students, 5 Research Fellows (Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)

6. Benefits : Yearly Houston meeting, annual reports, access toBenefits : Yearly Houston meeting, annual reports, access to student interns, expert in fluid flow modeling, seismic, and student interns, expert in fluid flow modeling, seismic, and eventually EM imaging eventually EM imaging Goal: Develop innovative computational methods for seismic Goal: Develop innovative computational methods for seismic imaging and subsurface fluid flow modeling. Examples imaging and subsurface fluid flow modeling. Examples include 3D waveform inversion, 3D RTM, TI modeling, include 3D waveform inversion, 3D RTM, TI modeling, reservoir fluid simulator. reservoir fluid simulator. Center for Subsurface Imaging and Fluid Modeling (CSIM) Consortium Advantages : More than $1,500,000/yr in KAUST researchAdvantages : More than $1,500,000/yr in KAUST research funds, tightly coupled visualization+supercomputer resources funds, tightly coupled visualization+supercomputer resources + reservoir fluid modeling+ seismic imaging + reservoir fluid modeling+ seismic imaging Computers: IBM Blue Gene 225 Tflop, Intel+GPU ClustersComputers: IBM Blue Gene 225 Tflop, Intel+GPU Clusters GPU+IBM experts Collaborations : UT Austin (Stoffa+TTI), UU (GPU)Collaborations : UT Austin (Stoffa+TTI), UU (GPU)

7. Research Goals G.T. Schuster (Columbia Univ., 1984) Seismic Interferometry: VSP, SSP, OBS Multisource+Preconditioned RTM+MVA+Inversion+Modeling: TTI 3D RTM, GPU: Stoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUST Shaheen Cornea Seismic Lab: >630 Channel capacity, resisitivity

8. Research Goals Shuyu Sun (UT Austin, 2005) Modeling of multiphase flow in porous media (new approaches for fractures, diffusion, capillarity …) (new approaches for fractures, diffusion, capillarity …) Advanced finite element methods (dynamic mesh adaption, multiscale resolution, (dynamic mesh adaption, multiscale resolution, element-wise conservation, efficient linear solvers, …) element-wise conservation, efficient linear solvers, …) Computational thermodynamics of reservoir fluid

9. 2010 CSIM Consortium Inaugural Members: Aramco, Exxon, Chevron, BP, Petrobras, GXT, PEMEX BP, Petrobras, GXT, PEMEX ($25 K/year) Annual Meeting: Houston Jan. 2011 Midyear Report: Summer 2010 Software Policy: Same as UTAM for Schuster Shuyu Sun Policy Shuyu Sun Policy

10. http://utam.gg.utah.edu/csim

11. 1980 Multisource Seismic Imaging vs copper VLIW Superscalar RISC 197019902010 1 100 100000 10 1000 10000 Aluminum Year 202020001980 CPU Speed vs Year

12. JackBuckskin KaskidaTiber 35,055 Feet Motivation for Better Seismic Imaging Strategy ¼ billion $$$ well

13. FWI Problem & Possible Soln. Problem: FWI computationally costlyProblem: FWI computationally costly Solution: Multisource Encoded FWISolution: Multisource Encoded FWI Preconditioning speeds up by factor 2-3 Iterative encoding reduces crosstalk

14. Multisource Migration: m mig =L T d Forward Model: Multisource Phase Encoded Imaging d +d =[ L +L ]m 1221 L {d { =[ L +L ](d + d ) 122 1 TT = L d +L d + 122 1 TT L d +L d L d +L d212 1 Crosstalk noise Standard migration TT m = m + (k+1)(k)

15. Multisource S/N Ratio # geophones/CSG # CSGs L [d + d +.. ] 1 2 21 d + d T d, d 2 1 L [d + d + … ] 1 2 T, …. +….

16. Multisrc. Migration vs Standard Migration # iterations Iterative Multisrc. Migration vs Standard Migration vs MS S-1 M ~ ~ # geophones/CSG # CSGs MS MI

17. Crosstalk Term Time Statics Time+Amplitude Statics QM Statics L d +L d L d +L d212 1 TT

18. Summary Time Statics Time+Amplitude Statics QM Statics 1. Multisource crosstalk term analyzed analytically 2. Crosstalk decreases with increasing w, randomness, dimension, iteration #, and decreasing depth dimension, iteration #, and decreasing depth 3. Crosstalk decrease can now be tuned 4. Some detailed analysis and testing needed to refine predictions. predictions. L d +L d L d +L d212 1 TT

19. Fast Multisource Least Squares Kirchhoff Mig.Fast Multisource Least Squares Kirchhoff Mig. Multisource Waveform Inversion (Ge Zhan)Multisource Waveform Inversion (Ge Zhan) Multisource Technology

20. 0 Z k(m) 3 0X (km)16 The Marmousi2 Model The area in the white box is used for S/N calculation.

21. 0X (km)16 0 Z k(m) 3 0 Z (km) 3 0X (km)16 Conventional Source: KM vs LSM (50 iterations) LSM (100x) KM (1x)

22. 0X (km)16 0 Z k(m) 3 0 Z (km) 3 0X (km)16 200-source Supergather: KM vs LSM (300 its.) LSM (33x) KM (1/200x)

23. S/N 0 1 I 300 S/N = 7 The S/N of MLSM image grows as the square root of the number of iterations. MI

24. Fast Multisource Least Squares Migration ( Dai)Fast Multisource Least Squares Migration ( Dai) Multisource Waveform Inversion (Boonyasiriwat)Multisource Waveform Inversion (Boonyasiriwat) Multisource Technology

25. Multisource Migration: m mig =L T d Forward Model: m =[L T L] -1 L T d Multisrc-Least FWI: Multisource Encoded FWI m’ = m - L T [Lm - d] f ~ [L T L] -1 f Steepest Descent Preconditioned d +Nd =[N L +NL ]m Nd +Nd =[N L +NL ]m 12212112 multisource preconditioner

26. Multiscale Waveform Tomography 1. Collect data d(x,t) 2. Generate synthetic data d(x,t) by FD method syn. 3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG. syn. 2 4. To prevent getting stuck in local minima: a). Invert early arrivals initially a). Invert early arrivals initially mute 7 b). Use multiscale: low freq. high freq. b). Use multiscale: low freq. high freq.

27. 0 km 20 km 0 km 6 km 3 km/s 6 km/s Boonyasiriwat et al., 2009, TLE

28. 3 km/s 6 km/s Initial model 5 Hz 10 Hz 20 Hz Waveform Tomograms 3 km/s 6 km/s 3 km/s 6 km/s 3 km/s 6 km/s 0 km 6 km 0 km 6 km 0 km 6 km 0 km 20 km 6 km

29. 17 Data Pre-Processing 3D-to-2D conversion Attenuation compensation Random noise removal

30. 17 Source Wavelet Estimation Pick the water-bottom Stack along the water-bottom to obtain an estimate of source wavelet Generate a stacked section In some cases, source wavelet inversion can be used.

31. 17 Gradient Computation and Inversion Multiscale inversion: low to high frequency Dynamic early-arrival muting window Normalize both observed and calculated data within the same shot Quadratic line search method (Nocedal and Wright, 2006) A cubic line search can also be used.

32. Low-pass Filtering 18 (b) 0-15 Hz CSG (c) 0-25 Hz CSG

33. Dynamic Early-Arrival Muting Window 19 0-15 Hz CSG 0-25 Hz CSG Window = 1 s

34. 19 0-15 Hz CSG 0-25 Hz CSG Window = 2 s Dynamic Early-Arrival Muting Window

35. 20 020 2.5 0 Depth (km) X (km) Traveltime Tomogram 1500 3000 Velocity (m/s) Waveform Tomogram 2.5 0 Depth (km) Results

36. 21 020 2.5 0 Depth (km) X (km) Waveform Tomogram 1500 3000 Velocity (m/s) 2.5 0 Depth (km) Vertical Derivative of Waveform Tomogram

37. Kirchhoff Migration Images 22

38. Kirchhoff Migration Images 22

39. Comparing CIGs 23

40. Comparing CIGs 24 CIG from Traveltime Tomogram CIG from Waveform Tomogram

41. Comparing CIGs 25

42. Comparing CIGs 26 CIG from Traveltime Tomogram CIG from Waveform Tomogram

43. Comparing CIGs 27

44. Comparing CIGs 28 CIG from Traveltime Tomogram CIG from Waveform Tomogram

45. Multi-Source Waveform Inversion Strategy (Ge Zhan) Generate multisource field data with known time shift Generate synthetic multisource data with known time shift from estimated velocity model Multisource deblurring filter Using multiscale, multisource CG to update the velocity model with regularization Initial velocity model 144 shot gathers

46. 3D SEG Overthrust Model (1089 CSGs) 15 km 3.5 km 15 km

47. 3.5 km Dynamic QMC Tomogram (99 CSGs/supergather) (99 CSGs/supergather) Static QMC Tomogram (99 CSGs/supergather) 15 km Dynamic Polarity Tomogram (1089 CSGs/supergather) Numerical Results

48. Multisource FWI Summary (We need faster migration algorithms & better velocity models) IO 1 vs 1/20 Cost 1 vs 1/20 or better Resolution dx 1 vs 1 Sig/MultsSig ? Stnd. FWI Multsrc. FWI Stnd. FWI Multsrc. FWI

49. Multisource FWI Summary (We need faster migration algorithms & better velocity models) Future: Multisource MVA, Interpolation, Field Data, Migration Filtering, LSM