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2010

Center for Subsurface Imaging and Fluid Modeling. 2010. Shuyu Sun and GT Schuster. 8 PhD students, 5 Research Fellows (Prof Sherif Hanafy , Dr. Chaiwoot B. et al.). Bill Bosworth: PhD Colgate, Marathon 21 years, Apache 5 years, senior research advisor Apache.

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2010

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

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

  3. Great Appreciation Mara Rovelli, Sabrina Percher, MarielaureBoulot, Antonia Forshaw, MirnaHaydar, MariamFouad

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

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

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

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

  8. 2010 CSIM Consortium ($25 K/year) Inaugural Members: Aramco, Exxon, Chevron, BP,Petrobras, GXT, PEMEX Annual Meeting: Houston Jan. 2011 Midyear Report: Summer 2010 Software Policy: Same as UTAM for Schuster Shuyu Sun Policy

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

  10. Multisource Seismic Imaging vs CPU Speed vs Year 100000 10000 copper 1000 Aluminum VLIW Speed 100 Superscalar 10 RISC 1 1970 1980 1980 1990 2000 2010 2020 Year

  11. Motivation for Better Seismic Imaging Strategy Jack Buckskin ¼ billion $$$ well 35,055Feet Kaskida Tiber

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

  13. Ld +L d 1 2 2 1 = L d +L d + =[L +L ](d + d ) 1 2 1 2 1 1 2 2 d +d =[L +L ]m 1 2 1 2 mmig=LTd Multisource Migration: Multisource Phase Encoded Imaging { { L d Forward Model: T T T T T T m = m + (k+1) (k) Crosstalk noise Standard migration

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

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

  16. Ld +L d 1 2 2 1 Crosstalk Term T T Time Statics Time+Amplitude Statics QM Statics

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

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

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

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

  21. 200-source Supergather: KM vs LSM (300 its.) 0 Z k(m) KM (1/200x) 3 0 X (km) 16 0 Z (km) LSM (33x) 3 0 X (km) 16

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

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

  24. Nd +Nd =[NL +NL ]m 1 1 2 2 1 1 2 2 mmig=LTd Multisource Migration: m =[LTL]-1LTd Multisrc-Least FWI: multisource preconditioner Preconditioned m’ = m - LT[Lm - d] f Steepest Descent f ~ [LTL]-1 Multisource Encoded FWI Forward Model:

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

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

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

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

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

  30. 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. 17

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

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

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

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

  35. 3000 Waveform Tomogram 0 Velocity (m/s) Depth (km) 1500 2.5 Vertical Derivative of Waveform Tomogram 0 Depth (km) 2.5 21 0 X (km) 20

  36. Kirchhoff Migration Images 22

  37. Kirchhoff Migration Images 22

  38. Comparing CIGs 23

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

  40. Comparing CIGs 25

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

  42. Comparing CIGs 27

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

  44. Multi-Source Waveform Inversion Strategy (Ge Zhan) 144 shot gathers Generate multisource field data with known time shift Initial velocity model 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

  45. 3D SEG Overthrust Model(1089 CSGs) 15 km 3.5 km 15 km

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

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

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

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