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Utah Tomography and Modeling/Migration (UTAM) Consortium S. Brown, Chaiwoot B., W. Cao, W. Dai, S. Hanafy, G. Zhan, G. Schuster, Q. Wu, X. Wang, Y. Xue,

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Presentation on theme: "Utah Tomography and Modeling/Migration (UTAM) Consortium S. Brown, Chaiwoot B., W. Cao, W. Dai, S. Hanafy, G. Zhan, G. Schuster, Q. Wu, X. Wang, Y. Xue,"— Presentation transcript:

1 Utah Tomography and Modeling/Migration (UTAM) Consortium S. Brown, Chaiwoot B., W. Cao, W. Dai, S. Hanafy, G. Zhan, G. Schuster, Q. Wu, X. Wang, Y. Xue, and S. Zhang

2 2009 UTAM Consortium BPChevron-TexacoCGG-VeritasPemex-IMPPetrobrasPGSPemex-IMP PetrobrasSaudi-AramcoSchlumberger-WGTGSTullowTotal ($30 K/year)‏

3 2009 Annual UTAM Meeting Jan. 7-8, 2010Jan. 7-8, 2010 Univ.of Utah, Salt Lake CityUniv.of Utah, Salt Lake City All 2009 UTAM members invitedAll 2009 UTAM members invited 3D RTM code by Sam Brown3D RTM code by Sam Brown 3D waveform tomography code3D waveform tomography code 2D waveform tomography code2D waveform tomography code

4 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

5 RTM Problem & Possible Soln. Problem: RTM computationally costlyProblem: RTM computationally costly Solution: Multisource LSM RTMSolution: Multisource LSM RTM 5 Preconditioning speeds up by factor 2-3 LSM reduces crosstalk

6 Random Time Shifted CSG and Add : m’ = m - L T [Lm - d] Multisource Least Squares Migration Workflow f d =d + d 1 2 Compute Preconditioner : f = [L T L] Iterate Preconditioned CG: *f =

7 9 SEG/EAGE Salt Model Depth (km)‏ X (km)‏ Velocity (m/s)‏ Time (s)‏ X (km)‏ CSG Multisource CSG

8 3 Z (km)‏ ModelLSM Kirchhoff Migration Model, KM, and LSM Images 0 3km LSM 10 srcs (5 its)‏ KM 10 Srcs LSM 10 srcs (30 its)‏ 90x 1x 1.5x 9x 0.1x

9 Conclusions 1. Multisrc. LSM effective in suppressing cross- talk for up to 40 simultaneous sources, but at loss subsalt accuracy 2. Multisrc. LSM with 5 iterations+deblurring acceptable results for MVA 3. Caveat: KM & Modeling were adjoints of one another 4. Need formula for S/N(# srcs,  x)‏

10 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

11 Multi-Source MVA (Xin Wang)‏ Strategy Generate multi-source data with known time shift LSM with Deblurring Filter from the background velocity model Formation of CIGs Manually pick reflectors depths Pick event depths and convert depth residuals into traveltime residuals Smear residuals through background velocity model to update velocity model and estimate the step length Using steep descent method to update the velocity model LSM with Deblurring Filter from the updated velocity model MVAMVA

12 True ModelBackground Velocity Model 0 X (km)‏ X (km)‏ CIG of 16 Multi-Source CIG of Single Source Multi-Source MVA Layer Model ns ns km/s km/s LSM with MF of 16 Sources 0 X (km)‏

13 True ModelBackground Velocity Model 0 X (km)‏ X (km)‏ CIG of 8 Multi-Source CIG of Single Source Multi-Source MVA 2D SEG/EAGE Sslt Model ns ns km/s km/s LSM with MF of 10 Sources 0 X (km)‏

14 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

15 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

16 0X(m)‏ Z (m)‏ m/s Single-Source Waveform Tomogram 0X(m)‏ Z (m)‏ m/s Marmousi Model Smooth Starting Model Marmousi Model and Multiscale Waveform Inversion 12-Source Waveform Tomogram

17 Conclusions 1. >10x speedup with deblurred multisrc waveform inversion 2. Optimal strategy for distribution of sources? 3. Potential extra speedup with 3D?

18 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

19 9 Convergence Problem with Strong Velocity Contrast and Narrow Aperture (Chaiwoot)‏ Depth (km)‏ X (km)‏ Velocity (m/s)‏

20 10 Initial Velocity Models Depth (km)‏ 0 4 X (km)‏ v(z) Model Traveltime Tomogram Velocity (m/s)‏

21 13 Flooding Technique Depth (km)‏ 0 4 X (km)‏ Waveform Tomogram after Salt Flood Using v(z) Model w/o Flooding Velocity (m/s)‏ Waveform Tomogram after Salt+Sediment Flood

22 Conclusions 1. Not having low  and wide src-rec offset not necessarily a show stopper 2. Flooding+waveform tomography strategy for for subsalt tomography for subsalt tomography 3. Problem: Need update strategy for salt boundary delineation with complicated salt. boundary delineation with complicated salt.

23 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

24 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

25 3D Full-Waveform Inversion: Synthetic Result True Velocity ModelVelocity Tomogram 4096 IBM Processors

26 3D Full-Waveform Inversion: Synthetic Result 4096 IBM Processors

27 Conclusions 1. 3D Waveform Tomography adapted to KAUST’s IBM Shaheen: >220 Tflops 2. Flooding+waveform tomography strategy 3. Preliminary results for 3D Pemex GOM data by January? data by January? 4. Goal: 3D TI in 2010

28 Midyear Overview Multisrc. Least Squares Mig. with DeblurringMultisrc. Least Squares Mig. with Deblurring Waveform Inversion+FloodingWaveform Inversion+Flooding Markov Chain Salt PickerMarkov Chain Salt Picker Fast Multisrc. Waveform Inversion with DeblurringFast Multisrc. Waveform Inversion with Deblurring 3D Waveform Inversion3D Waveform Inversion Multisource MVA with DeblurringMultisource MVA with Deblurring Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data

29 G(x|A)‏ Natural Green’s function SSP Sea bed Reflectors Ocean Surface x B A G(x|B)‏ Model based data SSP Sea bed Ocean Surface x B A Virtual source G(B|A)‏ Interpolated data SSP Sea bed Reflectors Ocean Surface x B A Virtual receiver Interferometric Interpolation OBS & SSP Data (Sherif Hanafy)‏

30 SEG/EAGE Velocity Model Velocity (m/s)‏

31 Acquisition Parameters Input –12 Streamers –Crossline offset is 150 m –Inline offset is 25 m –310 receivers/streamer –Total number of receivers 3720 Goal –33 Streamers –Crossline offset is 50 m –Inline offset is 12.5 m –619 receivers/streamer –Total number of receivers Sparse geometryDense geometry

32 Scale 7 km Time (s)‏ Streamer 21 SEG/EAGE Model – Input Data

33 Scale 7 km ’1’ 4 6 Time (s)‏ Streamer 212’1’ SEG/EAGE Model – Virtual Data No Matching Filter 4 6 Time (s)‏ Streamer 212’1’ 4 6 Time (s)‏ Streamer 4132 Actual CSG

34 Trace Comparison Time (s)‏ X (km)‏ These traces are extracted from virtual streamer # 2, where all traces in this streamer are interferometrically generated. Red lines are real traces Blue lines are virtual traces

35 Conclusions 3D marine SSP data can be interpolated with interferometry.3D marine SSP data can be interpolated with interferometry. Proposed approach is successfully tested on synthetic models.Proposed approach is successfully tested on synthetic models. Limitations:  x < /2 orLimitations:  x < /2 or Future: Field data test, Extrapolation of the dataFuture: Field data test, Extrapolation of the data

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37 Center for Subsurface Imaging and Fluid Modeling (CSIM) Consortium 2 Professors, 3 Postdocs, 1 Research Associate, 4 PhD students 2 Professors, 3 Postdocs, 1 Research Associate, 4 PhD students G.T. Schuster and Shuyu Sun

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39 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 EM imaging EM imaging Goal: Develop innovative and integration of computational Goal: Develop innovative and integration of computational methods for seismic imaging and subsurface fluid flow methods for seismic imaging and subsurface fluid flow modeling. Examples include 3D waveform inversion, 3D RTM, modeling. Examples include 3D waveform inversion, 3D RTM, TI modeling, reservoir fluid simulator. TI modeling, reservoir fluid simulator.CSIM Advantages : More than $2,000,000 in KAUST researchAdvantages : More than $2,000,000 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

40 Research Goals G.T. Schuster (Columbia Univ., 1984)‏ Seismic Interferometry: VSP, SSP, OBS Multisource+Preconditioned RTM+MVA: Waveform Tomography+RTM+TI+EM+Real Time Steering: Shaheen Cornea

41 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

42 2010 CSIM Consortium Inaugural Members: Aramco British Petroleum British Petroleum ($25 K/year)‏ Annual Meeting: Houston Jan. 7-8, 2011 Midyear Report: Summer 2010 Software Policy: Same as UTAM for Schuster

43 Students & Membership 3 of Schuster’s PhD students get dual UU- KAUST dual degrees Schuster on committee exploring dual degree Membership agreement similar to UTAM Money sent to KAUST USA in WASH.DC “Free” student interns

44 Visit to KAUST Day 1: rest and dinner Day 2: Technical talks and tour Day 3: Red Sea coral reef scuba diving or golf or golf KAUST hosts your hotel+food. 3-day visit in March


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