Seismic Scanning Tunneling Macroscope Gerard T. Schuster, Sherif M. Hanafy, and Yunsong Huang.
Utah Tomography and Modeling/Migration (UTAM) Consortium S. Brown, Chaiwoot B., W. Cao, W. Dai, S....
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Transcript of Utah Tomography and Modeling/Migration (UTAM) Consortium S. Brown, Chaiwoot B., W. Cao, W. Dai, S....
Utah Tomography and Utah Tomography and Modeling/MigrationModeling/Migration (UTAM)(UTAM)
Consortium Consortium
S. Brown, S. Brown, Chaiwoot B.Chaiwoot B., , W. CaoW. Cao, W. Dai, , W. Dai, S. HanafyS. Hanafy, G. Zhan, , G. Zhan, G. SchusterG. Schuster, Q. Wu, X. Wang, , Q. Wu, X. Wang,
Y. Xue, and S. Zhang Y. Xue, and S. Zhang
2009 UTAM2009 UTAM Consortium ConsortiumBPBP
Chevron-TexacoChevron-Texaco
CGG-VeritasCGG-Veritas
Pemex-IMPPemex-IMP
PetrobrasPetrobras
PGSPGS
Pemex-IMPPemex-IMP
PetrobrasPetrobras
Saudi-AramcoSaudi-Aramco
Schlumberger-WGSchlumberger-WG
TGSTGS
TullowTullow
TotalTotal
($30 K/year)($30 K/year)
2009 Annual UTAM 2009 Annual UTAM MeetingMeeting
• 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
Midyear OverviewMidyear 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
RTM Problem & Possible Soln.RTM Problem & Possible Soln.• Problem:Problem: RTM computationally costly RTM computationally costly
• Solution:Solution: Multisource LSM RTM Multisource LSM RTM
55
Preconditioning speeds up by factor 2-3Preconditioning speeds up by factor 2-3
LSM reduces crosstalkLSM reduces crosstalk
Random Time Shifted CSG and Add :Random Time Shifted CSG and Add :
m’ = m - Lm’ = m - LTT[Lm - d][Lm - d]
Multisource Least Squares Migration WorkflowMultisource Least Squares Migration Workflow
ff
d =d + dd =d + d11 22
Compute Preconditioner : f =Compute Preconditioner : f = [L[LTTL]L] -1-1
Iterate Preconditioned CG:Iterate Preconditioned CG:
*f = *f =
9
SEG/EAGE Salt ModelSEG/EAGE Salt Model
00 161644
00
Dep
th (
km)
Dep
th (
km)
X (km)X (km) 15001500
45004500
Vel
ocit
y (m
/s)
Vel
ocit
y (m
/s)
Tim
e (s
)T
ime
(s)
X (km)X (km) X (km)X (km)
CSGCSG Multisource CSGMultisource CSG
33
Z (
km)
Z (
km)
88
00
1.51.5
Z (
km
)Z
(k
m)
ModelModel LSMLSM Kirchhoff MigrationKirchhoff Migration
Model, KM, and LSM ImagesModel, KM, and LSM Images
0 3km0 3km
LSM 10 srcs (5 its)LSM 10 srcs (5 its) KM 10 SrcsKM 10 SrcsLSM 10 srcs (30 its)LSM 10 srcs (30 its)
90x90x 1x1x
1.5x1.5x 9x9x 0.1x0.1x
ConclusionsConclusions1. Multisrc. LSM effective in suppressing 1. Multisrc. LSM effective in suppressing
cross-talk for up to 40 simultaneous cross-talk for up to 40 simultaneous sources, but at loss subsalt accuracy sources, but at loss subsalt accuracy
2. Multisrc. LSM with 5 iterations+deblurring2. Multisrc. LSM with 5 iterations+deblurring acceptable results for MVA acceptable results for MVA
3. Caveat: KM & Modeling were adjoints 3. Caveat: KM & Modeling were adjoints of one another of one another
4. Need formula for S/N(# srcs , 4. Need formula for S/N(# srcs , x)x)
Midyear OverviewMidyear 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
Multi-Source MVA Multi-Source MVA (Xin Wang)(Xin Wang)StrategyStrategy 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
MVA
True Model Background Velocity Model
0 X (km) 5.840
0 X (km) 5.84
1.4
0
CIG of 16 Multi-Source CIG of Single Source
Multi-Source MVAMulti-Source MVALayer ModelLayer Model
3
1.83
1.4
ns0
1.4
ns0
1.4
km/s3
1.83
km/s LSM with MF of 16 Sources
0 X (km) 5.84
1.4
0
True Model Background Velocity Model
0 X (km) 5.90
0 X (km) 5.9
1.4
0
CIG of 8 Multi-Source CIG of Single Source
Multi-Source MVAMulti-Source MVA2D SEG/EAGE Sslt Model2D SEG/EAGE Sslt Model
4.5
1.5
1.4
ns0
1.4
ns0
1.4
km/s4.5
1.5
km/s LSM with MF of 10 Sources
0 X (km) 5.9
1.4
0
Midyear OverviewMidyear 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
Multi-Source Waveform Inversion StrategyMulti-Source Waveform Inversion Strategy(Ge Zhan) (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
0 X(m) 1910
0Z
(m
)59
55000
2000
m/s Single-Source Waveform Tomogram
0 X(m) 1910
0Z
(m
)59
5
5000
2000
m/s
Marmousi ModelMarmousi Model
Smooth Starting Model
Marmousi Model and Multiscale Waveform InversionMarmousi Model and Multiscale Waveform Inversion
12-Source Waveform Tomogram
ConclusionsConclusions
1. >10x speedup with deblurred multisrc 1. >10x speedup with deblurred multisrc waveform inversionwaveform inversion
2. Optimal strategy for distribution of sources?2. Optimal strategy for distribution of sources?
3. Potential extra speedup with 3D?3. Potential extra speedup with 3D?
Midyear OverviewMidyear 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
9
Convergence Problem with Strong Velocity Convergence Problem with Strong Velocity Contrast and Narrow Aperture (Chaiwoot)Contrast and Narrow Aperture (Chaiwoot)
00 161644
00
Dep
th (
km)
Dep
th (
km)
X (km)X (km) 15001500
45004500
Vel
ocit
y (m
/s)
Vel
ocit
y (m
/s)
10
Initial Velocity ModelsInitial Velocity Models
00 161644
00
Dep
th (
km)
Dep
th (
km)
Dep
th (
km)
Dep
th (
km)
00
44
X (km)X (km)
v(z) Modelv(z) Model
Traveltime TomogramTraveltime Tomogram
15001500
45004500
Vel
ocit
y (m
/s)
Vel
ocit
y (m
/s)
13
Flooding TechniqueFlooding Technique
00 161644
00
Dep
th (
km)
Dep
th (
km)
Dep
th (
km)
Dep
th (
km)
00
44
X (km)X (km)
Waveform Tomogram after Salt FloodWaveform Tomogram after Salt Flood
Using v(z) Model w/o FloodingUsing v(z) Model w/o Flooding
15001500
45004500
Vel
ocit
y (m
/s)
Vel
ocit
y (m
/s)
Waveform Tomogram after Salt+Sediment FloodWaveform Tomogram after Salt+Sediment Flood
ConclusionsConclusions
1. Not having low 1. Not having low and wide src-rec offset not and wide src-rec offset not necessarily a show stopper necessarily a show stopper
2. Flooding+waveform tomography strategy for2. Flooding+waveform tomography strategy for for subsalt tomographyfor subsalt tomography
3. Problem: Need update strategy for salt 3. Problem: Need update strategy for salt boundary delineation with complicated salt.boundary delineation with complicated salt.
Midyear OverviewMidyear 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
Midyear OverviewMidyear 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
3D Full-Waveform Inversion: Synthetic Result3D Full-Waveform Inversion: Synthetic Result
True Velocity Model Velocity Tomogram
4096 IBM Processors4096 IBM Processors
3D Full-Waveform Inversion: Synthetic Result
4096 IBM Processors4096 IBM Processors
ConclusionsConclusions
1. 3D Waveform Tomography adapted to 1. 3D Waveform Tomography adapted to KAUST’s IBM Shaheen: >220 TflopsKAUST’s IBM Shaheen: >220 Tflops
2. Flooding+waveform tomography strategy2. Flooding+waveform tomography strategy
3. Preliminary results for 3D Pemex GOM3. Preliminary results for 3D Pemex GOM data by January?data by January?
4. Goal: 3D TI in 20104. Goal: 3D TI in 2010
Midyear OverviewMidyear 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
G(x|A)Natural Green’s
function
SSP
Sea bed
Reflectors
Ocean Surface
xBA
G(x|B)Model based data
SSP
Sea bed
Ocean Surface
xBA
Virtual source
G(B|A)Interpolated data
SSP
Sea bed
Reflectors
Ocean Surface
xBA
Virtual receiver
Interferometric Interpolation OBS & SSP DataInterferometric Interpolation OBS & SSP Data(Sherif Hanafy)(Sherif Hanafy)
SEG/EAGE Velocity ModelSEG/EAGE Velocity Model
Velocity (m/s)1500 4500
Acquisition ParametersAcquisition 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 20427
Sparse geometry Dense geometry
Scale
7 km0
21
4
6
Tim
e (s
)
Streamer21
SEG/EAGE Model – Input DataSEG/EAGE Model – Input Data
Scale
7 km0
212’1’
4
6
Tim
e (s
)
Streamer21 2’1’
SEG/EAGE Model – Virtual DataSEG/EAGE Model – Virtual DataNo Matching FilterNo Matching Filter
4
6
Tim
e (s
)
Streamer21 2’1’
4
6
Tim
e (s
)
Streamer41 32
Actual CSGActual CSG
Trace ComparisonTrace Comparison3.2
5.2
Tim
e (s
)
X (km)7.70
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
ConclusionsConclusions• 3D marine SSP data can be interpolated with 3D marine SSP data can be interpolated with
interferometry.interferometry.• Proposed approach is successfully tested on Proposed approach is successfully tested on
synthetic models.synthetic models.• Limitations: Limitations: x < x < /2 or/2 or
2
11 T
vvx
Bx
Ax
• Future: Field data test, Extrapolation of the Future: Field data test, Extrapolation of the datadata
Center for Subsurface Imaging Center for Subsurface Imaging and Fluid Modelingand Fluid Modeling (CSIM)(CSIM)
Consortium Consortium
2 Professors, 3 Postdocs, 1 Research Associate, 4 PhD students2 Professors, 3 Postdocs, 1 Research Associate, 4 PhD students
G.T. Schuster and Shuyu SunG.T. Schuster and Shuyu Sun
http://utam.gg.utah.edu/csimhttp://utam.gg.utah.edu/csim
• BenefitsBenefits: : Yearly Houston meeting, annual reports, access toYearly 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 imagingEM imaging
• Goal: Goal: Develop innovative and integration of computational 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.
CSIMCSIM
• AdvantagesAdvantages: : More than $2,000,000 in KAUST researchMore than $2,000,000 in KAUST research funds, tightly coupled visualization+supercomputer resourcesfunds, tightly coupled visualization+supercomputer resources + reservoir fluid modeling+ seismic imaging+ reservoir fluid modeling+ seismic imaging
Research GoalsResearch GoalsG.T. Schuster (Columbia Univ.,G.T. Schuster (Columbia Univ., 1984)1984)
SeismicSeismic Interferometry: VSP, SSP, OBS Interferometry: VSP, SSP, OBS
Multisource+Preconditioned RTM+MVA: Multisource+Preconditioned RTM+MVA:
Waveform Tomography+RTM+TI+EM+Real Time Steering:Waveform Tomography+RTM+TI+EM+Real Time Steering:
ShaheenShaheen
CorneaCornea
Research GoalsResearch GoalsShuyu Sun (UT Austin, 2005)Shuyu Sun (UT Austin, 2005)
Modeling of multiphase flow in porous media Modeling of multiphase flow in porous media (new approaches for fractures, diffusion, capillarity …) (new approaches for fractures, diffusion, capillarity …)
Advanced finite element methods 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 fluidComputational thermodynamics of reservoir fluid
2010 CSIM2010 CSIM Consortium Consortium
Inaugural Members: AramcoInaugural Members: Aramco
British Petroleum British Petroleum
($25 K/year)($25 K/year)
Annual Meeting: Houston Jan. 7-8, 2011Annual Meeting: Houston Jan. 7-8, 2011
Midyear Report: Summer 2010Midyear Report: Summer 2010
Software Policy: Same as UTAM for SchusterSoftware Policy: Same as UTAM for Schuster
Students & MembershipStudents & Membership3 of3 of Schuster’s PhD students get dualSchuster’s PhD students get dual UU-KAUST UU-KAUST
dual degreesdual degrees
Schuster onSchuster on committee exploring dual degreecommittee exploring dual degree
Membership agreement similarMembership agreement similar to UTAMto UTAM
Money sent to KAUST USA in WASH.DCMoney sent to KAUST USA in WASH.DC
““Free” student internsFree” student interns
Visit to KAUSTVisit to KAUST
Day 1: rest and dinnerDay 1: rest and dinner
Day 2: Technical talks and tourDay 2: Technical talks and tour
Day 3: Red Sea coral reef scuba divingDay 3: Red Sea coral reef scuba diving
or golfor golf
KAUST hosts your hotel+foodKAUST hosts your hotel+food..
3-day visit in March3-day visit in March