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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. - PowerPoint PPT Presentation
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20102010
Center for Subsurface Center for Subsurface Imaging and Fluid ModelingImaging and Fluid Modeling
Shuyu Sun and GT SchusterShuyu Sun and GT Schuster
8 PhD students, 5 Research Fellows8 PhD students, 5 Research Fellows(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)
Bill Bosworth: Bill Bosworth: PhD Colgate, Marathon 21 years, PhD Colgate, Marathon 21 years,
Apache 5 years, seniorApache 5 years, senior research advisor Apacheresearch advisor Apache
Mike Zinger: Mike Zinger: BS Iowa State, Amoco 20 years,BS Iowa State, Amoco 20 years,
10 years Aramco,Team Leader Red Sea Expl.10 years Aramco,Team Leader Red Sea Expl.
David Keyes: David Keyes: PhD Harvard, Columbia Univ.,YalePhD Harvard, Columbia Univ.,Yale
Univ., GordonUniv., Gordon Bell Prize, VP SIAMBell Prize, VP SIAM
Ibrahim Hoteit: Ibrahim Hoteit: PhD J. Fourier, Data assimilationPhD J. Fourier, Data assimilation
Dinesh Kaushik: Dinesh Kaushik: PhD, Gordon Bell Prize, algorithmsPhD, Gordon Bell Prize, algorithms
C. Boonyasiriwat: C. Boonyasiriwat: PhD, U of Utah, FWI and simulationPhD, U of Utah, FWI and simulation
Raed Al Huseini: Raed Al Huseini: PhD, Economic DevelopmentPhD, Economic Development
Shuyu Sun: Shuyu Sun: PhD UT Austin, S. Carolina Univ., reservoir PhD UT Austin, S. Carolina Univ., reservoir
simulationsimulation
Great AppreciationGreat Appreciation
Mara Rovelli, Sabrina Percher, Marielaure Boulot, Antonia Mara Rovelli, Sabrina Percher, Marielaure Boulot, Antonia
Forshaw, Mirna Haydar, Mariam FouadForshaw, Mirna Haydar, Mariam Fouad
20102010
Center for Subsurface Center for Subsurface Imaging and Fluid ModelingImaging and Fluid Modeling
Shuyu Sun and GT SchusterShuyu Sun and GT Schuster
8 PhD students, 5 Research Fellows8 PhD students, 5 Research Fellows(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)(Prof Sherif Hanafy, Dr. Chaiwoot B. et al.)
• 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 eventually EM imagingeventually EM imaging
• Goal: Goal: Develop innovative computational methods for seismic 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 andCenter for Subsurface Imaging andFluid Modeling (CSIM) ConsortiumFluid Modeling (CSIM) Consortium
• AdvantagesAdvantages: : More than $1,500,000/yr in KAUST researchMore than $1,500,000/yr in KAUST research funds, tightly coupled visualization+supercomputer resourcesfunds, tightly coupled visualization+supercomputer resources + reservoir fluid modeling+ seismic imaging+ reservoir fluid modeling+ seismic imaging
• Computers: Computers: IBM Blue Gene 225 Tflop, Intel+GPU ClustersIBM Blue Gene 225 Tflop, Intel+GPU Clusters GPU+IBM expertsGPU+IBM experts
• CollaborationsCollaborations: : UT Austin (Stoffa+TTI), UU (GPU)UT Austin (Stoffa+TTI), UU (GPU)
Research GoalsResearch GoalsG.T. Schuster (Columbia Univ.,G.T. Schuster (Columbia Univ., 1984)1984)
Seismic Interferometry: VSP, SSP, OBSSeismic Interferometry: VSP, SSP, OBS
Multisource+Preconditioned RTM+MVA+Inversion+Modeling: Multisource+Preconditioned RTM+MVA+Inversion+Modeling:
TTI 3D RTM, GPU: TTI 3D RTM, GPU: Stoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUSTStoffa+CSIM, UUtah K. Johnson SCI, PSU, KAUST
ShaheenShaheen
CorneaCornea
Seismic Lab: >630 Channel capacity, resisitivitySeismic Lab: >630 Channel capacity, resisitivity
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: Inaugural Members: Aramco, Exxon, Chevron, Aramco, Exxon, Chevron,
BPBP,, Petrobras, GXT, PEMEXPetrobras, GXT, PEMEX
($25 K/year)($25 K/year)
Annual Meeting: Houston Jan. 2011Annual Meeting: Houston Jan. 2011
Midyear Report: Summer 2010Midyear Report: Summer 2010Software Policy: Same as UTAM for SchusterSoftware Policy: Same as UTAM for Schuster
Shuyu Sun PolicyShuyu Sun Policy
http://utam.gg.utah.edu/csimhttp://utam.gg.utah.edu/csim
1980
Multisource SeismicMultisource SeismicImagingImaging
vs
copper
VLIW
Superscalar
RISC
1970 1990 2010
1
100
100000
10
1000
10000
Aluminum
Year
202020001980
CPU Speed vs Year
JackJackBuckskinBuckskin
KaskidaKaskidaTiberTiber
35,055 Feet
Motivation for Better Seismic Imaging StrategyMotivation for Better Seismic Imaging Strategy
¼ billion $$$ well¼ billion $$$ well
FWI Problem & Possible Soln.FWI Problem & Possible Soln.
• Problem:Problem: FWI computationally costly FWI computationally costly
• Solution:Solution: Multisource Encoded FWI Multisource Encoded FWI
Preconditioning speeds up by factor 2-3Preconditioning speeds up by factor 2-3
Iterative encoding reduces crosstalkIterative encoding reduces crosstalk
Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
Multisource Phase Encoded ImagingMultisource Phase Encoded Imaging
d +d +dd =[ =[L +L +LL ]m ]m11 222211
LL{dd{
=[=[L +L +LL ]( ](dd + + dd ) ) 11 222211
TT TT
= = L d +L d +L dL d + + 11 222211
TT TT
LL dd + +L L dd22 112211
Crosstalk noiseCrosstalk noiseStandard migrationStandard migration
TT TT
m = m +(k+1) (k)
Multisource S/N RatioMultisource S/N Ratio
# geophones/CSG# geophones/CSG
# CSGs# CSGs
L [d + d +.. ]1 221
d +d T d , d 2211
L [d + d + … ]1 2
T , …. +….
Multisrc. Migration vs Standard Migration
# iterations# iterations
Iterative Multisrc. Migration vs Standard Migration
vs
vs
MSMSS-1
M~~
# geophones/CSG# geophones/CSG # CSGs# CSGs
MSMI
Crosstalk TermCrosstalk Term
Time Statics
Time+Amplitude Statics
QM Statics
LL dd + +L L dd22 112211
TT TT
SummarySummary
Time Statics
Time+Amplitude Statics
QM Statics
1. Multisource crosstalk term analyzed analytically1. Multisource crosstalk term analyzed analytically
2. Crosstalk decreases with increasing 2. Crosstalk decreases with increasing , randomness, , randomness, dimension, iteration #, and decreasing depthdimension, iteration #, and decreasing depth
3. Crosstalk decrease can now be tuned3. Crosstalk decrease can now be tuned
4. Some detailed analysis and testing needed to refine 4. Some detailed analysis and testing needed to refine predictions.predictions.
LL dd + +L L dd22 112211
TT TT
• Fast Multisource Least Squares Fast Multisource Least Squares Kirchhoff Mig.Kirchhoff Mig.
• Multisource Waveform Inversion (Ge Zhan)Multisource Waveform Inversion (Ge Zhan)
Multisource TechnologyMultisource Technology
0Z
k(m
)3
0 X (km) 16
The Marmousi2 Model
The area in the white box is used for S/N calculation.
0 X (km) 16
0Z
k(m
)3
0Z
(k
m)
3
0 X (km) 16
Conventional Source: KM vs LSM (50 iterations)
LSM (100x)
KM (1x)
0 X (km) 16
0Z
k(m
)3
0Z
(k
m)
3
0 X (km) 16
200-source Supergather: KM vs LSM (300 its.)
LSM (33x)
KM (1/200x)
S/N
0
1 I300
S/N =7
The S/N of MLSM image grows as the square root of the number of iterations.
MI
• Fast Multisource Least Squares Migration ( Dai)Fast Multisource Least Squares Migration ( Dai)
• Multisource Waveform Inversion (Boonyasiriwat)Multisource Waveform Inversion (Boonyasiriwat)
Multisource TechnologyMultisource Technology
Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
m =[Lm =[LTTL]L]-1-1LLTTddMultisrc-Least FWI:Multisrc-Least FWI:
Multisource Encoded FWIMultisource Encoded FWI
m’ = m - Lm’ = m - LTT[Lm - d][Lm - d]
f ~ [Lf ~ [LTTL]L]-1-1
ff Steepest DescentSteepest Descent
PreconditionedPreconditioned
Nd +Nd =[Nd +Nd =[NL +NL ]mL +NL ]m11 222211 2211 11 22
multisource preconditionermultisource preconditioner
Multiscale Waveform TomographyMultiscale Waveform TomographyMultiscale Waveform TomographyMultiscale Waveform Tomography
1. Collect data d(x,t)1. Collect data d(x,t)
2. Generate synthetic data d(x,t) by FD method2. Generate synthetic data d(x,t) by FD methodsynsyn..
3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG.3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG.synsyn.. 22
4. To prevent getting stuck in local minima:4. To prevent getting stuck in local minima: a). Invert early arrivals initiallya). Invert early arrivals initially
mute
7
b). Use multiscale: low freq. high freq.b). Use multiscale: low freq. high freq.
0 km0 km 20 km20 km
0 km0 km
6 km6 km 3 km/s3 km/s
6 km/s6 km/s
Boonyasiriwat et al., 2009, TLEBoonyasiriwat et al., 2009, TLE
3 km/s3 km/s
6 km/s6 km/s
Initial modelInitial model
5 Hz5 Hz
10 Hz10 Hz
20 Hz20 Hz
Waveform TomogramsWaveform Tomograms
3 km/s3 km/s
6 km/s6 km/s
3 km/s3 km/s
6 km/s6 km/s
3 km/s3 km/s
6 km/s6 km/s
0 km0 km
6 km6 km
0 km0 km
6 km6 km
0 km0 km
6 km6 km
0 km0 km
0 km0 km 20 km20 km
6 km6 km
17
Data Pre-ProcessingData Pre-Processing
3D-to-2D conversion3D-to-2D conversion
Attenuation compensationAttenuation compensation
Random noise removalRandom noise removal
17
Source Wavelet EstimationSource Wavelet Estimation
Pick the water-bottomPick the water-bottom
Stack along the water-bottom to obtain an estimate ofStack along the water-bottom to obtain an estimate ofsource waveletsource wavelet
Generate a stacked sectionGenerate a stacked section
In some cases, source wavelet inversion can be used.In some cases, source wavelet inversion can be used.
17
Gradient Computation and InversionGradient Computation and Inversion
Multiscale inversion: low to high frequencyMultiscale inversion: low to high frequency
Dynamic early-arrival muting windowDynamic early-arrival muting window
Normalize both observed and calculated data within the sameNormalize both observed and calculated data within the sameshotshot
Quadratic line search method (Nocedal and Wright, 2006)Quadratic line search method (Nocedal and Wright, 2006)A cubic line search can also be used.A cubic line search can also be used.
Low-pass FilteringLow-pass Filtering
18
Offset (km)
Tim
e (s)
(a) Original CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(b) 5-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)Tim
e (s)
(c) 10-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
(b) 0-15 Hz CSG (c) 0-25 Hz CSG
Dynamic Early-Arrival Muting WindowDynamic Early-Arrival Muting Window
19
Offset (km)
Tim
e (s)
(a) Original CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(b) 5-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(c) 10-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
0-15 Hz CSG
Offset (km)
Tim
e (s)
(a) Original CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(b) 5-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(c) 10-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
0-25 Hz CSG
Window = 1 s Window = 1 s
19
Offset (km)
Tim
e (s)
(a) Original CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(b) 5-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(c) 10-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
0-15 Hz CSG
Offset (km)
Tim
e (s)
(a) Original CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(b) 5-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
Offset (km)
Tim
e (s)
(c) 10-Hz CSG
0 2 4
0
0.5
1
1.5
2
2.5
3
3.5
4
0-25 Hz CSG
Window = 2 s Window = 2 s
Dynamic Early-Arrival Muting WindowDynamic Early-Arrival Muting Window
2000 20202.52.5
00
Dep
th (
km)
Dep
th (
km)
X (km)X (km)
Traveltime TomogramTraveltime Tomogram
15001500
30003000
Vel
ocity
(m
/s)
Vel
ocity
(m
/s)
Waveform TomogramWaveform Tomogram
2.52.5
00
Dep
th (
km)
Dep
th (
km)
ResultsResults
2100 2020
2.52.5
00
Dep
th (
km)
Dep
th (
km)
X (km)X (km)
Waveform TomogramWaveform Tomogram
15001500
30003000
Vel
ocity
(m
/s)
Vel
ocity
(m
/s)
2.52.5
00
Dep
th (
km)
Dep
th (
km)
Vertical Derivative of Waveform TomogramVertical Derivative of Waveform Tomogram
Kirchhoff Migration ImagesKirchhoff Migration Images
22
Kirchhoff Migration ImagesKirchhoff Migration Images
22
Comparing CIGsComparing CIGs
23
Comparing CIGsComparing CIGs
24
CIG from Traveltime Tomogram CIG from Waveform Tomogram
Comparing CIGsComparing CIGs
25
Comparing CIGsComparing CIGs
26
CIG from Traveltime Tomogram CIG from Waveform Tomogram
Comparing CIGsComparing CIGs
27
Comparing CIGsComparing CIGs
28
CIG from Traveltime Tomogram CIG from Waveform Tomogram
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
144 shot gathers144 shot gathers
3D SEG Overthrust Model(1089 CSGs)
15 km
3.5 km
15 km
3.5 km
Dynamic QMC TomogramDynamic QMC Tomogram (99 CSGs/supergather)(99 CSGs/supergather)
Static QMC TomogramStatic QMC Tomogram(99 CSGs/supergather)(99 CSGs/supergather)
15 km
Dynamic Polarity TomogramDynamic Polarity Tomogram(1089 CSGs/supergather)(1089 CSGs/supergather)
Numerical ResultsNumerical Results
Multisource FWI SummaryMultisource FWI Summary(We need faster migration algorithms & better velocity models)(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. FWIStnd. FWI Multsrc. FWI
Multisource FWI SummaryMultisource FWI Summary(We need faster migration algorithms & better velocity models)(We need faster migration algorithms & better velocity models)
Future: Multisource MVA, Interpolation, Future: Multisource MVA, Interpolation, Field Data, Migration Filtering, LSM Field Data, Migration Filtering, LSM