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)

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

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?

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


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.

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


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

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

utah tomography and modeling/migration (utam) consortium s. brown, chaiwoot b., w. cao, w. dai, s....

Documents

multiscale waveform

velocity modellsm

sources0x km5

multisource lsm rtm

lsm images0

lsm effective

deblurring filter

utah tomography