Papia Nandi-Dimitrova Education Rice University, PhD Geophysics 2012-Present University of Wyoming, MS Geophysics2005 University of Illinois, Urbana-Champaign.

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Presentation transcript:

Papia Nandi-Dimitrova Education Rice University, PhD Geophysics 2012-Present University of Wyoming, MS Geophysics2005 University of Illinois, Urbana-Champaign BS Computer Science2002 BS Finance1997 Experience BP Exploration, production, processing, imaging R&D Conoco-Phillips, Chevron, LBNL, NCSA,+ 1

Common-offset Extended Full- Waveform Inversion Papia Nandi-Dimitrova Uwe Albertin 2

EFWI: The Extended Domain 3

EFWI: Separation of Scales Log data: Kansas Geological Survey,

EFWI: Two loops Fit modify 5

Motivation Offset domain vs shot offset=h shot gather smaller aperture larger aperture common offset bin 6

Dividing data into bins Nandi-Dimitrova & Etgen, 2016 h 7

Constant density acoustic wave-equation Born forward modeling operator Least-Squares Migration (inner loop) c = acoustic velocity p = pressure f = source function G=Greens function solution δG = perturbation of Greens function δm = velocity perturbation x’ = subsurface point at time t’ x r = receiver location at time t x s = source location at time 0 8

LSM (inner loop) Apply to model perturbation to generate predicted data Minimize LS objective function Put gradient into conjugate gradient solver for model update d m =modeled data d’=recorded data after demultiple h’=common offset bin center h= ½ surface offset distance h’+/- 2h=common offset bin 9

LSM (inner loop) Apply to model perturbation to generate predicted data Minimize LS objective function Put gradient into conjugate gradient solver for model update d m =modeled data d’=recorded data after demultiple h’=common offset bin center h= ½ surface offset distance h’+/- 2h=common offset bin 10

LSM (inner loop) Apply to model perturbation to generate predicted data Minimize LS objective function Put gradient into conjugate gradient solver for model update d m =modeled data d’=recorded data after demultiple h’=common offset bin center h= ½ surface offset distance h’+/- 2h=common offset bin 11

source= bandpassed spike, Hz 4000 – m, every 50 m (254 shots), 3700 m depth receivers= m, every 25 m (625 receivers), 3700 m depth 3 seconds recording time, 4 ms sampling 20 m grid spacing in x and z source= bandpassed spike, Hz 3500 – 5100 m, every 50 m (32 shots), 4000 m depth receivers= m, every 36 m (625 receivers), 4000 m depth 3 seconds recording time, 4 ms sampling 12 m grid spacing in x and z 12

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Migrated x=5000 split into 5 Bins Migrated x=5000 split into 10 Bins Least-Squares Migrated Gathers 15

16% data fit 25% 38% 57% 73% Offset 0 – 79% Offset 1 – 83% Offset 2 – 83% Offset 3 – 85% Offset 4 – 87% 16

LSM vs RTM RTM -> adjoint operator – equivalent to the first iteration of LSM LSM -> inverse operator – more balanced amplitudes – can compensate for imperfect acquisition 17

Field Area: Viking Graben

CORTM Gathers 20

EFWI: Two loops Fit modify 21

DSO (outer loop) Gradient calculated through Variable Projection Method (Golub and Pereyra, 2003 ) DSO on offset gathers has precedence (Mulder and ten Kroode, 2002, Chauris and Noble, 2001) This method/code was developed in Yin Huang’s thesis (2016), except in the shot domain. We expect improved results in the common-offset domain because of a larger aperture. 22 D 2 F T = tomographic operator, or the transpose of the 2 nd derivative of the Born Operator

Future Work Synthetic tests on more complex models that cannot be solved via FWI Gradient calculation via Variable Projection/Huang 2016 Application to field data TRIP sponsors, Texas Advanced Computing Center, developers of Seismic Unix, Madagascar & iWave 23 Thank you