1 / 45

Imaging Conditions for Primary Reflections and for Multiple Reflections

Imaging Conditions for Primary Reflections and for Multiple Reflections. Jianming Sheng, Hongchuan Sun, Yue Wang and Gerard T. Schuster. University of Utah. Outline. Introduction. Primary-Only Imaging Condition. Multiple-Only Imaging Condition. Conclusions. Introduction.

eryk
Download Presentation

Imaging Conditions for Primary Reflections and for Multiple Reflections

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Imaging Conditions for Primary Reflections and for Multiple Reflections Jianming Sheng, Hongchuan Sun, Yue Wang and Gerard T. Schuster University of Utah

  2. Outline • Introduction • Primary-Only Imaging Condition • Multiple-Only Imaging Condition • Conclusions

  3. Introduction Other de-multiple methods: (prior to migration imaging) (1) Exploit moveout differences (2) Predict and subtract multiples

  4. Introduction Our two new approaches: (during migration imaging) (1) POIC (1) Primary-Only Imaging Condition: Migrate primary reflections Discard multiple reflections (2) MOIC (2) Multiple-Only Imaging Condition: Migrate multiple reflections Discard primary reflections

  5. Outline • Introduction • Primary-Only Imaging Condition • Multiple-Only Imaging Condition • Conclusions

  6. Primary-Only Imaging Condition • Methodology • Synthetic Data Example • Unocal Field Data Example

  7. Forward Modeling Primary Multiple S S R R Depth Offset Offset

  8. M1 M1 M2 M2 M3 M3 Forward Modeled Data Primary Multiple Data P1 P1 + Time (s) Offset Offset Offset

  9. Problem in Kirchhoff Migration Data ( primary + multiple ) Standard imaging condition Image ( primary + multiple )

  10. Objective of POIC Migration Data ( primary + multiple ) Primary-only imaging condition Image ( primary + multiple )

  11. Migration with POIC Key Steps: (1) pick seismic events automatically; obs

  12. Key Steps: (2) calculate shooting angle and incidence anglefor event using local slant stack; obs Migration with POIC

  13. Migration with POIC S R  Key Steps: (3) Shoot ray from the source using shooting angle ; Depth Offset

  14. Migration with POIC S R Key Steps: (4) Shoot ray from the receiver using incidence angle;  Depth Offset

  15. Migration with POIC S R Key Steps: (5) Find the crossing point P, whose traveltime is: SP +RP   Depth P Offset

  16. POIC Constraint An event is a primary reflection only if : obs = SP + RP Primary reflections are migrated calculated picked

  17. Multiple Reflection S R   Depth P Offset

  18. Multiple Reflection An event is a multiple reflection if : obs =SP + RP Multiple reflections are discarded

  19. Migration with POIC Data ( primary + multiple ) Primary-Only Imaging Condition obs,and Image ( primary + multiple )

  20. Primary-Only Imaging Condition • Methodology • Synthetic Data Example • Unocal Field Data Example

  21. 0 Depth (km) 6 5 0 Distance (km) 5-Layer Model A Shot Gather 0 P1 P2 Time (s) P3 P4 4 0 Distance (km) 3

  22. P1 P1 P2 P2 P3 P3 P4 P4 5 5 0 0 Distance (km) Distance (km) Kirchhoff Image POIC Image 0 Multiple Depth (km) 6

  23. Primary-Only Imaging Condition • Methodology • Synthetic Data Example • Unocal Field Data Example

  24. Stack Before Multiple Removal 0 M1 Time (s) M2 M2 4 313 1400 CDP Number

  25. Stack After -p Multiple Removal 0 M1 Time (s) M2 M2 4 313 1400 CDP Number

  26. Kirchhoff Image 0 M1 M2 Depth (km) M2 4 9 2 Distance (km)

  27. POIC Image 0 M1 M2 Depth (km) M2 4 9 2 Distance (km)

  28. Outline • Introduction • Primary-Only Imaging Condition • Multiple-Only Imaging Condition • Conclusions

  29. Multiple-Only Imaging Condition • Methodology • Nine-layered Model • SEG/EAGE Salt Model

  30. Ghost G P Primary Primary S G’ G S G’ G S G’ G VIRTUAL SOURCE X X X X’ X’ X’ Step1: Create crosscorrelograms

  31. Step2: Migrate crosscorrelograms Migration image Trial image point With Imaging Condition

  32. Three-layered Model Crosscorrelogram Image Kirchhoff Image Reflector Reflector Artifacts Artifacts Key Idea of MOIC

  33. Key Idea of MOIC True reflectors Step3: Multiply the crosscorrelogram image by the Kirchhoff image

  34. Multiple-Only Imaging Condition • Methodology • Nine-layered Model • SEG/EAGE Salt Model

  35. 0 0.6 Depth (km) 1.2 1.8 2.4 3.0 Nine-Layered Model Model Crosscorrelogram image Distance (km) Distance (km) 3.0 3.0 0 0

  36. 0 0.6 Depth (km) 1.2 1.8 2.4 3.0 Nine-Layered Model Kirchhoff Image Product Image artifacts Distance (km) Distance (km) 3.0 3.0 0 0

  37. Multiple-Only Imaging Condition • Methodology • Nine-layered Model • SEG/EAGE Salt Model

  38. SEG/EAGE Salt Model 0 0.6 1.2 Depth (km) 1.8 2.4 3.0 3.6 0 5.0 10.0 15.0 Distance (km)

  39. Crosscorrelogram Image 0 0.6 1.2 Depth (km) 1.8 2.4 3.0 3.6 0 5.0 10.0 15.0 Distance (km)

  40. Kirchhoff Image 0 0.6 1.2 Depth (km) 1.8 2.4 3.0 3.6 0 5.0 10.0 15.0 Distance (km)

  41. Product Image 0 0.6 1.2 Depth (km) 1.8 2.4 3.0 3.6 0 5.0 10.0 15.0 Distance (km)

  42. Outline • Introduction • Primary-Only Imaging Condition • Multiple-Only Imaging Condition • Conclusions

  43. Conclusions POIC: Multiples are effectively attenuated during the imaging process MOIC: Multiples are considered as signal and correctly imaged

  44. Further Work POIC: 1) Apply to other field data sets; 2) Develop more robust algorithms; MOIC: 1) Attenuate crosscorrelogram artifacts; 2) Deal with high-order and internal multiples.

  45. Acknowledgments We thank the sponsors of University of Utah Tomography and Modeling /Migration (UTAM) Consortium for their financial support . We are appreciative of Yi Luo for his early insights into MOIC.

More Related