1. Brad Artman undergraduate: Colorado School of Mines, Geophysical Engineer graduate: Stanford University, Ph.D. candidate work experience: –Western Atlas Logging Services, Junior Engineer –U.S. Geological Survey, Visiting Scientist –Shell Deepwater Development Inc., Petrophysicist & Exploration Geophysicist

2. passive seismic imaging at Valhall Brad Artman, Stanford Exploration Project – Advanced imaging team Monday, September 27

3. multiple modeling in the image-space Brad Artman, Stanford Exploration Project – Advanced Imaging Team Ken Matson, Advanced Imaging Team Monday, September 27

4. passive seismic imaging at Valhall Brad Artman, Stanford Exploration Project – Advanced imaging team Monday, September 27

5. passive seismology not event location structural imaging –reflection seismology: subsurface investigation from the time- delayed reflections of sound off of geologic variations. –passive imaging: with no application of controlled experimental sources, a relationship between a recorded transmission wavefield and reflection wavefields is required. requires: stationary seismometers, lots of disk space

6. crustal scale exploration

7. earthquake energy

8. capitalizing on ambient noise earthquake arrivals ocean waves wind vibrations coupled with foundations cultural activity –vehicle and boat traffic –drilling noise –nearby seismic acquisition

9. Valhall one of the North sea giant fields partners Amerada Hess, Shell and Total reservoir highly porous chalk first production1982 field life2028 field production 90,000 bpd/day expected ultimate recovery 1,050 mm stb oil produced to date (01.01.2003) 500 mm stb oil remaining reserves 540 mm stb oil high activity level – new wells & well work

10. Valhall Life of Field Seismic (LoFS) Permanent field wide seismic array installed at Valhall during 2003 –120 km seismic cables –2414 groups of 4C sensors –Covers 45sq km –3 seismic surveys acquired, 4 th to be acquired mid-September

11. operations state of the art airgun array carried by stand-by boat – 53,000 shots per survey ~1/2 cost of LoFS installations related to the source

12. passive seismology by correlation why image? –linearity of wavefield extrapolation application to Valhall LoFS why try passive seismic imaging? future plans

13. transmission wavefield time (s) depth (m) position(m)

14. ambient noise r1r2 t r1r2

15. ambient noise r1r2 t r1r2

16. ambient noise r1r2 t r1r2

17. ambient noise r1r2 t r1 r1 r1 r2 lag r1r2

18. ambient noise r1r2 t r1 r1 r1 r2 twt r1r2

19. ambient noise r1r2 t r1 r1 r1 r2 lag twt r1r2

20. 0 1200600 position(m) 20 25 30 time(s) 5 10 0200 100 lag(s) 0 0.1 0.3 400300 offset(m)

21. 0 1200600 position(m) 20 25 30 time(s) 5 10 0 -100 200 100 lag(s) 0 0.1 0.3 offset(m) 300

22. 0 1200600 position(m) 20 25 30 time(s) 5 10 0 -100-200 200 100 lag(s) 0 0.1 0.3 offset(m)

23. 0 1200600 position(m) 20 25 30 time(s) 5 10 0 -100-200100 lag(s) 0 0.1 0.3 offset(m) -300 n long traces n short traces 2

24. passive seismology by correlation why image? –linearity of wavefield extrapolation application to Valhall LoFS why try passive seismic imaging? future plans

25. why image? signal/noise enhancement one correlated shot gather migrated image

26. flow model T= Transmission wavefield D= Source wavefield (down-going) U= Receiver wavefield (up-going) R= Total reflection data R z+1 T z T z R z + U z D z U D +- T T +- correlation extrapolation

27. SR Migration flow model T= Transmission wavefield D= Source wavefield (down-going) U= Receiver wavefield (up-going) R= Total reflection data R z+1 T z T z R z + U z D z U D +- T T +- correlation extrapolation

28. CMP Migration flow model T= Transmission wavefield D= Source wavefield (down-going) U= Receiver wavefield (up-going) R= Total reflection data R z+1 T z T z R z + U z D z U D +- T T +- correlation extrapolation

29. Passive Migration flow model T= Transmission wavefield D= Source wavefield (down-going) U= Receiver wavefield (up-going) R= Total reflection data R z+1 T z T z R z + U z D z U D +- T T +- correlation extrapolation

30. Shot-profile datuming analogy R = U D R = R e R = U D e = U e (D e ) 0 1 0 0 0 * * * * 1 1 0 00 00 0 +i Kz  z +i Kz(U)  z + i Kz(D)  z +i Kz(U)  z-i Kz(D)  z

31. imaging advantages poor data quality mandates imaging transformation from transmission to reflection wavefield can be accomplished along the way saves time –n instead of n 2 traces –removes IFFT of n 2 (long) traces –trace length difference ~cancels strict compute cost savings –file i/o provides big savings 1 shot of n traces vs. n shots of n traces multiple image-space summations

32. synthetic proof of concept reflection gather active migration

33. synthetic proof of concept correlated passive gather passive migration

34. passive seismology by correlation why image? –linearity of wavefield extrapolation application to Valhall LoFS why try passive seismic imaging? future plans

35. Valhall data

37. trace # Depth slice near 88m energy localized around rig moveout across traces suggests surface noise

38. Valhall data Reflector? mono-freq. boat noise rig activity

39. Valhall pipe cut normalization 4km 12km

40. Valhall pipe cut image 4km 12km

41. Valhall pipe cut image 4km 12km

42. Valhall active seismic 4km 12km

43. Valhall pipe cut image

44. passive seismology by correlation why image? –linearity of wavefield extrapolation application to Valhall LoFS why try passive seismic imaging? future plans

45. why try passive seismic imaging understand a completely undeveloped experiment capitalize on: –existing hardware –competitor’s sources –teleseismic & local noise extend imaging bandwidth to lower frequencies imaging forward scattered modes

46. passive seismology by correlation why image? –linearity of wavefield extrapolation application to Valhall LoFS why try passive seismic imaging? future plans

47. continued exploration of existing data –multi-component experiments –appropriate bandwidth parameterization –time/energy requirements –earthquake sources rig-site continuous correlation BP’s passive seismic imaging capabilities –file-handling infrastructure –native 3D imaging algorithms

48. Oyo-Geospace cable

49. multiple modeling in the image-space Brad Artman, Stanford Exploration Project – Advanced Imaging Team Ken Matson, Advanced Imaging Team Monday, September 27

51. Surface Related Multiple Elimination (SRME) –mechanics –classic shortfall –addressing the problem through imaging shot-record imaging multiple modeling at Maddog implications and status

52. * = = * = * Surface Related Multiple Extraction

53. r s SRME

54. r s ?

55. r s

56. r s ?

57. r s

58. r s ?

59. r s

60. E N *

61. E N *

62. Exact kinematic modeling Linearly increasing amplitude error w/ order of multiples Suffers when FULL acquisition not supplied co-located sources and receivers … in the image space Exact kinematic modeling- independent of velocity Same amplitude problems (requires adaptive subtraction) Wavefront healingWavefront healing SRME

63. z x wavefront healing

64. z x

65. z x

66. Surface Related Multiple Extraction (SRME) –mechanics –classic shortfall –addressing the problem through imaging shot-record imaging multiple modeling Maddog implications and status

67. flow model T= Transmission wavefield D= Source wavefield (down-going) U= Receiver wavefield (up-going) R= Total reflection data R z+1 T z T z R z + U z D z U D +- T T +- correlation extrapolation

68. flow model M= Multiple model U = Receiver wavefield (up-going) M z M o + U o U o U z U z +- convolution extrapolation * *

69. Z=0 shot-record migration

70. Z>0

71. shot-record migration

73. shot-record normalization Z>0

74. shot-record normalization

75. image-space multiple model Z>0

76. image-space multiple model

77. Surface Related Multiple Extraction (SRME) –mechanics –classic shortfall –addressing the problem through imaging shot-record imaging multiple modeling Maddog implications and status

78. shot-record migration

79. image-space multiple model

80. migration

81. migration after subtraction

82. subtraction after migration

83. migration after subtraction

84. migration

85. Surface Related Multiple Extraction (SRME) –mechanics –classic shortfall –addressing the problem through imaging shot-record imaging multiple modeling Maddog implications and status

86. image space multiple modeling exact kinematics –inexact dynamics requires adaptive subtraction –1-2 less dimensions makes subtraction less expensive velocity independent incremental expense (1.5x) to produce during shot- record migration –direct extension to Common Image Gathers –split-spread input data required less expensive than regularization + SRME + migration

87. status documented 2- and 3-D programs running suite of 2-D synthetic tests single shot 3-D synthetic test –comprehensive testing will require significant resources

88. acknowledgements Sverre Brandsberg-Dahl, Joe Dellinger, Valhall BU Richard Clarke, John Etgen, Advanced Imaging Team Phuong Vu, David Lewis, Keith Gray, Jerry Ehlers, Randy Selzer Ken Matson, Gerchard Pfau

89. migrated conventional multiples

90. migrated image

91. image-space multiple model

92. migrated conventional multiples

93. migrated image