1. Pitfalls in Rock Properties and Lithology Prediction in Dolomite
John Logel
Anadarko Canada Corp

2. Objective
To demonstrate that subtle but powerful seismic phenomenon can substantially harm your seismic data and your interpretation
TLC processing and “new” algorithms may not be enough to help
Quality control, detailed investigation of data, and proper prospecting and risk are critical.

3. Dolomite as the reservoir The surprise prospect and well
Outline
Dolomite as the reservoir
The surprise prospect and well
The Mistie
The VSP results
The enemy (High order interbed multiples, Q)
Possible fixes and conclusions

4. Hydrothermal Dolomite Process
Davies, et al.

5. AI Dolo with 10 % por = LS 6% por = Shale
and economic properties
Rock Properties
Dolomite
2.87 g/cc
7000 P m/s
3960 S m/s
20090 AI
11365 SI
1.77 Vp/Vs
95 Gpa Bulk
45 Gpa Shear
Limestone
2.71 g/cc
6450 P m/s
3440 S m/s
17479 AI
9322 SI
1.88 Vp/Vs
77 Gpa Bulk
32 Gpa Shear
Shale
2.58 g/cc
5800 P m/s
2000 S m/s
15000 AI
5200 SI
2.9 Vp/Vs
21 Gpa Bulk
7 Gpa Shear
50 MM.day IP
50B – 3 TCF UR
high spacing
10 MM/day IP
10 B – 50 B UR
low drainage
0 MM /day IP
0 BCF
No more drilling
AI Dolo with 10 % por = LS 6% por = Shale
Vp/Vs changes less

6. Tuning curve for “Typical” Dolomite
reservoir
amp
4 to 20 ms zone of anomolous amplitude
For 45 Hz Dom freq
isochron

7. Dolomite Zero Offset Modeling
Dolomite Porosity Development
Dolomite
Tight
Porous

8. Processing Highlights
Boutique TLC Processing
PSTM
2 Passes of velocities
2 passes and 2 different Algorithms of De-multiple (Radon and Inquisitor hyperbolic)
Controlled Amp and Phase QC
Controlled Scaling

9. 3 Window Amp extraction NO CAPP

10. 3 Window Amp extraction CAPP (Deep Win Scaler)

11. 3 Window Amp extraction Full CAPP

12. LMR Interpretation Methodology8
LambdaRho -
MuRho
Interpretation Template
Crossplot
Guide
250
MuRho Gpa
.gm/cc
LambdaRho GPa
125
POROUS
DOLOMITE
Gas
Calcareous
Shales
Clastic
SHALE
SAND
Wet
1:1
2:1
TIGHT
LIMESTONE
100 50
150
200
Goodway etal.

13. Co-rendered Amplitude and Linament detection

14. Prospect
Waveform
classification
map
Prospect
And leads

15. Pre-drill Analysis Analogue Prospect lambda Mu Ratio diff
Prospect Analogue Well
lambda
Analogue Mu
Ratio
diff
Prospect

16. Drill the Well!!!!

17. Post-Drill !!
Prospect
Analogue
GR dt rhob
GR dt rhob

18. Raw Synthetic tie
CC= .29
Zone of
Interest

19. What Now ??

20. Petrophysicist

22. Post edit Synthetic tie
CC=.4
Zone of
Interest

23. Processing
Geophysicist

24. Other Problems, Pitfalls and Corrections
Multiples
Q (attenuation)
VSP

25. VSP showing potential sources of
Interbed multiples
High velocity
Low Velocity/Laminated
Basin Fill Shales
Outside Corridor Stack
Inside Corridor Stack
High Velocity
Zone of
Interest

26. Zero (corridor) VSP stack Inside (multiple) stack seismic data
VSP – Data comparison
Zero (corridor) VSP stack Inside (multiple) stack seismic data
Zone of
Interest

27. VSP (downgoing colour – upgoing WVA)
with Multiples
Interbed multiples
First arrivals
Interbed multiples

28. Logs Elastic Modeling Comparison Elastic Model 90 degrees Elastic
Raw
seismic
Logs
synthetics

29. Q (attenuation)
“Q” is the attenuation of frequency and the changes in phase because of it.
Shales or other “soft” material are most Q prone
Laminated material are also very Q prone
If it can be determined it can be corrected (difficult)

30. Wavelet extraction and response
Extracted wavelet including shallow
Extracted wavelet deep
Theoretical response

31. Processed VSP and Q computation
Drift Correction Method
Spectral Ratio Method

32. Q Application
correct qcomp orig

33. Q application and Amplitude Envelope
Corrected Qcomp Orig

34. VSP zos corrected model mult original
Final tie with synthetics validation
VSP zos corrected model mult original

35. Questions about Multiples????
Shouldn’t they be adequately attenuated?
Aren’t they too low of amplitude to worry about?
Aren’t they “constant”?
Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response?
Doesn’t the stack take care of it?

36. Questions about Multiples????
Shouldn’t they be adequately attenuated?
Aren’t they too low of amplitude to worry about?
Aren’t they “constant”?
Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response?
Doesn’t the stack take care of it?

37. 2D expanding models
Primaries only
Primaries and Multiples

38. 2D expanding models
Primaries only
Primaries and Multiples

39. Questions about Multiples????
Shouldn’t they be adequetely attenuated?
Aren’t they too low of amplitude to worry about?
Aren’t they “constant”?
Shouldn’t you be able to differentiate Multiples from reservoir by a different AVO response?
Doesn’t the stack take care of it?

40. AVO response for Dolomite Reservoir
Reservoir AVO response CLASS IV

41. horizons, multiples and VSP
Seismic data showing
horizons, multiples and VSP
VSP
no decon
Pri & mult
VSP
decon
Prim only
Horizons
Multiples

42. Zoom-in on multiple and logs

43. Seismic gathers showing primaries and multiples
2000
-2000

44. Inquisitor Hyperbolic transform High resolution Radon Hybrid Radon
De-Multiple “Fixes”
Inquisitor Hyperbolic transform
High resolution Radon
Hybrid Radon
Inverse Scattering Subseries
Gabor decon (radial trace domain)
Wavelet Transforms
Forensic Audiology
Discrete Fourier transform
target oriented DECON
Mapping
** Discussed in this presentation

45. Elastic Model gather with
Hyperbolic transform
velocity
Porosity
Multiple

46. Dominant Principal Comps

47. Principal Comps recombined
Original
De-Multipled

48. Zone specific DFT Amp Phase Multiple contaminated Primaries
Primary Dominant Freq
Multiple Dominant Freq
Multiple contaminated Primaries
Phase

49. Map of first Dominant Frequency

50. Target specific Gap decon
Original Section
Original
De-multipled
De-Multipled Section

51. Multiple Contaminant risk map

52. Conclusions
Higher order Interbed multiples can mask potential reservoir properties and create false anomolies.
Application of “blind” transform de-multiple can be dangerous – Must be QCed.
“Q” can hinder the interpretation and should be considered.
Correction for multiples and Q can help high grade prospect risk and improve success rate.

53. Acknowledgements
ARCIS Corp and Pulse data Inc. for permission to use there data.
Schlumberger for permission to use there seismic data
Our partners … For there support, discussion and permission to publish.
Rick Kuzmiski (GEDCO) for his input and processing of the VSP.
Rob Duthie for his input and processing of VSP and his contribution of the VSP explanation slides.
Apoterra Seismic and Spectrum Seismic for there patience in the learning and input into the seismic processing.
Andy Gamp (ACC) for some of the Multiple Modeling.

54. References
Goodway, W., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lame’s petrophysical parameters, CSEG Recorder, 22, No7, 3-5.
Li, Y.Y., Goodway, W., and Downton, J., 2003, Recent advances in application of AVO to carbonate reservoirs: case histories, CSEG Convention abstracts.
Rafavich, F., Kendall, C. H. St. C., and Todd, T. P., 1984, The relationship between acoustic properties and the petrographic character of carbonate rocks, Geophysics, 49,
Rio, P, Mukerji, T., Mavko, G., Marion, D., 1996, Velocity dispersion and upscaling in a laboratory-simulated VSP, Geophysics, 61,
Tonn, R., 1991, the determination of seismic quality factor Q from VSP data: A comparison of different computational methods. Geophysical Prospecting, 39, 1-27.