1. A CRITICAL ASSESSMENT OF A NEW
POST-STACK BATCH-Q-ESTIMATION
ALGORITHM
Daniel Woods
MSc Exploration Geophysics
Module: SOEE5110M
Student ID: 200-630-888

2. WHAT IS 𝑄?
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
1
𝑄eff
=
1
𝑄int
+
1
𝑄app
 Quantifies the progressive loss of amplitude with time.
 Fraction of energy loss per radian is proportional to 𝑄−1 - so the lower the
value of 𝑄, the more attenuative the medium.
 Estimating 𝑄 from surface seismic measures the Effective Quality factor - the
inseparable combination of Intrinsic and Apparent attenuation.

3. 𝑄APP VS 𝑄INT
Intrinsic
 Prominent mechanism is Wave
Induced Fluid Flow.
 Absorption of elastic energy
due to frictional forces.
Müller, T. M., Gurevich, B., & Lebedev, M. (2010). Seismic wave attenuation and dispersion resulting from wave-induced flow in porous rocks—A review. Geophysics, 75(5), 75A147-75A164.
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Apparent
 Frequency dependant effects
such as Scattering.
 Can be determined from well
log information.

4. 𝑄 EFFECT
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
 Loss of amplitude
 Lower dominant
frequency
 Poorer resolution
Preferential loss of
higher frequencies

5. AIMS
1. Test and assess the feasibility of a freely released post-stack 𝑄 estimation
tool, which is named ‘𝑄-est’, on both synthetic and real data.
2. Assess the application of an inverse−𝑄 filter to real data using the estimated
𝑄 fields in order to compensate for attenuation.
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

6. 𝑄-EST METHOD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
1. Start on one trace, calculate
power spectra of two windows
separated by time 𝛿𝑡 (sample
rate) and each of window
length 𝑛.
2.
3.
4.
𝑛
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
𝑛
𝑙𝑛
𝐴2(𝑓
𝐴1(𝑓
= −𝜋
𝛿𝑡
𝑄
𝑓 + ln(𝑅 ∙ 𝐺
𝐴2(𝑓
𝐴1(𝑓
Where R and G are the reflection coefficient and geometrical spreading factor,

7. 𝑄-EST METHOD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
1.
2. Calculate the natural log of the
spectral ratios.
3.
4.
𝑙𝑛
𝐴2(𝑓
𝐴1(𝑓
= −𝜋
𝛿𝑡
𝑄
𝑓 + ln(𝑅 ∙ 𝐺
𝑦 = 𝑚𝑥 + 𝑐
Where R and G are the reflection coefficient and geometrical spreading factor,

8. 𝑄-EST METHOD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
1.
2.
3. Compute the slope between a
specified bandwidth to solve for
𝑄. Output this value at the top
of the second window.
4.
𝑦 = 𝑚𝑥 + 𝑐
𝑙𝑛
𝐴2(𝑓
𝐴1(𝑓
= −𝜋
𝛿𝑡
𝑄
𝑓 + ln(𝑅 ∙ 𝐺
Bandwidth
Slope
Where R and G are the reflection coefficient and geometrical spreading factor,

9. 𝑄-EST METHOD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
1.
2.
3.
4. Repeat for each time sample
and trace.
𝑦 = 𝑚𝑥 + 𝑐
𝑙𝑛
𝐴2(𝑓
𝐴1(𝑓
= −𝜋
𝛿𝑡
𝑄
𝑓 + ln(𝑅 ∙ 𝐺
Bandwidth
Slope
Where R and G are the reflection coefficient and geometrical spreading factor,

10. DATA
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
 Modern long offset 2D data.
 Data were acquired with 8,000 m offsets, shot as
regional cross border lines and recorded 9
seconds of data.
 Batch 1: 18,649 km (Red lines), covers full extent
of survey.
 Processing sequence is consistent and images
are of high-quality broadband across the North
Sea.

11. TESTING SYNTHETIC DATA
Key
a) Original synthetic (no 𝑄
applied)
b) Assumed 𝑄 field
c) Synthetic after forward
modelling for 𝑄
d) Recovered 𝑄 field from
𝑄-est tool
e) 𝑄 compensated image
 Synthetic seismograms were
generated from wells that tied to
the dataset.
 Attempting to recover an assumed
𝑄 field.
 Recovered field shows good
correlation to assumed with
percentage error of 16%.
 Amplitudes were sufficiently
recovered in 𝑄 compensation and
had a correlation coefficient of
0.98 to original synthetic.
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Key
a) Original synthetic (no 𝑄
applied)
b) Assumed 𝑄 field
c) Synthetic after forward
modelling for 𝑄
d) Recovered 𝑄 field from
𝑄-est tool
e) 𝑄 compensated image

12. ESTIMATED 𝑄 FIELDS
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

13. LATERAL CONSISTENCY
 Zoomed in image shows high temporal resolution
 Lateral consistenancy between formation tops is good

14. ERRONEOUS HIGH ANOMALIES
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
 Anomalous high 𝑄 values are
associated with anomalous high
dominant frequencies or
upwards shifts in frequency
content.
 Can be caused by unresolvable
thin beds.
 Results in slope of natural log
of spectral ratios to be very
shallow (High 𝑄) or positive
(Negative 𝑄 – output null
value).
 Resolved by narrowing
bandwidth of which slope is fit
or clipping values down the
High dominant
frequency

15. SHALLOW GAS
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Low dominant
frequency
 Anomalous low 𝑄 values are
associated with anomalous
downwards shifts in frequency
content.
 Causes slope of natural log of
spectral ratios to be relatively high
(low 𝑄).
 Indicates highly attenuating bodies
such as gas reservoirs.
 NMO stretch broadens the wavelet
and lowers the dominant frequency
causing inaccurate low 𝑄 values. This

16. 𝑄 VERSUS OFFSET
 Propogating wave spends
longer in attenuating medium
at farther offsets.
 Stacking procedure can cause
bias in 𝑄 estimates and
‘smeared’ results.
 Magnitude of smearing can be
observed in near, mid and far
stack estimated 𝑄 fields.
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Decreasing 𝑄
estimates

17. 𝑄-COMP – RAW FIELD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

18. 𝑄-COMP - SMOOTHED
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

19. 𝑄-COMP – NO COMPENSATION
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

20. 𝑄-COMP – ESTIMATED FIELD
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

21. 𝑄-COMP – SINGULAR VALUE
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

22. 𝑄-COMP – ESTIMATED FIELD ZOOM
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

23. 𝑄-COMP – SINGULAR VALUE ZOOM
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary

24. 𝑄-COMP – SPECTRA
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
Preferential
boosting of
higher
frequencies
Better
resolution
Lower ambient noise
Flatter spectrum
No compensation
Estimated Q field
Singular value

25. 𝑄-COMP – TRACE
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
No Compensation
Estimated field
compensation
Single value of 110
compensation
Better
resolution
Increasing
amplitudes

26. SUMMARY
Concept
of Q
AIMS Q Est Data Testing
Estimated
Q Fields
Q-comp Summary
 The 𝑄-est tool is simple to implement but requires care in that scaling,
demultiple and NMO have been applied properly.
 Parameterisation is required of the window length and signal bandwidth over
which the slope of the natural log of the spectral ratios is computed.
 Anomalous 𝑄 values can be observed to correlate with the seismic data structure
as well as hydrocarbon reservoirs.
 Using the clipped and smoothed 𝑄 fields in 𝑄 compensation successfully
recovers amplitudes and provides better resolution than using a singular 𝑄 value
of 110 in 𝑄 compensation.

27. ACKNOWLEDGEMENTS

28. OFFSHORE NETHERLANDS GAS

29. Q-COMP – NO COMPENSATION

30. Q-COMP – ESTIMATED FIELD

31. Q-COMP – SINGULAR VALUE

32. Q-COMP - SPECTRA