Evaluation of the Sensitivity of Seismic Inversion Algorithms to Different St...
FinalPresentation_200630888 (1)
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.
Not actually new algorithm, created 20 years by some chaps working for Amoco, but it’s been freely released and now aiming to incorporate this into the PRIMA software.
Using surface seismic to quantify attenuation has a drawback in a thinly layered Earth in that it cannot distinguish between 𝑄 int , the intrinsic quality factor describing anelastic absorption, and 𝑄 app , the apparent quality factor describing frequency-dependant attenuation effects such as scattering.
There are many mechanisms that influence attenuation such as mechanical compression, grain boundary friction and bubble compression. However, the most dominant attenuation mechanism in porous media is that of wave-induced fluid flow. The most prominent contributer to Qeff is often unknown. Therefore this can be a problem if using Qe as a seismic attribute ie pore fluid characterisation
So areas under highly attenuating bodies such as gas reservoirs, will have lower frequencies and amplitudes. If we can quantify the magnitude of attenuation – what does it tell us about the subsurface (relate to previous slide ie cause) and how can we compensate or it
By doing this, TGS hope to incorporate the 𝑄 eff estimation tool into their proprietary processing software and utilise it in their processing flows.
Simple to use with no special processing required and few parameters required for input. Estimates effective seismic Q
Employs an algorithm based on a method of spectral ratios.
Ratios are computed one sample at a time using a continuously time-variant DFT (Discrete Fourier Transform).
Simple to use with no special processing required and few parameters required for input.
Important that data have not already been Q compensated.
Simple to use with no special processing required and few parameters required for input. Estimates effective seismic Q
Employs an algorithm based on a method of spectral ratios.
Ratios are computed one sample at a time using a continuously time-variant DFT (Discrete Fourier Transform).
Simple to use with no special processing required and few parameters required for input.
Important that data have not already been Q compensated.
Simple to use with no special processing required and few parameters required for input. Estimates effective seismic Q
Employs an algorithm based on a method of spectral ratios.
Ratios are computed one sample at a time using a continuously time-variant DFT (Discrete Fourier Transform).
Simple to use with no special processing required and few parameters required for input.
Important that data have not already been Q compensated.
Simple to use with no special processing required and few parameters required for input. Estimates effective seismic Q
Employs an algorithm based on a method of spectral ratios.
Ratios are computed one sample at a time using a continuously time-variant DFT (Discrete Fourier Transform).
Simple to use with no special processing required and few parameters required for input.
Important that data have not already been Q compensated.
Still good to show testing even if it didn’t work. Can always have 20 major slides then loads more prepared at the end of your presentation ready to go into more details if questions are asked
