Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Optimal Seismic Resolution.
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Transcript of Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Optimal Seismic Resolution.
Signal Estimation Technology Inc.
Maher S. Maklad
A Brief Overview ofOptimal Seismic Resolution
Signal Estimation Technology Inc.
Seismic deconvolution aims at estimating a band-limited version of the earth’s reflectivity. This is achieved by compressing the time duration of the wavelet.
In order to make the problem tractable, the reflectivity is commonly assumed to have a white spectrum; an assumption that has been invalidated by many researchers. A lot of research has aimed at compensating for the colour of the reflectivity, mainly using well log information.
The presence of noise further complicates matters. Seismic noise not only make it difficult to visually detect primary reflections, but it is also amplified by wavelet compression filters, setting a limit on how far one can compress the seismic pulse. In practice, a noise attenuation technique such as FX prediction filtering or Radon filtering is called upon to address the noise problem. This adds more implicit assumptions about the constituents of seismic data.
Resolve provides an algorithm for deconvolution of noisy data where the operator is designed based on the estimated signal-to-noise ratio spectra and the wavelet is estimated without white reflectivity assumption. The result is a more geologically faithful data set where the spectrum of the data follows the trend of the spectrum of well log reflectivity without using well logs. This is evidenced by the examples given in this presentation.
Introduction
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Wavelet Amplitude Spectra• Estimated from the estimated signal not directly from the noisy data• No white reflectivity assumption: spectrum of decon data follows the spectrum
of well log reflectivity more closely, thus producing geologically more faithful data
SNR Used to estimate signal spectra Used to shape the input wavelet spectrum leading to
- improved resolution and - controlled noise amplification
Required spectrao Estimated using a proprietary pole-zero modelling techniqueo Very accurate for short time windows
- operator focuses on the zone of interest- option for sliding time operator adapts to changes in spectra with time
Unique Features of Resolve
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Improved resolution with controlled noise amplification• Better detection of geologic features: faults, channels, wedges, etc.• A viable alternative to reprocessing old data• Works well on scanned paper sections
Geologically more faithful data Improved horizon maps and attribute estimation More accurate inversion Improved reservoir characterization More accurate reserve estimation and risk assessment
Business Impact of Resolve
Signal Estimation Technology Inc.
• Your team is under constant pressure to extract the most information from corporate assets as accurately and swiftly as possible.
• This information provides the foundation on which your business makes decisions.
• These decisions are based on a perception of reality. The result of these decisions depends on the accuracy of the perception.
• How to use seismic attributes to enable more informed decisions for the identification, reduction and management of risk while maximizing reward? One answer is to investigate both standard and alternative interpretation workflows available to determine ways of validating and/or improving upon “current practices”.
Resolution Optimization: Motivation
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Anatomy of Seismic Data
= Consists of several components:
SEISMIC
Seismic(t) = Wavelet(t) * Reflectivity(t) + noise(t) Convolutional Model
Seismic attribute analysis uses information extracted from the seismic data or its constituents.
Seismic Response
Tim
e
Energy Source
Wavelet
*
EarthReflectivity
Reflectivity
+
Noise
Noise
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Earth Filter
=
Seismic Response
EarthReflectivity
Noise
+
Tim
e
Noise AttenuationObservations: Signal-to-Noise Ratio (SNR) is often not stressed.
*
Consequences: Horizon time and amplitude maps as well as other seismic attributes leave something to be desired. For example see the impact of removing noise on the following horizon amplitude map..
GCWS_top Amplitude map
Before After
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Tim
e
.EnergySource
=
Seismic response
EarthReflectivity
Noise
+*
Deconvolution attempts to undo the effect of the wavelet.The simple inverse wavelet operator will blow up the noise because the wavelet is band-limited with very high inverse at some frequencies. This prompted the need for sophisticated solutions.
Convolutional Model Time Domain: Seismic(t) = Wavelet(t) * Reflectivity(t) + noise(t) Frequency Domain: Seismic(f) = Wavelet(f) x Reflectivity(f) + noise(f)
Deconvolution of Noisy Data
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Resolution Optimization
EnergySource
=
Seismic response
EarthReflectivity
Noise
+
Tim
e *
The objectives are:
• Improve resolution while controlling noise. To do this we need to:
o Estimate the wavelet in the presence of noise
o Shape the wavelet according to SNR. .
• Preserve the colour of the reflectivity. We should not impose the white reflectivity assumption.
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Well log generated Reflectivity Spectrum
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Resolution Optimization ….resultsBefore After
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Frequency (Hz)
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Resolve has made improvements in the following areas:
Resolution Optimization ….validationPeak Frequency
After
• Increased the bandwidth of the data from ~ 200 Hz to ~ 300 Hz.
• Increased peak frequency of the data from ~ 140 Hz to > 250 Hz.
• Made the spectrum of the data
follow the spectrum of the log generated reflectivity more closely providing confidence in the spectral gains, and enhanced stratigraphic and structural interpretation.
Bandwidth
Before
After
Well log generated Reflectivity Spectrum
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A Western Alberta Conglomerate Beach Play: Data Before Decon
A series of beach Conglomerates, each capped by a coal sequence. The coals are closely spaced and strong reflectors.
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A Western Alberta Conglomerate Beach Play: Data After Decon
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A Western Alberta Conglomerate Beach Play: Power Spectra Before and After Decon in dB
Before
After
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Frequency in Hz
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Deconvolution of Raw Stacks
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Unfiltered, Unscaled Raw Stack
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After PC-Filter
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After PC-Filter and Resolve
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Power Spectra
Raw-Stk PC-Filter Resolve
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Example 2: Raw Stack
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Figure 3-7
Tim
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After PC-Filter
Figure 3-8
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Residuals = Raw – PC-Filtered Data
Figure 3-9
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Power & SNR Spectra of Raw and PC-Filtered Data
Window TWT (msec)A 590 900B 570 890C 670 930D 573 880E 573 890
A
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CD
E
Power Spectra Raw Stk
Power Spectra PC-Filter
SNR SpectraRaw Stk
SNR SpectraPC-Filter
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Figure 3-10b
Signal Estimation Technology Inc.Figure 3-11
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After PC-Filter and ResolveT
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Processor’s Final Stack
Figure 3-12
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Wavelet Spectra
Cepstral Lag
FFT
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Crosspower Spectra
Frequency
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plitu
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Post Resolve AnalysisAmplitude Spectra from WaveletCepstrum
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Figure 3-13
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8 Bit and Scanned Data
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A Land Example : Input DataT
ime
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Zone of Interest
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After Resolve
Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace
Zone of InterestStratigraphic trap Structural trap
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After
After
Before Before
CEPSTRUM AMPLITUDE SPECTRUM FROM WAVELET
WAVELET SPECTRA
Am
plit
ude
dB
dB
dB
Analysis Before and After Resolve
Cepstral Lag Frequency (Hz)
CROSSPOWER SPECTRA
Frequency (Hz)Frequency (Hz)
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Original Processed Volume
A Marine Example - Input Data
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Original Processed Volume
Spectrally Shaped Volume
After Resolve
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Note: Input data was 8bit filtered and scaled data from workstationSpectral displays Before and After Resolve
Note: After post-stack spectral shaping the dominant frequency of the data has increased by ~ 40 Hz and the bandwidth has increased by ~20 Hz.
Before After
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Scanned Data
Scanned Data
Original
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Scanned data after PC-Filter and Resolve
After Noise Attenuation and Resolve
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Spectral Shaping using Resolve™
Original Processed Volume
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Spectrally Shaped Volume
Spectral Shaping using Resolve™
Original Processed Volume
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Impact of Resolve on Horizon Maps
Here we have 3 versions of the same data Filtered pre-stack spectral whitened and FXY Decon Unfiltered Migrated Stack Resolve Applied to Unfiltered Migrated StackA horizon map was extracted from each volume and displayed
underneath the corresponding seismic. All maps show a channel. The extent of the channel is largest for the first version, smaller for the second and smallest for the Resolve version. The map generated from Resolve is more accurate due to the improved resolution (sharper events) and the geologically faithful image (no white reflectivity assumption used).
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Filtered Pre-stack Spectral Whitened and FXY Decon
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Unfiltered Migrated Stack
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Unfiltered Migrated Stack After Resolve
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• Resolve improves the resolution of seismic data without amplification of noise (i.e. constrained by SNR).
• No white reflectivity assumption leading a better spectral representation of earth reflectivity.
• The attributes estimated after applying Resolve are noise-resistant and more geologically faithful for improved reservoir characterization.
• More accurate interpretation of horizons and faults.• A viable alternative to reprocessing old data.• Effective for scanned 8-bit data
Conclusions
.
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