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


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


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


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


• 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


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


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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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


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


Resolution Optimization ….resultsBefore After


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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


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.


A Western Alberta Conglomerate Beach Play: Data After Decon


A Western Alberta Conglomerate Beach Play: Power Spectra Before and After Decon in dB

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After

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Deconvolution of Raw Stacks


Unfiltered, Unscaled Raw Stack


After PC-Filter


After PC-Filter and Resolve


Power Spectra

Raw-Stk PC-Filter Resolve

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Example 2: Raw Stack

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Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace

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Figure 3-7

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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

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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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Crosspower Spectra

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Post Resolve AnalysisAmplitude Spectra from WaveletCepstrum

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Figure 3-13


8 Bit and Scanned Data


A Land Example : Input DataT

ime

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Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace

Zone of Interest


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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Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace

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Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace


After

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Before Before

CEPSTRUM AMPLITUDE SPECTRUM FROM WAVELET

WAVELET SPECTRA

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Analysis Before and After Resolve

Cepstral Lag Frequency (Hz)

CROSSPOWER SPECTRA

Frequency (Hz)Frequency (Hz)


Original Processed Volume

A Marine Example - Input Data



Spectrally Shaped Volume

After Resolve


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


Scanned Data

Scanned Data

Original


Scanned data after PC-Filter and Resolve

After Noise Attenuation and Resolve


Spectral Shaping using Resolve™



Spectrally Shaped Volume

Spectral Shaping using Resolve™



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).


Filtered Pre-stack Spectral Whitened and FXY Decon


Unfiltered Migrated Stack


Unfiltered Migrated Stack After Resolve


• 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

.


Powerful Scientific Tools for all Phases of the Life Cycle of your Assets

signal estimation technology inc. maher s. maklad a brief overview of optimal seismic resolution

Documents

signal estimation technology

signal spectra

estimated signal

seismic noise

constituents of seismic

log reflectivity

noise problem

white spectrum