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Maher S. Maklad

A Brief Overview of Optimal Seismic Resolution. Resolve. Maher S. Maklad. Introduction. 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.

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Maher S. Maklad

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  1. A Brief Overview of Optimal Seismic Resolution Resolve Maher S. Maklad

  2. Introduction • 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. 

  3. Unique Features of Resolve • 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 spectra • Estimated using a proprietary pole-zero modelling technique • 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

  4. Business Impact 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

  5. Resolution Optimization: Motivation • 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”.

  6. Earth Reflectivity Energy Source Seismic Response Noise SEISMIC + * Time Consists of several components: Wavelet Reflectivity Noise Anatomy of Seismic Data = Seismic(t) = Wavelet(t) * Reflectivity(t) + noise(t) Convolutional Model Seismic attribute analysis uses information extracted from the seismic data or its constituents.

  7. 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 Seismic Response Before After Noise Attenuation Observations: Signal-to-Noise Ratio (SNR) is often not stressed. Earth Filter Earth Reflectivity Noise + = * Time

  8. Deconvolution of Noisy Data . Noise Energy Source Earth Reflectivity Seismic response 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. + = * Time Convolutional Model Time Domain:Seismic(t) = Wavelet(t) * Reflectivity(t) + noise(t) Frequency Domain: Seismic(f) = Wavelet(f) x Reflectivity(f) + noise(f)

  9. 0 -5 -10 -15 Magnitude (dB) -20 -25 -30 -35 -40 -45 0 100 200 300 400 500 Frequency (Hz) Resolution Optimization The objectives are: • Improve resolution while controlling noise. To do this we need to: • Estimate the wavelet in the presence of noise • Shape the wavelet according to SNR.. • Preserve the colour of the reflectivity. We should not impose the white reflectivity assumption. Noise Energy Source Earth Reflectivity Seismic response + = * Time Well log generated Reflectivity Spectrum

  10. Before After Resolution Optimization ….results

  11. Peak Frequency • 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 After Resolution Optimization ….validation Resolve has made improvements in the following areas: After Before Before Well log generated Reflectivity Spectrum

  12. A Western Alberta Conglomerate Beach Play: Data Before DeconA series of beach Conglomerates, each capped by a coal sequence. The coals are closely spaced and strong reflectors.

  13. A Western Alberta Conglomerate Beach Play: Data After Decon

  14. 0.0 After -10.0 -20.0 Before -30.0 -40.0 40 60 80 0 20 100 Frequency in Hz A Western Alberta Conglomerate Beach Play: Power Spectra Before and After Decon in dB

  15. Deconvolution of Raw Stacks

  16. Unfiltered, Unscaled Raw Stack

  17. After PC-Filter

  18. After PC-Filter and Resolve

  19. Power Spectra 0 -10 -20 dB Down -30 Raw-Stk PC-Filter Resolve -40 -50 0 40 80 120 160 200 Frequency

  20. Example 2: Raw Stack 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Shot 1420 1430 1440 1450 1460 1470 1480 Shot Time Time Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Figure 3-7

  21. After PC-Filter 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Shot 1420 1430 1440 1450 1460 1470 1480 Shot Time Time Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Figure 3-8

  22. Residuals = Raw – PC-Filtered Data 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Shot 1420 1430 1440 1450 1460 1470 1480 Shot Time Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Figure 3-9

  23. Power & SNR Spectra of Raw and PC-Filtered Data SNR Spectra Raw Stk Power Spectra Raw Stk 30 20 10 0 -10 -20 0 -10 -20 -30 -40 A C D Window TWT (msec) A 590 900 B 570 890 C 670 930 D 573 880 E 573 890 dB E B 0 20 40 60 80 100 0 20 40 60 80 100 Frequency Frequency Power Spectra PC-Filter SNR Spectra PC-Filter 0 -10 -20 -30 -40 40 30 20 10 0 dB 0 20 40 60 80 100 0 20 40 60 80 100 Frequency Frequency Figure 3-10b

  24. After PC-Filter and Resolve 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Shot 1420 1430 1440 1450 1460 1470 1480 Shot Time Time Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Figure 3-11

  25. Processor’s Final Stack 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Shot 1420 1430 1440 1450 1460 1470 1480 Shot Time Time Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Figure 3-12

  26. Post Resolve Analysis 0.0 -10.0 -30.0 Amplitude Spectra from Wavelet Cepstrum 0.8 0.4 -0.4 -0.8 FFT Amplitude dB 0 20 40 80 100 0 10 30 40 Frequency Cepstral Lag 0.0 -10.0 -30.0 -40.0 0.0 -10.0 -30.0 -40.0 Crosspower Spectra Wavelet Spectra After After Before dB dB Before 0 20 40 80 100 0 20 40 80 100 Frequency Frequency Figure 3-13

  27. 8 Bit and Scanned Data

  28. A Land Example : Input Data Shot 400 420 440 460 480 500 520 540 560 Shot 1.3 Time 1.4 1.5 Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace Zone of Interest

  29. After Resolve Shot 400 420 440 460 480 500 520 540 560 Shot Shot 400 420 440 460 480 500 520 540 560 Shot 1.3 Time Time 1.4 1.5 Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace Trace 560 580 600 620 640 660 680 700 720 740 760 780 800 820 840 860 880 900 920 Trace Zone of Interest Stratigraphic trap Structural trap

  30. AMPLITUDE SPECTRUM FROM WAVELET CEPSTRUM Amplitude dB WAVELET SPECTRA After After Before Before dB dB Analysis Before and After Resolve CepstralLag Frequency (Hz) CROSSPOWER SPECTRA Frequency (Hz) Frequency (Hz)

  31. A Marine Example - Input Data Original Processed Volume

  32. After Resolve Original Processed Volume Spectrally Shaped Volume

  33. Spectral displays Before and After Resolve Note: Input data was 8bit filtered and scaled data from workstation After Before 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.

  34. Scanned Data Scanned Data Original

  35. Scanned data after PC-Filter and Resolve After Noise Attenuation and Resolve

  36. Spectral Shaping using Resolve™ Original Processed Volume

  37. Spectral Shaping using Resolve™ Original Processed Volume Spectrally Shaped Volume

  38. 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 Stack A 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).

  39. Filtered Pre-stack Spectral Whitened and FXY Decon

  40. Unfiltered Migrated Stack

  41. Unfiltered Migrated Stack After Resolve

  42. Conclusions • 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 .

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

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