1. Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Optimal Seismic Resolution

2. 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

3. Signal Estimation Technology Inc. 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 o Estimated using a proprietary pole-zero modelling technique o 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

4. Signal Estimation Technology Inc. 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

5. 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

6. Signal Estimation Technology Inc. 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 Time Energy Source Wavelet * Earth Reflectivity + Noise

7. Signal Estimation Technology Inc. Earth Filter = Seismic Response Earth Reflectivity Noise + Time Noise Attenuation Observations: 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

8. Signal Estimation Technology Inc. Time. Energy Source = Seismic response Earth Reflectivity 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

9. Signal Estimation Technology Inc. Resolution Optimization Energy Source = Seismic response Earth Reflectivity Noise + Time * 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. Well log generated Reflectivity Spectrum

10. Signal Estimation Technology Inc. Resolution Optimization ….results Before After

11. Signal Estimation Technology Inc. Before Resolve has made improvements in the following areas: Resolution Optimization ….validation Peak 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

12. Signal Estimation Technology Inc. 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.

13. Signal Estimation Technology Inc. A Western Alberta Conglomerate Beach Play: Data After Decon

14. Signal Estimation Technology Inc. A Western Alberta Conglomerate Beach Play: Power Spectra Before and After Decon in dB Before After 20100 0.0 -10.0 -20.0 -30.0 -40.0 0 406080 Frequency in Hz

15. Signal Estimation Technology Inc. Deconvolution of Raw Stacks

16. Signal Estimation Technology Inc. Unfiltered, Unscaled Raw Stack

17. Signal Estimation Technology Inc. After PC-Filter

18. Signal Estimation Technology Inc. After PC-Filter and Resolve

19. Signal Estimation Technology Inc. Power Spectra Raw-StkPC-FilterResolve 04080120160200 0 -10 -20 -30 -40 -50 Frequency dB Down

20. Signal Estimation Technology Inc. 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 1480Shot Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Tim e Figure 3-7 Time

21. Signal Estimation Technology Inc. After PC-Filter Figure 3-8 Tim e 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 1480Shot Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace

22. Signal Estimation Technology Inc. Residuals = Raw – PC-Filtered Data Figure 3-9 0.5 0.6 0.7 0.8 0.9 1.0 0.5 0.6 0.7 0.8 0.9 1.0 Time 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 1480Shot Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace

23. Signal Estimation Technology Inc. Power & SNR Spectra of Raw and PC-Filtered Data WindowTWT (msec) A590 900 B570 890 C670 930 D573 880 E573 890 A B C D E Power SpectraRaw Stk Power SpectraPC-Filter SNR SpectraRaw Stk SNR SpectraPC-Filter 0 -10 -20 -30 -40 0 20 40 60 80 100 Frequency 0 -10 -20 -30 -40 0 20 40 60 80 100 30 20 10 0 -10 -20 Frequency 0 20 40 60 80 100 Frequency 0 20 40 60 80 100 Frequency dB 40 30 20 10 0 Figure 3-10b

24. Signal Estimation Technology Inc. Figure 3-11 Tim e 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 1480Shot Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace After PC-Filter and Resolve Time

25. Signal Estimation Technology Inc. Processor’s Final Stack Figure 3-12 Tim e 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 1480Shot Trace 2310 2330 2350 2370 2390 2410 2430 2450 Trace Time

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

27. Signal Estimation Technology Inc. 8 Bit and Scanned Data

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

30. Signal Estimation Technology Inc. After Before CEPSTRUM AMPLITUDE SPECTRUM FROM WAVELET WAVELET SPECTRA Amplitude dB Analysis Before and After Resolve Cepstral Lag Frequency (Hz) CROSSPOWER SPECTRA Frequency (Hz)

31. Signal Estimation Technology Inc. Original Processed Volume A Marine Example - Input Data

32. Signal Estimation Technology Inc. Original Processed Volume Spectrally Shaped Volume After Resolve

33. Signal Estimation Technology Inc. Note: Input data was 8bit filtered and scaled data from workstation Spectral 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

34. Signal Estimation Technology Inc. Scanned Data Original

35. Signal Estimation Technology Inc. Scanned data after PC-Filter and Resolve After Noise Attenuation and Resolve

36. Signal Estimation Technology Inc. Spectral Shaping using Resolve™ Original Processed Volume

37. Signal Estimation Technology Inc. Spectrally Shaped Volume Spectral Shaping using Resolve™ Original Processed Volume

38. Signal Estimation Technology Inc. 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. Signal Estimation Technology Inc. Filtered Pre-stack Spectral Whitened and FXY Decon

40. Signal Estimation Technology Inc. Unfiltered Migrated Stack

41. Signal Estimation Technology Inc. Unfiltered Migrated Stack After Resolve

42. Signal Estimation Technology Inc. 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.

43. Signal Estimation Technology Inc. Powerful Scientific Tools for all Phases of the Life Cycle of your Assets