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Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Robust Multi-channel Seismic Inversion.

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Presentation on theme: "Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Robust Multi-channel Seismic Inversion."— Presentation transcript:

1 Signal Estimation Technology Inc. Maher S. Maklad A Brief Overview of Robust Multi-channel Seismic Inversion

2 Signal Estimation Technology Inc. Introduction Unlike other inversion techniques, Reflect does not require well or horizon information, It produces a unique reflectivity model by optimally estimating the locations, amplitudes and number of reflection coefficients, consistent with an input wavelet. This feature is particularly useful in areas where well control is sparse and/or the interpretation is complex. Since neither well data nor horizon information were used in the estimation of reflectivity and pseudo-impedance, any available well data or horizon data can be used to validate the quality of the inversion or integrity of the well data or interpretation. Combined with other attributes this inversion can assist with the understanding of reservoir seal pair geometries. Since no initial model is needed, Reflect can distinguish between true and apparent seismic events; a problem encountered in many situations (porosity or tuning? Shale plugs? Etc.).

3 Signal Estimation Technology Inc. Wavelet estimation Using seismic data only (S-Pulse) Using seismic and well log data (W-Pulse) Phase validation ising data compression in Reflect Detecting reflection coeffucients  Spikes are located to maximize compression in the model: synthetics plus residuals  Improves immunity to random noise which cannot be compressed by wavelet.  Multi-channel detection for immunity to random noise and events continuity.  Number of RC is reached when compression is maximized. No initial model, horizon or well log information needed Multi-input Inversion for AVO/AVA work o Simultaneous inversion of angle volumes or offset volumes o Each volume can be given a different weight for robustness against coherent noise. Quality control features  Residual cubes, energy maps and box plots are provided tocheck the quality and consistency of the results. Unique Features of Reflect

4 Signal Estimation Technology Inc. No initial model, horizon or well log information needed Useful for deep water and frontier work. Resolve source of seismic anomalies (porosity or side-lobe, shale plug, etc.). Use well logs to validate inversion. Use inversion to check integrity of well data and horizon picking. Wavelet phase validation without log Super sharp images. 4D applications. Detect lamination & TOC in shale plays. Azimuth data inversion without well logs. Multi-input Inversion for AVO/AVA work Business Impact of Reflect

5 Signal Estimation Technology Inc. Inversion & Data Compression Reflect Trace 32bits/sample wavelet 32 bits/sample 32 bits/sample 1 bit/sample reflectivity residuals q bits /sample + * Reflect decodes seismic into a a wavelet, a reflectivity sequence and a residual sequence in order to minimize the number of bits in reflectivity and residuals. If the wrong wavelet is used, dispersion will result in a higher number of RC’s than necessary and hence, less compression. Thus, the best wavelet will produce the best compression.. The number of reflection coefficients is automatically determined by maximizing compression. Furthermore, no initial model is used making it possible to resolve difficult interpretation problems (e.g. the presence or absence of shale plugs; whether an event is real or not) that cannot be solved by other methods. Notice that random noise cannot be compressed by the wavelet. Thus, this scheme is robust to random noise.

6 Signal Estimation Technology Inc. Synthetics with dipoles, medium noise Compression Criterion Phase  Synthetic EstimatedTruehase Reflectivity Phase Estimation Using Data Compression: Best wavelet phase results in best compression.

7 Signal Estimation Technology Inc. Wedge Model Example Two blind synthetic models were provided by a client. We estimated the wavelet used to invert the models using seismic data only (S-Pulse). The next two slides show the inversion on the two wedge models with 30% noise. Notice that the multi-channel inversion handled the noise effectively. In fact one can argue that the inverted data is cleaner than the input data! This is due to the compression criterion (noise cannot be compressed) and the multi- channel nature of the algorithm,

8 Signal Estimation Technology Inc. Noisy Wedge Model 1 Synthetics Estimated Reflectivity

9 Signal Estimation Technology Inc. Noisy Wedge Model 2 Estimated Reflectivity Synthetics

10 Signal Estimation Technology Inc. Inversion of 8-bit data The inversion of the 8 bit data set was done without using any well information including the estimation of the wavelet. The only use of well logs is its overlay on the inversion for comparison. We can clearly see that the result substantially agrees with the well log. The inversion of the 8 bit data set was done without using any well information including the estimation of the wavelet. The only use of well logs is its overlay on the inversion for comparison. We can clearly see that the result substantially agrees with the well log.

11 Signal Estimation Technology Inc. 8-bit Data: Seismic After Decon Perseus ppt-13 Note : data exported from landmark database (ie. 8 bit, filtered, scaled, migration) Time

12 Signal Estimation Technology Inc.

13 Inversion of 8 bit Data Logged Impedance Time Aptian Discon. Main Uncon.. Athol Formation.. Bulk Shift < 10 msec Bathonian Sands Perseus 1

14 Signal Estimation Technology Inc. Deep Water Example Spectrally Shaped Volume Pseudo Acoustic Impedance Volume with Low Frequency Trend Reflectivity Volume Pseudo Acoustic Impedance Volume

15 Signal Estimation Technology Inc. Spectrally Shaped Volume Pseudo Acoustic Impedance Volume with Low Frequency Trend Reflectivity Volume Pseudo Acoustic Impedance Volume Deep Water Example

16 Signal Estimation Technology Inc. Spectrally Shaped Volume Pseudo Acoustic Impedance Volume with Low Frequency Trend Reflectivity Volume Pseudo Acoustic Impedance Volume Deep Water Example

17 Signal Estimation Technology Inc. Spectrally Shaped Volume Pseudo Acoustic Impedance Volume with Low Frequency Trend Reflectivity Volume Pseudo Acoustic Impedance Volume Deep Water Example

18 Signal Estimation Technology Inc. SET’s Approach to AVO 1. Estimate wavelet for each stack (angle or offset) 2. Equalize wavelets 3. Perform simultaneous inversion [R(  ) or R(x)] 4. Use R(.) or R(.) as attributes or 5. Invert R(  )into elastic properties reflectivity 6. Generate relative change volumes 7. Calibrate and add trend

19 Signal Estimation Technology Inc. Elastic Impedance Processing Sequence WaveletEstimationWaveletEstimationWaveletEstimation Partial Stack 1Partial Stack NPartial Stack 2 Wavelet compensated Partial Stack 1 Wavelet compensated Partial Stack 2 Wavelet compensated Partial Stack 3 Robust Multichannel Inversion Elastic Impedance from Partial Stack 1 Elastic Impedance from Partial Stack 2 Elastic Impedance from Partial Stack N Reference Wavelet

20 Signal Estimation Technology Inc. Elastic Impedance Inversion H1 H2 H3 H4 H3 H4 H2 H1

21 Signal Estimation Technology Inc. 3D Xplot: Multi-Offset Impedances High Permeability

22 Signal Estimation Technology Inc. Porosity or Tuning? In this area, gas is found in dolomitized pockets within limestone. The limestone layer is encased in shale. Therefore, the seismic character of porosity is a trough following the peak associated with the shale/limestone interface. Unfortunately, such troughs may also arise due to tuning effects, particularly when the thickness of the limestone layer changes laterally. The inversion results show a negative reflection coefficient below the top of the limestone due to porosity at the producing well. No negative reflection coefficient is seen below the top of the limestone at the dry hole. This is an example where model-based techniques are not useful. Apr. 22 nd, 2004

23 Signal Estimation Technology Inc. Limestone Top of N-VE Porosity RC Bottom of Limest Producing Top of Limestone No Porosity No Positive RCs Bottom of Limestone Dry Estimated reflectivity superimposed on seismic data ►Positive RC < Negative RC

24 Signal Estimation Technology Inc. Two wells were drilled on two lines over the Coronation Trend in the Provost area. One well was a very good producer while the other was a tighter sand. The objective of this project is to undertake an analysis to see whether it is possible to seismically differentiate between the producing well and the dry hole. The play is a Rex sandstone channel. The next 6 slides illustrate the inversion of the 2 lines using Reflect starting with the good well. Western Canada Example

25 Signal Estimation Technology Inc. Line A – Original Data And Interpeted Picks W4 Sparky Coal - Original Base Channel - Original Wabamun - Original

26 Signal Estimation Technology Inc. Line A – Reflectivity on Wiggle Seismic Sparky Coal - InversionPotential Porosity - Inversion W4 Channel - InversionWabamun - Inversion + RC - RC

27 Signal Estimation Technology Inc. Line A – Estimated Pseudo Acoustic Impedance (AI) Low High W4 Sparky Coal - InversionPotential Porosity - Inversion Channel - Inversion Wabamun - Inversion

28 Signal Estimation Technology Inc. Line b – Original Data And Interpeted Picks Coal - Original Base Channel - Original Carbonate - Original

29 Signal Estimation Technology Inc. Reflectivity (Colour) with Spectrally Shaped Wiggle Traces Coal - Inversion Potential Porosity Inversion Channel - Inversion Carbonate - Inversion + RC - RC

30 Signal Estimation Technology Inc. Estimated Pseudo Acoustic Impedance Low High Coal - Inversion Potential Porosity - Inversion Channel - Inversion Carbonate - Inversion

31 Signal Estimation Technology Inc. Discussion of the inversion results u Inversion shows distinct patterns over the producing well and the dry well: At the producing well a strong negative shows the lower porosity. No negative RC is seen at the dry hole. There appear to be minor suggestions of the upper porosity on the acoustic impedances. u The porous channel on line is clearly mapped. u On the dry hole line, the inversion provided more accurate interpretation of the channels and detected 2 potentially porous channels from SPs and u Wavelet estimation was performed using seismic alone (S-Pulse). u.Wavelet validation in Reflect resulted in phase corrections less than u The phase-corrected wavelets were then used in the inversion. u Wavelets estimated using both seismic and well logs (W-Pulse) matxhed the validated wavelets very well.

32 Signal Estimation Technology Inc. Reflect and Seismic-Guided Well Logs Interpolation Although it does not use well logs, Reflect results usually matches available logs well. Poor match signals either poor well log or the presence of coherent noise in the seismic at such well location. Model-based inversion cannot resolve subtle seismic interpretation problems. Model-based inversion echoes your bias through the intial model. Error in logs propagate to inversion. Need to do cross validation, removing one well at a time and exclude wells with poor match when not included in the inversion. u Low frequency may overwhelm interesting impedance changes. u Advisable to use Reflect first: it often provides you with the answer. It provides a good initial model and allows to uncover problems with logs or seismic. u The next two slides illustrate wgat happen if you include a bad log in the inversion.

33 Signal Estimation Technology Inc. Model-based Inversion with corrupt well log Original objective: Estimate Impedance from 3D seismic volume using model based inversion. This low impedance around well d838 did not show in Reflect inversion prompting the need to examine the logs at this well. A model was buit from sonic and gamma ray logs to predict density. Obviously, the well in question was not used in model building.Are There was a large error between the predicted and measured density at this well. This low impedance around well d838 did not show in Reflect inversion prompting the need to examine the logs at this well. A model was buit from sonic and gamma ray logs to predict density. Obviously, the well in question was not used in model building.Are There was a large error between the predicted and measured density at this well.

34 Signal Estimation Technology Inc. Revised Inversion (without d838 well) Revised Inversion (without d838 well) Original Inversion (with d838 well) Original Inversion (with d838 well) Model-based Inversion with corrupt well log

35 Signal Estimation Technology Inc. Reflect vs. Model-Based Inversion Seismic Cube

36 Signal Estimation Technology Inc. Reflect vs. Model-Based Inversion Estimated Reflectivity - Reflect

37 Signal Estimation Technology Inc Reflect vs. Model-Based Inversion Relative Impedance - Reflect

38 Signal Estimation Technology Inc. Reflect vs. Model-Based Inversion Impedance – Model-Based

39 Signal Estimation Technology Inc. Seismic Residual Reflect vs. Model-Based Inversion Model-BasedReflect Above: Residuals = Seismic - Model Above: Spectra of seismic and resiuals Seismic Residual

40 Signal Estimation Technology Inc. n Automatic detection of the number of RC using data compression n Data compression insures robustness against random noise. n Multi-channel inversion provides additional noise resistance. n Phase validation using data compression provides accurate wavelet without using well lugs. n Multi-input option for AVO and elastic impedance applications. n Weighted multi-input option for robustness against coherent noise. n Wavelet effect is removed allowing for 4-D applications. n Discriminates between real events and artifacts. n More accurate horizon definition, both amplitude and time. Conclusions

41 Signal Estimation Technology Inc. Compression Each wavelet is replaced by a reflection coefficient and the resulting residuals Spikes are located to maximize compression in the model: synthetics plus residuals. Number of RC is reached when compression is maximized. Best wavelet produces the most compression, thus the phase of the wavelet can be estimated. Improves immunity to random noise which cannot be compressed by wavelet. Multi-channel algorithm  Improves immunity to random noise and events continuity. No initial model, horizon or well log information needed Multi-input Inversion for AVO/AVA work o Simultaneous inversion of angle volumes or offset volumes o Each volume can be given a different weight for robustness against coherent noise. Quality control features  Residual cubes, energy maps and box plots are provided tocheck the quality and consistency of the results. Unique Features of Reflect

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


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