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

Signal Estimation Technology Inc.

Maher S. Maklad

A Brief Overview ofRobust Multi-channel Seismic Inversion


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.).


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 neededMulti-input Inversion for AVO/AVA worko Simultaneous inversion of angle volumes or offset volumeso 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


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


Inversion & Data Compression

Reflect

Trace 32bits/sample

wavelet 32 bits/sample

32 bits/sample

1 bit/samplereflectivity

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.


Synthetics with dipoles, medium noise

Co

mp

ress

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teri

on

Phase °Synthetic-

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10100

10125

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10225

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10275

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10325

10350

10375

10400

Estimated Truehase

Reflectivity

Phase Estimation Using Data Compression:Best wavelet phase results in best compression.


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,


Noisy Wedge Model 1

Synthetics

Estimated Reflectivity


Noisy Wedge Model 2

Estimated Reflectivity

Synthetics


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.


8-bit Data:Seismic After DeconPerseus-1

1740.ppt-13Note : data exported from landmark database (ie. 8 bit, filtered, scaled, migration)

Tim

e 2.0

2.1

2.2

1.9


Xline 1740 Relative Impedance : Perseus Gas FieldInversion: Non-Linear Optimisation (independent of well and horizon information): 40 degree phase rotation

Perseus 1

Logged Impedance

Tim

e

Note : data exported from landmark database (ie. 8 bit, filtered, scaled, migration) 1740.ppt-12

Aptian Discon.

Main Uncon..

Athol Formation..

Bulk Shift < 10 msec

Toolonga Calcilutite

Bathonian Sands..

2.0

2.1

2.2

1.9

2.0


Inversion of 8 bit Data

Logged Impedance

Tim

e

Aptian Discon.

Main Uncon..

Athol Formation..

Bulk Shift < 10 msec

Bathonian Sands..

2.0

2.1

2.2

1.9

2.0

Perseus 1


Deep Water Example

Spectrally Shaped Volume

Pseudo Acoustic Impedance Volume with Low Frequency

Trend

Reflectivity Volume

Pseudo Acoustic Impedance Volume




Trend

Reflectivity Volume


Deep Water Example




Trend

Reflectivity Volume


Deep Water Example




Trend

Reflectivity Volume


Deep Water Example


SET’s Approach to AVO

1. Estimate wavelet for each stack (angle or offset)

2. Equalize wavelets

3. Perform simultaneous inversion [R(q) or R(x)]

4. Use R(.) or R(.) as attributes or

5. Invert R(q)into elastic properties reflectivity

6. Generate relative change volumes

7. Calibrate and add trend


Elastic Impedance Processing Sequence

Wavelet Estimation

Wavelet Estimation

Wavelet Estimation

Partial Stack 1 Partial Stack NPartial Stack 2

Wavelet compensatedPartial Stack 1



Robust MultichannelInversion

Elastic Impedance fromPartial Stack 1

Elastic Impedance fromPartial Stack 2

Elastic Impedance fromPartial Stack N

ReferenceWavelet


Elastic Impedance Inversion

H1

H2

H3H4

H3H4

H2

H1


3D Xplot: Multi-Offset Impedances

High Permeability


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. 22nd, 2004


LimestoneTop of

N-VE Porosity RC

Bottom ofLimest

Producing

Top ofLimestone

No Porosity No Positive RCsBottom ofLimestone

Dry

Estimated reflectivity superimposed on seismic data

►Positive RC < Negative RC


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


Line A – Original Data And Interpeted Picks16-14-036-10W4

Sparky Coal - OriginalBase Channel - OriginalWabamun - Original


Line A – Reflectivity on Wiggle Seismic

Sparky Coal - Inversion Potential Porosity - Inversion

16-14-036-10W4

Channel - Inversion Wabamun - Inversion+ RC

- RC


Line A – Estimated Pseudo Acoustic Impedance (AI)

Low High

16-14-036-10W4

Sparky Coal - Inversion Potential Porosity - Inversion

Channel - Inversion Wabamun - Inversion


Line b – Original Data And Interpeted Picks

Coal - OriginalBase Channel - OriginalCarbonate - Original


Reflectivity (Colour) with Spectrally Shaped Wiggle Traces

Coal - Inversion Potential Porosity Inversion

Channel - InversionCarbonate - Inversion

+ RC

- RC


Estimated Pseudo Acoustic Impedance

Low High

Coal - Inversion Potential Porosity - InversionChannel - Inversion Carbonate - Inversion


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 290-308 and 344-355.

u Wavelet estimation was performed using seismic alone (S-Pulse).

u .Wavelet validation in Reflect resulted in phase corrections less than 150.

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.


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.

uLow frequency may overwhelm interesting impedance changes.

uAdvisable 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.

uThe next two slides illustrate wgat happen if you include a bad log in the inversion.


Model-based Inversion with corrupt well log

Original objective: Estimate Impedance from 3D seismic volume using model based inversion.

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.


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


Reflect vs. Model-Based Inversion

Seismic Cube



Estimated Reflectivity - Reflect


1.97

1.160.800.390.10-0.25-0.60

-1.00

-1.70

-2.70


Relative Impedance - Reflect



Impedance – Model-Based


Seismic

Residual

Reflect vs. Model-Based InversionModel-Based Reflect

Above: Residuals = Seismic - Model

Above: Spectra of seismic and resiuals

Seismic

Residual


Automatic detection of the number of RC using data compression Data compression insures robustness against random noise. Multi-channel inversion provides additional noise resistance. Phase validation using data compression provides accurate wavelet without

using well lugs. Multi-input option for AVO and elastic impedance applications. Weighted multi-input option for robustness against coherent noise. Wavelet effect is removed allowing for 4-D applications. Discriminates between real events and artifacts. More accurate horizon definition, both amplitude and time.

Conclusions


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 neededMulti-input Inversion for AVO/AVA worko Simultaneous inversion of angle volumes or offset volumeso 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


Powerful Scientific Tools for all Phases of the Life Cycle of

your Assets

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