Processing Pitfalls: Astride the Cutting Edge of Technology Part II —Pitfall avoidance Mike Perz, Geo-X Systems Ltd.

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO gather preparation  Deconvolution  Processing works well in many cases

Pitfall avoidance in seismic processing



Not really avoidance, rather detection and escape in the processing world

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO gather preparation  Deconvolution  Processing works well in many cases

Pitfall Grab Bag

 Trimming on a multiple  Scale window encroaching on mute  Geometry errors  Anything ground roll  Velocity picking  Fast interbed multiples  …

Trim statics example:

Offset-dependent maxshift (10ms  0ms)

P M 1700

Offset (m)

200

Trim statics example:

maxshift =10 ms at all offsets

P M 1700

Offset (m)

200

Pitfall Grab Bag

 Trimming on a multiple  Scale window encroaching on mute  Geometry errors  Anything ground roll  Velocity picking  Fast interbed multiples  …

Mean scaling: input (ideal)

Event with AVO 1500 Offset (m ) 0

Mean scaling: gentle mute

Scaling window Event with AVO 1500 Offset (m ) 0

Mean scaling: harsh mute

Scaling window Event with AVO 1500 Offset (m ) 0

Pitfall Grab Bag

 Trimming on a multiple  Scale window encroaching on mute  Geometry errors  Anything ground roll  Velocity picking  Fast interbed multiples  …

Pitfall escape/detection Golden Rule:

 Understand algorithmic assumptions  Recognize degree to which data conform to assumptions

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO gather preparation  Deconvolution  Processing works well in many cases

Time-interval map at target level

N

Far-offset fold at target level

N 18 30

Compare:

time-interval map at target level

N

Pitfall detection guideline:

 Seek spatial correlation between independent data attributes

Aside: Footprint explanation

COFF used for simulating 1-D earth response

2700 Offset (m ) 0

Aside: Footprint explanation

Time slice at target from 1-D earth simulation

N

Time slice near ZOI structure stack

N

Compare:

time slice near ZOI after binbal

N

Pitfall escape guideline #1:

 Be prepared to stray from “accepted” flows Poor offset distribution Reduced S/N Smearing due to bin borrowing

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO gather preparation  Deconvolution  Processing works well in many cases

Results after “AVO-friendly” processing

CMP gathers 0 Offset (m) 1500 Product (Intercept*Gradient) stack

Shot before F-K filtering

0 - 20 K (cycles/1000m) 40 1500 Offset (m ) 0 100

Shot after F-K filtering

0 - 20 K (cycles/1000m) 40 1500 Offset (m ) 0 100

0

Synthetic shot before F-K filtering

Offset (m ) 1500 - 30 0 K (cycles/1000m) 40 100

0

Synthetic shot after F-K filtering

Offset (m ) 1500 - 30 0 K (cycles/1000m) 40 Mute zone 100

F-X noise attenuation: synthetic test

Input CMP Gather Processed CMP Gather AVO 0 Offset (m ) 1500

F-X synthetic test: Plot of peak amplitudes

Result after AVO-unfriendly processing

CMP gathers

(F-X, F-K filtering)

Offset (m) 0 1500 Product (Intercept*Gradient) stack

Compare:

AVO-friendly processing

Offset (m) CMP gathers 0 1500 Product (Intercept*Gradient) stack

Pitfall escape guideline # 1:

 Be prepared to stray from “accepted” flows Amplitude distortion due to noise attenuation Noise in data

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO gather preparation  Deconvolution  Processing works well in many cases

Two N-S lines reveal lateral wavelet instability

Line 40 Line 10

Shot record

Bad shot

Amp spec 0 Autocor 50

Amplitude map, target level

N-S line 40 N-S line 10

Shot-averaged NCCF

N 0.20

0.46

Drift thickness

N 63 m 130 m

Residual shot phase spectra at 13 Hz

N -24 ° 22 °

Pitfall detection guideline:

 Seek spatial correlation between independent data attributes

Troubleshooting strategy: strip down to “Plain Jane” flow Operator length?

Prewhitening?

Zero phase decon?

Conclude: problem lies with decon operator minimum phase estimate

Counterexample: don’t strip down to “Plain Jane” flow Input shots F-K filter 16 Hz Notch filter CMP gather Unreliable min phase estimate?

SC solution indeterminacy problems?

Data don’t fit SC model?

Interaction effects between SC decon and F-K or notch filter?

Stack

Pitfall escape guideline #2:

Use KISS principle when troubleshooting  Strip down flow to “bare bones” then systematically tweak parameters  Saves time in testing  Ensures “apples to apples” comparisons  Prevents confounding of effects

Two inlines after new processing (zero phase decon)

Line 40 Line 10

Compare:

: Two inlines reveal lateral wavelet instability

Line 40 Line 10

Pitfall escape guideline #1:

 Be prepared to stray from “accepted” flows Poor estimates of min phase Failure to remove real earth min phase filters

Introduction

 Pitfall avoidance  Pitfall grab bag  Case studies illustrating pitfall avoidance  Acquisition footprint  AVO processing problem  Deconvolution breakdown  Processing works well in many cases

f

N

“Feel good” example: mining data set

700 800 900 700 800 900

max 

y

20m 130Hz

Lateral resolution: Expanded view of time slice near mine terminus

N 400 m

 dom 4 sin 45  17m

Vertical resolution: tie of N-S line 70 to regional well

Expanded view at mine level

3 m

 dom

REALLY expanded view

Summary

 Pitfall detection  Seek correlation between independent attributes  Question physical validity of processing parameters  Pitfall escape  Keep an open mind to alternative flows  Adhere to KISS principle  Understand assumptions governing each process, degree to which data conform to those assumptions

Acknowledgements

 Encana Corporation  Burlington Resources Canada Ltd.

 Talisman Energy Inc.

 Two anonymous data donors  Ron Weedmark, Mike Pesowski, Ron Larson  Darren Betker, Earl Heather, Monica Martin, Oliver Kuhn, Andrew Royle  Geo-X Systems Ltd.