Talk6 W5 Zwaan

Shell Exploration & Production

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Avo Inversion and Processing:

Dedication and Integration

Marcel Zwaan, Yvan Charreyron, Dave Bateman

Shell E&P Europe


Acknowledgments

Exxon-Mobil and Shell E&P

Greg Hester, Richard Shipp, Paul Pillai, Alexander Sementsov (Fugro-Jason)

and Peter Ashton


Signal processing

Psdm initial velocity model

Psdm Prio cube

Velocity model

updating

FinalVolume

migration

AI/EI/ AVOInversion

DiscoverNeed for

Further processing

A project is a series of “throwing over the fence exercises”

petrophysics

The “Problem”


Several inversion projects took much longer than planned and AVO inversions did not deliver to expectations

• Noise levels too high (noise multiples)

• Data did not obey expected amplitude versus offset trends

• Fluid/Litho discrimination impossible in practice

• long unexpected post processing projects in between inversion steps

• Petrophysical logs not suitable for inversion – e.g. V-shale only presentover reservoir, etc…..

The Background


Pre-processing

Psdm initial velocity model

Psdm Prio cube

Velocity model

updating

FinalVolume

migration

AI/EI/ AVOInversion

Inversion Feasibility

Identify Post-processing

steps

Project integration is enabled By a feasibility step in parallel to the depth migration cycle

Petrophysics(QC)

The “Solution”


• The Penguin Field

• Pre-Stack Diagnostics

• Post Stack Diagnostics

• Inversion impact

• Conclusions

Overview


Some “Common” Words

• Pre-stack data: Common Image Gather (from pre-stack depth migration)

• Post stack data: Sub-stacks – near mid and far angle stacks

• Angle : Angle of incidence

• Inversion: AVO inversion

• Amplitude: Reflection amplitude (“event” amplitude)

• Impedance: P-Impedance and S-impedance (density times p-velocity and density times s-velocity, resp.)


Discovered 1974 - Sub Sea tie-back to Brent-C - 65 km Production Flow-line

PenguinsCluster

Brent Charlie

The Penguin Field Some facts


Penguin - Strategraphy and Main Inversion ObjectivePenguin A

Jurassic intra-Kimmeridge Magnus sands

Penguin BTriassic sands

Penguin CDEclassical middle JurassicBrent reservoir sequence

FluidsPenguin C,D - light oilPenguin E - gas condensates

• Average reservoir sand thickness:130 and 225 ft, • average porosity is 15% • average Net-to-Gross ratio around 75%. • Productive intervals: Etive, Rannoch sequence

• main scope of the inversion project - Penguin CDE –Brent sequence only

Base Heather Unconformity

Base Brent Unconformity


Get the expectations right:

Are Fluid and Litho discriminations possible?


SST versus Shale discrimination possible

Vshale logsFrom top Brent To TD

Vshale coloured – Versus P-Impedance and S-Impedance

Vshale logsFrom top Brent Top Dunlin

Shale Volume X-Plots

IP

IS

IP

IS


Logs with synthetics (Aki-Richards) Synthetic: Left brine – Right gas condensate

Brine and Hydrocarbon synthetics

brine Gas condensate

Offsets: 300 – 3000 m

P-Sonic Density S-Sonic Poisson’s P-Son_chk


• Sand versus shale discrimination possible

• No Fluid discrimination feasible


Wavelet estimation and near far stack alignment

Aim: check the alignment of the data


Left: Near Right: far (Near and far wavelets estimated)Near match good – far match: misalignment – spectral balancing needed

near stack far stack

Seismic to well tie – and Frequency contents

Blue: syntheticRed: trace at well location

P-Sonic – S-Sonic - Density P-Sonic – S-Sonic - Density


Conclusion:

Near (mid) and far stacks need to be alignedAnd the spectra need to be balanced to a common reference (mid stack)


Angle versus offsets

How to compute the angle versus offsets

QC of angle versus offsets with angle versus offset in the well


Colour: Angle – every colour is an angle slot of 3 degrees.Angle versus offset functions at well location compared with angle vs offset relation from P-Sonic logAngles behave linear with offset

offset

tim

e

Angle valuesColour

6

12

18

24

25

36

Angle versus offset Gathers


Relation between angle and offset

twtrmsTV

VOffset2

int)sin(

ii

iii

rms t

tVV

2int,

2

22

0rms

twt V

OffsetTt

Compute angles versus offset by the Walden approximation:

(A.T. Walden: Geophysical Prospecting 39, pp. 915 942 1991)

with

and

Snell’s law:

X

Ttwt, Vrms

Vint

rmsVV

)sin()sin(

int

twtrmsTV

X2)sin(


Pre- and Post stack diagnostics


The aim of these diagnostics is to assess whether the data– be it pre stack data or near mid and far sub-stacks –satisfy the two term AVO behaviour:

Where L is the intercept and M is the Gradient.

2sin( MLA

Does the data obey the classical Aki and RichardsAmplitude versus offset trend?

(cf. “Making AVO Sections More Robust” by Andrew Walden, BP, 52nd EAGE Meeting Copenhagen, 1990).

Two term AVO equation - Diagnostics


Diagnostics on pre-stack data

amplitude

Offset or angle

offset

Amplitude of real data Compute errorPer sample

error

Compute the squared error

amplitude

Fix a zero offset time – Fit the Aki and Richards equationOutput: L and M

Compute the amplitude from The Aki and Richards equationfrom the given L and M

Subtract this amplitude from the data amplitude and square it

Sum (stack) this error data overThe different sub-stack ranges – e.g. near mid and far ranges

Output: Error cubesRed=positive impedance


Normalised AVO ERROR – Near - Mid - Far The relative high values of these maps are considered to correspond to large errors in the data AVO behaviour

Error on Near – Mid – Far sub-stacks


Large residual move-out visible at top Brent(may become better after model update)

Red=positive impedance

X-unconformity

Top Brent

Top Dunlin

Error on Depth Migrated Gathers


PSDM Gather at locations of large errorInterference/Noise Or residual move-out?


X-unconformity

Top Brent

Top Dunlin

Error on Depth Migrated Gathers


The pre-stack diagnostics showed what to improve in the remainder of the processing sequence:

• Residual moveout, • multiples, • and (unspecified) noise (e.g. intra-bed multiples, mode conversions?)

Therefore the following processing steps were applied on the final Pre-Stack Depth Migrated data:

• a multiple removal on the psdm image gathers,

• alignment and spectral balancing of the sub-stacks,

• and post-stack multiple removal


Left: original – Right: pre-stack multiple removal (TX deconvolution) and post-stack multiple removal


Processing after Migration


The procedure on post-stack data

The reason to compute diagnostics on the sub-stack is that:1) The sub-stacks had undergone some essential processing steps:

SPLAT multiple removal, alignment of the data and spectral balancing.

2) As these sub-stack would be used in the inversion, we wanted to have diagnostics on the sub-stacks

Use the L-M fit on near mid and far stacks:

- Compute L and M on the Near and Mid sub-stacks – L1 and M1

- Compute L and M on the Near and Far sub-stacks – L2 and M2

The Sign Flip diagnostic :

The signed difference is quite illustrative as a diagnostic because it is indicative of a total mismatch of the two-term approximation and the amplitude behaviour of the data.

100,100)()(10021

2121

MM

MMMsignMsign

Diagnostics on post-stack data


M estimates. Left: from near and mid – Right: from near and farThe Mid map shows larger M values as compared to far M (same colour-scale)

M estimated on Near – Mid – Far sub-stacks


M Error maps. Signed error – negative means a flip in the sign of M

Orange: sign flip

The Sign Flip Diagnostics


Inspection of the stacks at the location of a sign-flip according to the error map

weak stronger weakest

Red=positive impedance Yellow Horizon: Top Brent

Relation of Sign Flip with sub-stacks


Stacks at low error location – Near - Mid - Far

weak stronger strongest

Red=positive impedance Yellow Horizon: Top Brent

Relation of Sign Flip with sub-stacks


Summary of the AVO diagnostics:

• Both the pre and post stack diagnostics seem to work in the sense that the relative high error spotson the maps correspond to problem areas of the data

• The AVO diagnostics is used as a tool to check whether the AVO behavior of the datasatisfy the AVO assumptions in the AVO inversion

• The AVO diagnostics should be applied early on in the processing stream in order to be able toinfluence the psdm and /or processing of the data that will be used in the AVO inversion.


After the inversion

(Jason Rocktrace AVA inversion)


Review V-sh seismic vs wells – well 13-2 – goodComparisons at full frequency and seismic frequency scale (not shown)

Lows and highs do match

QC of V-shale cube


“Blind” Well ResultsThe horizontal C2 well (not indicated) encountered an up-thrown shale block in the reservoir section. The vertical 211/13-2 well (indicated) shows a very thin Kimmeridge section of approximately 30 ft. The V-shale cube from the inversion ties the well log very well over the reservoir section. A similar story holds for the D1 well.

Shale: red – yellowSand: blue


Conclusions:

Diagnostics

• Identification of data issues and early remedial action

• Helps to set realistic expectations

Inversion result

• Contains valuable features that had not been observed on reflection seismic previously(confirmed by wells)

But how did the project work?

• Still some delays because of unexpected amplitude striping

• But in general a much more integrated approach

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