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Integration of Processing and Reservoir GeophysicsTRANSCRIPT
Shell Exploration & Production
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Avo Inversion and Processing:
Dedication and Integration
Marcel Zwaan, Yvan Charreyron, Dave Bateman
Shell E&P Europe
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Acknowledgments
Exxon-Mobil and Shell E&P
Greg Hester, Richard Shipp, Paul Pillai, Alexander Sementsov (Fugro-Jason)
and Peter Ashton
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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”
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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
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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”
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• The Penguin Field
• Pre-Stack Diagnostics
• Post Stack Diagnostics
• Inversion impact
• Conclusions
Overview
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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.)
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Discovered 1974 - Sub Sea tie-back to Brent-C - 65 km Production Flow-line
PenguinsCluster
Brent Charlie
The Penguin Field Some facts
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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
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Get the expectations right:
Are Fluid and Litho discriminations possible?
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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
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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
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• Sand versus shale discrimination possible
• No Fluid discrimination feasible
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Wavelet estimation and near far stack alignment
Aim: check the alignment of the data
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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
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Conclusion:
Near (mid) and far stacks need to be alignedAnd the spectra need to be balanced to a common reference (mid stack)
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Angle versus offsets
How to compute the angle versus offsets
QC of angle versus offsets with angle versus offset in the well
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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
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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(
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Pre- and Post stack diagnostics
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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
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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
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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
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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
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PSDM Gather at locations of large errorInterference/Noise Or residual move-out?
Red=positive impedance
X-unconformity
Top Brent
Top Dunlin
Error on Depth Migrated Gathers
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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
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Left: original – Right: pre-stack multiple removal (TX deconvolution) and post-stack multiple removal
Red=positive impedance
Processing after Migration
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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
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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
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M Error maps. Signed error – negative means a flip in the sign of M
Orange: sign flip
The Sign Flip Diagnostics
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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
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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
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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.
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After the inversion
(Jason Rocktrace AVA inversion)
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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
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“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
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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