Seismic Meta-Attributes and the Illumination of the Internal Reservoir Architecture of a Deepwater...
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Seismic Meta-Attributes and the Illumination of the Internal Reservoir Architecture of a Deepwater Synthetic Channel Model* Staffan Van Dyke 1 and Renjun Wen 2 6HDUFK DQG 'LVFRYHU\ $UWLFOH 3RVWHG )HEUXDU\ $GDSWHG IURP RUDO SUHVHQWDWLRQ JLYHQ DW *HRVFLHQFH 7HFKQRORJ\ :RUNVKRS 5HYLVLWLQJ 5HVHUYRLU 4XDOLW\ ,VVXHV LQ 8QFRQYHQWLRQDO DQG &RQYHQWLRQDO 5HVRXUFHV 7HFKQLTXHV 7HFKQRORJLHV DQG &DVH 6WXGLHV $XVWLQ 7H[DV 1RYHPEHU $$3* 6HULDO ULJKWV JLYHQ E\ DXWKRU )RU DOO RWKHU ULJKWV FRQWDFW DXWKRU GLUHFWO\ *HRPRGHOLQJ 7HFKQRORJ\ &RUSRUDWLRQ +RXVWRQ 7H[DV VWDIIDQYDQG\NH#JHRPRGHOLQJFRP *HRPRGHOLQJ 7HFKQRORJ\ &RUSRUDWLRQ &DOEDU\ $OEHUWD UHQMXQZHQ#JHRPRGHOLQJFRP Abstract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ｳSRQGLQJ DWWULEXWHVｴ 7KH SURSRVHG PHWDDWWULEXWHV ZHUH DOVR XVHG DV LQSXW WR QHXUDOQHWZRUN VHLVPLF IDFLHV FODVVLILFDWLRQ ZKLFK UHVXOWHG LQ D FORVHU PDWFK WR WKH ｳJURXQGWUXWKｴ IDFLHV PRGHO WKDQ FRQYHQWLRQDO DWWULEXWHV References Cited +DUGDJH % ,QVWDQWDQHRXV VHLVPLF DWWULEXWHV FDOFXODWHG E\ WKH +LOEHUW 7UDQVIRUP 6HDUFK DQG 'LVFRYHU\ $UWLFOH ZHEVLWH DFFHVVHG -DQXDU\ &KRSUD 6 DQG .- 0DUIXUW 6HLVPLF DWWULEXWHV D KLVWRULFDO SHUVSHFWLYH *HRSK\VLFV Y S 6262
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We applied workflows of seismic attribute analysis and facies classification to a synthetic 3-D seismic volume, which was generated from velocity and density volumes in a 3-D turbidite facies model. The turbidite facies model was simulated based on a process-oriented method and is able realistically to capture major features in a turbidite environment. Because we had a priori knowledge of the geologic structure in the synthetic post-stack seismic volume, we were able to examine the efficacy of different seismic attribute analysis methods in delineating turbidite facies in the deepwater system. Based on attribute-analysis results of this synthetic seismic volume, we investigated the use of new seismic meta-attribute calculations for the detection of internal reservoir characteristics within deepwater reservoirs. New meta-attributes were created for characterizing features such as: the lateral continuity of amplitude response, the lateral continuity of similarly thick beds, and multiple combinations of geometric and response, energy, and instantaneous attributes, which are coined “ponding attributes.” The proposed meta-attributes were also used as input to neural-network seismic facies classification, which resulted in a closer match to the “ground-truth” facies model than conventional attributes.
Transcript of Seismic Meta-Attributes and the Illumination of the Internal Reservoir Architecture of a Deepwater...
Sei
smic
Met
a-A
ttri
bu
tes
an
d t
he
Illu
min
ati
on
of
the
Inte
rna
l R
eser
vo
ir A
rch
itec
ture
of
a D
eep
wa
ter
Sy
nth
etic
Ch
an
nel
Mo
del
*
Sta
ffan
Van
Dyk
e1 a
nd
Ren
jun
Wen
2
Ab
stra
ct
Ref
eren
ces
Cit
ed
SeismicMeta
Attributesandthe
IlluminationoftheInternalReservoir
IlluminationoftheInternalReservoir
Architecture
ofaDeepwaterSynthetic
ChannelModel
ChannelModel
By:StaffanVanDyke
andRenjunWen
GeomodelingTechnologyCorporation
Outline
Outline
Ci
fd
hll
l•
Constructionofdeepwaterchannelleveecomplex
syntheticseismicdataset
•Instantaneousattributes
•Instantaneousattributes
•Volumecurvature
•Meta
attributes
•Meta
attributes
•Semblance
St
ld
iti
•Spectraldecomposition
•Colorblending
Pi
ilC
tA
li(PCA)
•PrincipalComponentAnalysis(PCA)
•Neuralnetw
ork
faciesclassification
SyntheticDeepwaterCLC
FaciesModel
SyntheticDeepwaterCLC
FaciesModel
•Adeepwaterchannel
Adeepwaterchannel
leveecomplexmodel
wasbuiltusingprocess
orientedmethodology
orientedmethodology,
inwhichthreefan
channelsare
responsibleforthe
responsibleforthe
depositionanderosion
overtimeof9
distinctivelithofacies.
CLC
Model–Velocity
andDensity
CLC
ModelVelocity
andDensity
•Thefaciesmodelwas
populatedwithseismic
velocities(Vp)and
densitiesusingthe
conventionalgeostatistical
methodbyassigning
mean,standard
deviation
andvariogram
modelsfor
each
lithofacies.
•Each
lithofacieswas
populatedwithdistinctive
Vpanddensity
statistics.
•Anacousticim
pedance
volumewasthen
calculatedfrom
theVpand
density
volumes.
CLC
Model–SeismicResponse
CLC
Model
SeismicResponse
•A3Dseismicpost
stack
volume
wasgeneratedbyusinga30Hz
Rickerwaveletandtheacoustic
impedance
volumecalculated
from
velocity
anddensity
volumes,
througha1Dconvolutional
process.Majorcharacteristicsof
deepwaterturbiditechannelsseen
inactualseismicdata
are
re
createdinthissyntheticmodel.
•Weappliedworkflowsofseismic
attribute
analysisandfacies
classificationto
thissynthetic3D
seismicvolume.
•Because
weknowthe“ground
truth”faciesmodelresultingin
this3Dseismicmodel,wewere
this3Dseismicmodel,wewere
ableto
examinetheefficacy
of
differentseismicattribute
analysis
methodsinrevealingtheinitial
faciesmodel.
faciesmodel.
BasicSeismicAttributes
BasicSeismicAttributes
•In
reflectionseismology,seismicattributesrepresenta
quantity
derived,orextracted,from
theseismicwavelet(s),
such
asphase/frequency/amplitude
such
asphase/frequency/amplitude.
•Attributescanbeanalyzedeitherpost
stack
orpre
stack
(CMP
gathers)
gathers).
•Instantaneousattributesare
typicallyextractedfrom
asingle
trace,butmore
complexattributesare
calculatedacross
,p
multipletraceswithinadefinedwindow.
InstantaneousAttributes
•Thefirstattributeswere
InstantaneousAttributes
derivedfrom
the1D
complexseismictrace.
•Therecordedseismictrace
is
knownastherealpartofthe
complextrace.By
perform
inga90phase
tti
kth
rotation,knownasthe
HilbertTransform
,the
imaginary
partofthe
complextrace
isrevealed
complextrace
isrevealed.
•From
thiscomplextrace,
such
attributesas
instantaneous
instantaneous
amplitude/phase/frequency
canbecalculated.
(Hardage,2010)
InstantaneousAmplitude
InstantaneousAmplitude
•Atanycoordinate
on
Atanycoordinate
on
thetimeaxis,avector
a(t)canbecalculated
thatextends
thatextends
perpendicularlyaway
from
thetimeaxisto
interceptthehelical
interceptthehelical
complextrace
z(t).
•Thelength
ofthisvector
istheamplitudeofthe
complextrace
atthat
instantoftime–hence
theterm
“instantaneous
amplitude.”
InstantaneousPhase
InstantaneousPhase
•Thephase
atone
Thephase
atone
instantalonga
trace,independent
oftrace
amplitudes.
•Excellentindicator
forlateralcontinuity
ofreflectionevents.
InstantaneousFrequency
InstantaneousFrequency
•Therate
ofchange
Therate
ofchange
ofphase
overtime.
•Goodindicatorfor
Goodindicatorfor
porosity,thickness,
andpresence
of
hydrocarbons.
Meta
Attributes
Meta
Attributes
Fth
fthi
til
ttt
ibt
•Forthepurposesofthisarticle,meta
attributesare
consideredto
beanycombinationofseismicattributes
designedto
enhance
thesignalofthewavelet.
•Themostwellknownofthese
istheSweetness
attribute:
InstantaneousAmplitude
InstantaneousAmplitude
InstantaneousFrequency
•Sweetness
worksbecause
frequency
isanexcellentindicator
notonlyforporosity
andthickness,butforhydrocarbons.It
hasbeenobservedthatsiliciclasticreservoirsstandoutfrom
hasbeenobservedthatsiliciclasticreservoirsstandoutfrom
thebackgroundmuch
betterthanwhenemployingasingle
attribute.
Sweetness
Sweetness
NewMeta
Attributes
NewMeta
Attributes
•Duringtheanalysisofthesyntheticdata,we
derivedanumberofnewmeta
attributes,
including:
–ContinuityofAmplitudeResponses
ContinuityofAmplitudeResponses
–AmplitudeResponse
ofSim
ilarlyThickBodies
–ContinuityofSim
ilarlyThickBodies
–PondingMeta
Attributes
g
PondingAttributes
PondingAttributes
GeometricalAttributes
PondingAttributes
PondingAttributes
PondingAttributes
PondingAttributes
InstantaneousAmplitude
InstantaneousAmplitude
MostNegative
Curvature
MostNegative
Curvature
PondingAttribute
1
MAtt(P1)=ƒ(Inst.Amp,Dom.Freq,MostNeg.Curv.)
InstantaneousAmplitude
InstantaneousAmplitude
ShapeIndex
ShapeIndex
PondingAttribute
2PondingAttribute
2
MAtt(P2)=ƒ(Inst.Amp,Dom.Freq,ShapeIndex)
ContinuityofAmplitudeResponses
ContinuityofAmplitudeResponses
MAtt(CAR)=ƒ(Inst.Amp,Inst.Phase,Dom.Freq)
AmplitudeResponse
ofSim
ilarlyThickBodies
AmplitudeResponse
ofSim
ilarlyThickBodies
Amplituderesponse
of
similarlythickbodies
MAtt(ATB)=ƒ(Inst.Amp,Inst.Freq,Dom.Freq)
ContinuityofSim
ilarlyThickBodies
ContinuityofSim
ilarlyThickBodies
MAtt(CTB)=ƒ(Inst.Freq,Inst.Phase)
RefiningSemblance
RefiningSemblance
•Seismiccoherency
(semblance)isameasure
oflateral
changesintheseismicresponse
causedbyvariationin
tt
tti
hlith
lit
dth
structure,stratigraphy,lithology,porosity,andthe
presence
ofhydrocarbons(Chopra
andMarfurt,2005).
Ti
llbl
il
ith
ff
•Typicallysemblance
involvescomparingthewaveform
of
adjacenttracesto
oneanotherviathecalculationof
reflectordip/azimuthsthroughoutthedata
volume
reflectordip/azimuthsthroughoutthedata
volume.
•However,potentialbandingfrom
thezero
crossingcan
occurwhenthedip/azimuthsare
calculated
manifesting
occurwhenthedip/azimuthsare
calculated,manifesting
artificialanomaliesinthedata.
RefiningSemblance
RefiningSemblance
bl
ll
dh
k•Semblance
wascalculatedonthepost
stack
dataset.
•Thepost
stack
volumewasphase
rotated90
toobtaintheim
aginary
trace
component.
gy
p
•Thesemblance
from
theim
aginary
trace
was
calculated
calculated.
•Therotatedsemblance
volumewas
bt
tdf
thii
lbl
subtractedfrom
theoriginalsemblance
volume.
RefiningSemblance
RefiningSemblance
OriginalSemblance
RefinedSemblance
ColorBlending
ColorBlending
•Colorblendingofthefirst3PrincipalComponentAnalysis(PCA)
componentscalculatedbyStransform
methodologyofSpectral
Decomposition.
VolumeFaciesClassification
•Aneuralnetw
ork
classificationschemewasusedto
derive
aseismic
lithofaciesvolumebasedontheinputofmultipleseismicattributes.
Summary
andConclusions
Summary
andConclusions
Ct
tif
thti
3D
ii
t•
Constructionofasynthetic3Dseismicresponse
toa
deepwaterchannelleveecomplexmodelallowedcontrolled
analysisoftheefficacy
ofindividualandmeta
attributes.
•Meta
attribute
analysisofaSouth
Texasdataset–and
subsequently,anAlbertadataset–revealedsignificant
correlationsto
welllogdata,whichwere
strongerthanthose
g,
gforindividualattributes.Developmentandtestingofthese
andothernewmeta
attributescontinues.
•Otherquantitative
post
stack
techniquesperform
edbythe
Otherquantitative
post
stack
techniquesperform
edbythe
interpreteralsoilluminate
internalreservoirarchitecture.
–SpectralDecomposition
St
tlSli
/St
tGid
–StratalSlices/Strata
Grids
–FaciesClassification(trainedanduntrained)
–Colorblending
ContactInform
ation
ContactInform
ation
Sff
kStaffanVanDyke
SeniorGeophysicist
1001S.DairyAshford,Suite110
Houston,TX77077
(281)6774410
staffanvandyke@geomodelingcom
RenjunWen
ChiefArchitect
11006658thStreetSW
Calgary,AB
T2P3K7
Calgary,AB
T2P3K7
(403)2629172
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