TGS Arcis- Canada Arcis Heavy Oil RS
1
Seismic Characterization of Heavy Seismic Characterization of Heavy-Oil Reservoirs Oil Reservoirs Seismic Characterization of Heavy Seismic Characterization of Heavy-Oil Reservoirs Oil Reservoirs Heavy-oil is usually categorized based on its density, which is defined in terms of API 1. What is heavy 1. What is heavy-oil oil? 3. Subsurface heavy 3. Subsurface heavy-oil bearing formations oil bearing formations The majority of the heavy oil deposits are found in two countries, Canada (Alberta oil sands) and Venezuela (Orinoco belt), both of which contain SEM images showing bitumen in the pore space as well as other minerals SEM images showing bitumen in the pore space as well as other minerals 2. Where is heavy 2. Where is heavy-oil found? oil found? its density, which is defined in terms of API (American Petroleum Institute) gravity – the denser the oil, the lower the API gravity. API gravity values for liquid hydrocarbons range from 4° for tar-rich bitumen to 70° for condensates. The US Department of Energy defines heavy oil as having API gravity between 10° and 22.3° and this is followed as a standard. (Alberta oil sands) and Venezuela (Orinoco belt), both of which contain recoverable reserves comparable to those of Saudi Arabia. Other countries with significant oil deposits are the United States (California, Alaska and Utah), Mexico, Russia, China and Oman. Abundant bitumen occluding most of the pore space. Chert grains and sedimentary lithoclasts are also illustrated in this photo. Bitumen SL Chert Image illustrates the matrix bitumen (B), grain- rimming pyrite (Py), and ferroan dolomite (fD) that contribute to occluding the intergranular porosity of this sample. A bioclastic lithoclast dominates the middle right portion of this image. fD fD Py Py B 4. Rock physics analysis of heavy 4. Rock physics analysis of heavy-oil reservoirs using well oil reservoirs using well-log data log data (Image courtesy: Suncor Energy) We begin rock physics analysis by using the available well log data to cross-plot different pairs of parameters (density, Vp/Vs ratio, P-impedance and gamma-ray) for the reservoir formation (McMurray), at the appropriate depth. The different crossplots are shown in figures below. 10-11-81-10W4 Reservoir sand Interbedded sand 8-35-80-10W4 Interbedded mud 9-29-81-9W4 (Images courtesy: Paramount Energy Trust) Exploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca (Images courtesy: Core Labs) Distribution of heavy-oil reservoirs across the world Athabasca oil sands Exploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca A strong correlation is seen between bulk density and gamma-ray The outcome of this rock physics analysis is Depth: 100 m Depth: 400 m Depth:600 m Depth: 100 m Depth: 400 m Depth: 600 m Exploring if P Exploring if P-impedance is a lithology indicator for the McMurray formation in Athabasca impedance is a lithology indicator for the McMurray formation in Athabasca How about LMR? How about LMR? 1. To understand the relationship between lithology and the related rock parameters 2. Use the determined relationship to pick the lithology sensitive rock parameters that can be seismically derived. In these examples a good correlation is seen between density and gamma ray and so we attempt to derive density from seismic data. Depth: 100 m Depth: 400 m Depth:600 m Depth: 100 m Depth: 400 m Depth: 600 m Correlation between bulk density and P-impedance may be seen with depth from seismic data. 5. Workflow for deterministic interpretation 5. Workflow for deterministic interpretation of reservoir heterogeneity of reservoir heterogeneity 7. Synthetic tie with AVO derived reflectivities 7. Synthetic tie with AVO derived reflectivities Real Data Example From Athabasca Oil Sands Area Real Data Example From Athabasca Oil Sands Area Depth: 100 m Depth: 400 m Depth: 600 m Seismic data Well logs horizons 6. 3 6. 3-term AVO inversion term AVO inversion ρ ρ θ α β β β θ α β α α θ θ Δ - + Δ - Δ + = ) sin 4 1 ( 2 1 sin 4 ) tan 1 ( 2 1 ) ( 2 2 2 2 2 2 2 R reflection from VO inversion P-reflection synthetic ho-reflection ynthetic ho-reflection om VO inversion The 3-term AVO equation used for AVO inversion (Clean sands seen in red) (Clean sands seen in red) AVO friendly processing Improved 3-term AVO inversion P reflectivity S reflectivity Rho reflectivity Models of P impedance S impedance density geology Synthetic tie 1 km 220 ms P-r AV Rh sy Rh fro AV where is the P-velocity, is the S-velocity and is the density α β ρ P impedance S impedance density Colored P impedance Colored S impedance Colored Density Well tie and interpretation Multi-variate analysis and neural network analysis ZOI ZOI 220 ms 11 1 2 3 4 5 6 7 8 9 10 The figure above shows the correlation of log curves, the synthetics and the derived P-impedance and density reflectivities derived from seismic data. Notice the good correlation between the two pairs of reflectivities. A segment of a seismic section shown here after careful amplitude recovery and data conditioning for AVO inversion. The zone of interest is marked on the section. well-1 well-1 well-2 well-3 well-4 well-5 well-6 well-7 well-8 well-9 well-1 80ms (a) Segment of density reflectivity obtained after AVO inversion. (b) Equivalent segment of colored density derived from density reflectivity after simple trace integration. (a) 1 km Density reflectivity 8 trace integration. Notice, the density log curves are shown overlaid in black, gamma-ray curves in purple and impedance logs in blue. (c) Equivalent segment of density section derived from post-stack inversion of density (b) 1 km 80ms derived from post-stack inversion of density reflectivity. (d) Equivalent segment of Vshale section derived from density section on using the linear relationship between the density and Colored density 80ms gamma- ray in the rock physics analysis. Wells Wells 3 and and 7 were were drilled drilled recently recently and and so so served served as as blind blind well well tests tests. Well Well 3 is is shaley shaley within within the the McMurray McMurray formation formation and and density density (c) Density section after post-stack inversion 8 inversion inversion confirms confirms this this. Well Well 7 indicates indicates good good sand sand in in the the middle middle McMurray McMurray formation, formation, but but a sandy sandy cap cap in in the the upper upper McMurray McMurray. These These results results are are very very nicely nicely confirmed confirmed on on (d) 80ms These These results results are are very very nicely nicely confirmed confirmed on on the the inverted inverted density density section section. 8. Conclusions 8. Conclusions Heterogeneity within heavy-oil reservoirs can be characterized by adopting the deterministic For further information contact Satinder Chopra, [email protected] 403.781.1700 www.arcis.com RESERVOIR SERVICES SEISMIC DATA PROCESSING PARTICIPATION SURVEYS DATA MARKETING / MANAGEMENT © 2008 Arcis Corporation, Calgary, Canada This poster represents our current understanding about seismic characterization of heavy-oil reservoirs. While we recommend its application to seismic data analysis, we accept no responsibility for its use. We appreciate your ongoing feedback and discussion. V-shale section derived from the above panel 1 km approach outlined in this poster.
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Transcript of TGS Arcis- Canada Arcis Heavy Oil RS
Seismic Characterization of HeavySeismic Characterization of Heavy--Oil ReservoirsOil ReservoirsSeismic Characterization of HeavySeismic Characterization of Heavy--Oil ReservoirsOil Reservoirs
Heavy-oil is usually categorized based on
its density, which is defined in terms of API
1. What is heavy1. What is heavy--oiloil? 3. Subsurface heavy3. Subsurface heavy--oil bearing formationsoil bearing formations
The majority of the heavy oil deposits are found in two countries, Canada
(Alberta oil sands) and Venezuela (Orinoco belt), both of which contain
SEM images showing bitumen in the pore space as well as other mineralsSEM images showing bitumen in the pore space as well as other minerals
2. Where is heavy2. Where is heavy--oil found?oil found?
its density, which is defined in terms of API(American Petroleum Institute) gravity – thedenser the oil, the lower the API gravity.API gravity values for liquid hydrocarbonsrange from 4° for tar-rich bitumen to 70°for condensates. The US Department ofEnergy defines heavy oil as having APIgravity between 10° and 22.3° and this isfollowed as a standard.
(Alberta oil sands) and Venezuela (Orinoco belt), both of which containrecoverable reserves comparable to those of Saudi Arabia. Othercountries with significant oil deposits are the United States (California,Alaska and Utah), Mexico, Russia, China and Oman.
Abundant bitumen occluding most of the porespace. Chert grains and sedimentary lithoclastsare also illustrated in this photo.
Bitumen
SL
Chert
Image illustrates the matrix bitumen (B), grain-rimming pyrite (Py), and ferroan dolomite (fD)that contribute to occluding the intergranular
porosity of this sample. A bioclastic lithoclast
dominates the middle right portion of this image.
fDfD
PyPy
BB
4. Rock physics analysis of heavy4. Rock physics analysis of heavy--oil reservoirs using welloil reservoirs using well--log datalog data
(Image courtesy: Suncor Energy)
We begin rock physics analysis by using the available well log data to cross-plot different pairs of parameters (density, Vp/Vs ratio, P-impedance and gamma-ray) for the reservoir formation (McMurray), at the appropriate depth. The different crossplots are shown in figures below.
10-11-81-10W4
Reservoir sand Interbedded sand8-35-80-10W4
Interbedded mud9-29-81-9W4
(Images courtesy: Paramount Energy Trust)
Exploring if density is a lithology indicator for the McMurray formation in AthabascaExploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in AthabascaExploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca
(Images courtesy: Core Labs)Distribution of heavy-oil reservoirs across the world
Athabasca oil sands
Exploring if density is a lithology indicator for the McMurray formation in AthabascaExploring if density is a lithology indicator for the McMurray formation in Athabasca Exploring if Vp/Vs is a lithology indicator for McMurray formation in AthabascaExploring if Vp/Vs is a lithology indicator for McMurray formation in Athabasca
A strong correlation is seen
between bulk density and
gamma-ray
The outcome of this rock physics analysis is
Depth: 100 m Depth: 400 m Depth:600 m Depth: 100 m Depth: 400 m Depth: 600 m
Exploring if PExploring if P--impedance is a lithology indicator for the McMurray formation in Athabascaimpedance is a lithology indicator for the McMurray formation in Athabasca How about LMR?How about LMR?
1. To understand the relationship between lithology and the relatedrock parameters
2. Use the determined relationship to pick the lithology sensitiverock parameters that can be seismically derived.
In these examples a good correlation is seen betweendensity and gamma ray and so we attempt to derive densityfrom seismic data.
Depth: 100 m Depth: 400 m Depth:600 m Depth: 100 m Depth: 400 m Depth: 600 m
Correlation between bulk
density and P-impedance
may be seen with depth
from seismic data.
5. Workflow for deterministic interpretation 5. Workflow for deterministic interpretation of reservoir heterogeneityof reservoir heterogeneity
7. Synthetic tie with AVO derived reflectivities7. Synthetic tie with AVO derived reflectivities
Real Data Example From Athabasca Oil Sands AreaReal Data Example From Athabasca Oil Sands Area
Depth: 100 m Depth: 400 m Depth:600 m Depth: 100 m Depth: 400 m Depth: 600 m
Seismic data Well logshorizons
6. 36. 3--term AVO inversionterm AVO inversion
ρ
ρθ
α
β
β
βθ
α
β
α
αθθ
∆−+
∆−
∆+= )sin41(
2
1sin4)tan1(
2
1)(
2
2
2
2
2
2
2R
refl
ec
tio
n f
rom
A
VO
in
ve
rsio
n
P-r
efl
ec
tio
n
syn
the
tic
Rh
o-r
efl
ec
tio
n
syn
the
tic
Rh
o-r
efl
ec
tio
n
fro
m
AV
O i
nve
rsio
n
The 3-term AVO equation used for AVO inversion
(Clean sands seen in red) (Clean sands seen in red)
AVO friendly processing
Improved 3-term AVO inversion
P reflectivityS reflectivity
Rho reflectivity
Models ofP impedanceS impedance
density
horizonsgeology
Synthetic tie
1 km
220 ms
ραβαα 22
P-r
efl
ec
tio
n f
rom
A
VO
in
ve
rsio
n
Rh
os
yn
the
tic
Rh
ofr
om
A
VO
in
ve
rsio
n
where is the P-velocity, is the S-velocity and is the density α β ρ
P impedanceS impedance
density
Colored P impedanceColored S impedance
Colored Density
Well tie and interpretation
Multi-variate analysis and neural network analysis
ZOIZOI
220 ms
111 2 3 4 5 6 7 8 9 10
The figure above shows the correlation of log curves, the synthetics and thederived P-impedance and density reflectivities derived from seismic data.Notice the good correlation between the two pairs of reflectivities.
A segment of a seismic section shown here after careful amplitude recovery and dataconditioning for AVO inversion. The zone of interest is marked on the section.
we
ll-11
we
ll-1
we
ll-2
we
ll-3
we
ll-4
we
ll-5
we
ll-6
we
ll-7
we
ll-8
we
ll-9
we
ll-1
0
80
ms
(a) Segment of density reflectivity obtained after
AVO inversion.
(b) Equivalent segment of colored density
derived from density reflectivity after simple
trace integration.
(a)
1 km
Density reflectivity
80
ms
trace integration.
Notice, the density log curves are shown
overlaid in black, gamma-ray curves in purple
and impedance logs in blue.
(c) Equivalent segment of density section
derived from post-stack inversion of density
(b)
1 km
80
ms
derived from post-stack inversion of density
reflectivity.
(d) Equivalent segment of Vshale section
derived from density section on using the
linear relationship between the density and
Colored density
80
ms
gamma- ray in the rock physics analysis.
WellsWells 33 andand 77 werewere drilleddrilled recentlyrecently andand soso
servedserved asas blindblind wellwell teststests.. WellWell 33 isis shaleyshaley
withinwithin thethe McMurrayMcMurray formationformation andand densitydensity
(c)
Density section after post-stack inversion
80
ms
inversioninversion confirmsconfirms thisthis..
WellWell 77 indicatesindicates goodgood sandsand inin thethe middlemiddle
McMurrayMcMurray formation,formation, butbut aa sandysandy capcap inin thethe
upperupper McMurrayMcMurray..
TheseThese resultsresults areare veryvery nicelynicely confirmedconfirmed onon(d)
80
ms
TheseThese resultsresults areare veryvery nicelynicely confirmedconfirmed onon
thethe invertedinverted densitydensity sectionsection..
(d)
8. Conclusions8. Conclusions
Heterogeneity within heavy-oil reservoirs can be
characterized by adopting the deterministic
For further information contact Satinder Chopra, [email protected] 403.781.1700 www.arcis.com
RESERVOIR SERVICES SEISMIC DATA PROCESSING PARTICIPATION SURVEYS DATA MARKETING / MANAGEMENT
© 2008 Arcis Corporation, Calgary, CanadaThis poster represents our current understanding about seismic characterization of heavy-oil reservoirs.
While we recommend its application to seismic data analysis, we accept no responsibility for its use. We
appreciate your ongoing feedback and discussion.
V-shale section derived from the above panel1 km
approach outlined in this poster.
