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, schopra@arcis.com 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.