Pcube+ - high resolution horizon update by prestack inversion Øyvind M. Skjæveland and Richard William Metcalfe, Statoil. Odd Kolbjørnsen, Lundin (formerly NR). Per Røe and Ragnar Hauge, Norwegian Computing Centre (NR).

Outline

• Motivation

• Simplified example – thin gas sand – achieving a detailed interpretation

− Classical detuning

− Rock physics inversion walk-through

• Real case with several layers in tuning (Statfjord East flank)

• Concluding remarks

2

Horizon interpretation - customary practice:

Pick one substack.

Interpret horizons in max peak and max trough.

Thin layers - tuning and AVO issues:

Real layer boundaries not in max peak or max trough

Amplitudes and AVO carry information – not used.

Horizons update by prestack inversion:

Using rock physics knowlegde.

Moving horizons away from peaks and troughs.

Quantifying the uncertainty.

Resulting in:

Better volumetrics.

More accurate well placement.

Better understanding of uncertainty.

Horizon consistency check by prestack data.

Motivation – horizon update by inversion Horizons are widely used

• Volumetrics

• Well prognoses

• Geomodels

One of the most important

deliveries from geophysics.

Vp/V

s

AI

3

Simple case - Thin gas sand in tuning How to achieve a more detailed horizon placement

4

Well

gas

5

Below 0 and λ/2 the amplitude is affected by thickness

Well

position

Input horizons

Output horizons

True thick.

= f(amp, int.thick.)

The quick and easy way – detuning

Int.thick. True thick.

amp

True thickness

Knowledge of properties and assumption of blocky sand

enables detailed horizon prediction below tuning thickness

Simple case - Rock physics inversion AI and near stack only. 3 possible Lithology fluid classes (LFC’s)

6

4ms

LFC

grid

Elastic

properties and

wavelet known

from well.

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

Model 1 – very poor match

7

AI

Synthetic

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

LFC

configuration

Model 2 – very poor match

8

AI

Synthetic

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

LFC

configuration

Model 3 poor match

9

AI

Synthetic

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

LFC

configuration

Model 4 good match

10

AI

Synthetic

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

LFC

configuration

11

Some good-match models P(y.s) P(o.s.)

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

Probability: match-weighted sum of all models

P(gas)

Probability: match-weighted sum of all models

12

Some good-match models P(all)

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

Buland et al 2008

Pcube

0.3 0.4 0.3

13

Old shale

over

young shale

Old shale

over

gas sand

Gas sand

over

young shale

Extra:

Illegal

combinations

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

Probability: match-weighted sum of all models

Some good-match models P(all)

Buland et al 2008

Pcube

14

Young shale

AI = 8000

Old shale

AI = 8000

Gas sand

AI = 5000

wavelet

Old shale

over

young shale

Old shale

over

gas sand

Gas sand

over

young shale

Illegal

combinations

Probability: match-weighted sum of all legal models

Pcube+

Illegal combinations

impose blocky sand

Buland et al 2008

Pcube

Gas

sand

Brine

sand

Zone 1

Zone 2

Zone 3

Actual setup – prestack case

15

Smoothed model

=Prior model

Inversion

result

wavelets

AI

Vp/V

s

AI

Angle stacks

Old

shale

Young

shale

Gas

sand

Concept

Input horizons Inp

ut

ho

rizo

n u

nce

rtain

ty

Output horizons

w/uncertainty

Inve

rsio

n

Illegal

combinations

-per zone

Knowledge of properties and assumption of blocky sand

enables detailed horizon prediction below tuning thickness

Statfjord East flank

Brent Gp.

Statfjord Fm.

Dunlin Gp.

East flank

slump block C

SFC

SFB

SFA

Prod. start

1979

300+ wells

Partners:

Statoil

Centrica

ExxonMobil

Draupne

Mime

Cook/Amundsen

shale

Shetland

Sand

Drake/Burton

shale

AI

Vp/v

s

LFC properties

Shetland

Mime

Draupne

Reworked Brent

Intra-reservoir Dunlin

Isolated Brent

Dunlin Group

BCU

TIB

BCU + 35ms

TIB + 30ms

(BIB)

BCU – 5 ms

BCU + 10ms

Statfjord – model setup

BCU

T IB

17

Shetland

Mime

Draupne

Reworked Brent

Intra-reservoir Dunlin

Isolated Brent

Dunlin Group

BCU

TIB

BCU + 35ms

TIB + 30ms

(BIB)

BCU – 5 ms

BCU + 10ms

Statfjord – model setup

Seismic section with interpretation

18

Shetland

Mime

Draupne

Reworked Brent

Intra-reservoir Dunlin

Isolated Brent

Dunlin Group

BCU

TIB

BCU + 35ms

TIB + 30ms

(BIB)

BCU – 5 ms

BCU + 10ms

Seismic section with interpretation

and slave horizons

Statfjord – model setup – slave horizons

19

Shetland

Mime

Draupne

Reworked Brent

Intra-reservoir Dunlin

Isolated Brent

Dunlin Group

BCU

TIB

BCU + 35ms

TIB + 30ms

(BIB)

BCU – 5 ms

BCU + 10ms

Seismic section with interpretation

and sand concept model

Statfjord – model setup – slave horizons

20

Shetland

Isolated Brent

Dunlin Group

Shetland

Mime

Draupne

Reworked Brent

Intra-reservoir Dunlin

Isolated Brent

Dunlin Group

BCU

TIB

BCU + 35ms

TIB + 30ms

(BIB)

BCU – 5 ms

BCU + 10ms

BCU ± 10ms

TIB ± 25ms

BIB ± 25ms

Sand smoothed model section

= prior distribution for sand

Probability 0 1

21

Statfjord – model setup – sand prior section

BCU ± 10ms

TIB ± 25ms

BIB ± 25ms

Sand smoothed model section

= prior distribution for sand

Probability 0 1

22

Statfjord – model setup – sand prior section

BCU ± 4ms

TIB ± 11ms

BIB ± 8ms

Posterieor

uncertainty

(2σ ≈ P(95))

Sand inversion result section

= posterior distribution for sand

Probability 0 1

23

Statfjord – model setup – sand post. section

Inversion process – one trace

24

Isolated Brent

TIB ± 11ms

TIB

Deterministic

model

TIB ± 25ms

Human estimate

of uncertainty

Updated to match

prestack data

inversion

near, input horizons

far, input and slave horizons

sand prior model, input horizons

sand posterior, output horizons

far, output horizons

A B C

A B C

A B C

A B C

A B C

A B C

Input data, sand probability and horizons

25 m

i

Mid stack also used as input but not shown

Probability 0 1

Concept model from horizons

250m Probability 0 1

A

B

C

Input (prior

expected

isochrone)

(constant 30ms) A B C

A B C

Isochrone Isolated Brent sand

A

B

C

Output

(posterior

expected

isochrone)

0 65 Isolated Brent thickness, ms

40 20

sand prior model, input horizons

sand posterior, output horizons

26

500m

0 65 Isolated Brent thickness, ms

40 20

A

B

C

A

B

C

A B C

Isochrone prediction vs well result

0 50 Intra-Reservoir Dunlin thickness, ms

30 20 10 40

Formation TVT est.

pre-inv (m)

TVT est.

post-inv(m)

TVT well

obs. (m)

Mime 3 4 4

Draupne 9 10 12

Reworked

Brent

20 18 8

Intra-res

Dunlin

17 7 7

Isolated

Brent

36 58 55

Intra-reservoir

Dunlin shale

expected

thickness

Isolated Brent

sand

expected

thickness

Well B result (Jan 2016)

27

500m

A

B

C

A

B

C

A

B

C

Mean

(P50)

Maximum

(P10)

Minimum

(P90)

Isochrone Isolated Brent sand - uncertainty

0 65 Isolated Brent thickness, ms

40 20 28

500m

Concluding remarks

• Working with sand probability and uncertain horizons as input and output:

− Facilitates integration of geoscience.

− Easy to relate to for all disciplines compared to AI and vp/vs input/output

• Accurate horizon placement - below tuning, away from peak and trough is valuable

− Horizons with uncertainty are often what we want

− Volumetrics, well planning, geomodels, instant isochrones

• Constraining the inversion is key to achieving sub-tuning detail

− Limited number of lithologies – limited combinations of vp,vs,ρ allowed

− Limiting where the lithologies can be – based on geological input

− Excluding non-geological and non-physical layering (e.g. brine just above gas)

• Making amplitudes move horizons is making amplitudes matter.

29

Thanks

• to the Statfjord partnership for the permission to show the Statfjord results.

• to co-authors for

− doing the Statfjord work (Richard)

− developing the technique and setting up a JIP to take it further (NR)

• to you for listening

30