4 June 2014© Earthworks Environment & Resources Ltd. All rights reserved1 Reservoir Connectivity...

April 10, 2023 © Earthworks Environment & Resources Ltd. All rights reserved 1

Reservoir Connectivity and Fluid Uncertainty Analysis using Fast Geostatistical Seismic Inversion

Reservoir Connectivity and Fluid Uncertainty Analysis using Fast Geostatistical Seismic Inversion

Ashley Francis and Graham Hicks*

Earthworks Environment & Resources Ltd

*BG Group plc


Relative, Deterministic and Stochastic Inversion

• Seismic zero phasing and amplitude spectrum shaping

• Wavelet removal by assuming blocky, layered earth

• Introduce low frequency model to give absolute impedance estimates

• Perturb low and high frequency outside wavelet bandwidth to investigate uncertainty– Inversion is unique within seismic bandwidth

– Constrained by wells and spatial model outside seismic bandwidth

Relative Impedance, Coloured InversionRelative Impedance, Coloured Inversion

Deterministic InversionDeterministic Inversion

Stochastic InversionStochastic Inversion


Limitations of Deterministic Inversion

• Deterministic inversion works well in thick layered systems– Reservoir interval has strong reflectivity

– Reservoir layering is relatively thick with well defined units close to the seismic resolution limit

– Blocky layering with little vertical variation in reservoir properties so layer average impedance is useful

• Deterministic inversion can work in thinner intervals where tuning occurs if– Well control is good

– Conformal layering with gradual lateral thickness changes


Deterministic InversionStrong Reflectivity

9300 10600Absolute Impedance


Deterministic InversionPorosity Mapping

0.20

0.05

0.25

0.05

Porosity - JurassicPorosity - Jurassic Porosity – Rotliegendes/DevonianPorosity – Rotliegendes/Devonian


Seismic Property Constraints & Reservoir Models

• The use of deterministic seismic inversion as an input to reservoir modelling is becoming common

• Deterministic inversion is unsuitable for this purpose for several reasons– Seismic resolution is at a much coarser scale than the cell

size of reservoir models

– Deterministic seismic inversion contains a low frequency model derived generally from wells

– Reservoir volume may be under or over-estimated, especially for thin intervals

– Deterministic inversion connectivity is exaggerated due to the effective smoothing (resolution limitation)


Scale of MeasurementsCore Plug to Seismic

Zone C

Zone B

Zone A

20 m20 m

100 m100 m

2 m2 m

(x70)(x70)


Reservoir Model Support

• Reservoir model Scale-up of well logs is generally vertical correction only– Incomplete correction resulting in too high variance

– Varying cell thickness or deviated geometry will give arbitrary change of support correction across model

• Additional correction for horizontal scale-up– Variogram based support correction by fine-scale

simulation

– Not generally supported by reservoir model packages

• Seismic vertical support ≈ 10 – 20 cells thick


Reservoir Model Upscaling200 x 200 x 2 m Cell Size

1250 1260 1270 1280 1290 1300 1310 1320 13304000

5000

6000

7000

8000

9000

10000

11000

12000

13000

Log Values for Well10

TWT (ms)

Imp

ed

an

ce (

m/s

*g/c

c)

Original

Vertical Scale-up Only

Correct model Cell Support

1250 1260 1270 1280 1290 1300 1310 1320 13304000

5000

6000

7000

8000

9000

10000

11000

12000

13000


TWT (ms)

Imp

ed

an

ce (

m/s

*g/c

c)

Original

Vertical Scale-up Only

Correct model Cell Support


Deterministic Inversion4ms Block size (λ/8) @ 60Hz

1250 1260 1270 1280 1290 1300 1310 1320 13304000

5000

6000

7000

8000

9000

10000

11000

12000

13000


TWT (ms)

Imp

ed

an

ce (

m/s

*g/c

c)

Original

Seismic Support

1250 1260 1270 1280 1290 1300 1310 1320 13304000

5000

6000

7000

8000

9000

10000

11000

12000

13000


TWT (ms)

Imp

ed

an

ce (

m/s

*g/c

c)

Original

Seismic Support


Deterministic Inversion

• The impedance information at low frequencies is simply an interpolation of the well data and so deterministic inversions should not be used to condition reservoir models

Wells

Frequency

Seismic


Deterministic InversionConstraining Porosity

SGS PorositySGS Porosity

Collocated PorosityCollocated Porosity

Seismic ImpedanceSeismic Impedance

• Seismic property has similar values vertically over zone of reservoir model

• Collocated co-simulation follows low frequency trends of seismic impedance

• Low frequency trends are from wells not seismic

• If trends were valid, constraint should be to vertical average, not cell by cell


Reservoir Model Conditioning with Seismic Data

• Conditioning a reservoir model to a deterministic inversion is largely equivalent to conditioning to a map of the wells

• If model cell thickness ≈ seismic resolution (eg λ/4) then we can condition a reservoir model to– seismic attributes

– relative or coloured impedances

– deterministic inversion filtered to remove low frequencies

• This makes the assumption of first order stationarity of the mean ie no lateral trends


Reservoir Modelling with Stochastic Inversion

• Condition model to realisations of impedance generated through stochastic seismic inversion– Incorporates geophysical uncertainty in the reservoir model

– Sample rate closer to required cell thickness

• Stochastic inversion is– Spatially constrained, typically by a variogram

– Mean of 100+ realisations = deterministic inversion

– Each realisation honours the well data

– Forward convolution of each realisation matches seismic


Stochastic Inversion and Resolution

• Common misconception related to resolution– “It…allows substantially increased resolution, capturing

details well beyond seismic bandwidth”.

• Stochastic seismic inversion can be run at any required output sample rate

• This does not imply a higher resolution– resolution is controlled by the (limited) frequency content and

bandwidth of the seismic conditioning data

• Stochastic seismic inversion simulates the broad band impedance and so properly represents the uncertainty in the seismic inversion


When Should You Use Stochastic Inversion?

D > /2

D <= /4

Continuous Change

DeterministicInversion

DeterministicInversion

StochasticInversion

StochasticInversion

Continuous andConformal layers


Stochastic Inversion

• Principal of producing non-unique realisations of impedance rather than an average impedance

• Condition impedance realisations to seismic trace & wells– SGS plus accept/reject (Haas & Dubrule, 1994)

– Other MCMC methods (eg Moyen et al, 2007)

– Good reliable methods but relatively slow

• To be of any use in calculating probabilities and statistics we need to generate many realisations - at least 100+– Computational runtimes long so fast methods required

• MPSI UltraFast FFT direct method used here


Performance BenchmarkUnderlying Technology

• Moyen & Doyen (2008) IP/IS stochastic inversion– 35 Million cells x 25 Realisations

– 70 Hours on dual CPU 64-bit machine

• MPSI 32-bit pre-stack batch technology– Approximately 2 hours for same problem & hardware

• MPSI recent service project (IP/IS pre-stack)– 240 Million cells x 100 Realisations

– 27 hours on quad CPU PC

– Moyen & Doyen estimated time = 40 days

• MPSI 3-term simultaneous pre-stack stochastic inversion now available (IP/IS/Rho)


Stochastic InversionRealisation 1



Thick SandThick Sand


Deterministic InversionThick Sand Geobody


Stochastic InversionThick Sand Geobody 01


Thick Sand Volume

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100000 120000 140000 160000 180000 200000 220000 240000 260000 280000 300000

Volume

Cu

mu

lati

ve P

erce

nta

ge

Deterministic Sand VolumeDeterministic Sand Volume

For a thick sand the deterministicvolume estimate plots close to themean volume from the realisations

For a thick sand the deterministicvolume estimate plots close to themean volume from the realisations



Thin SandThin Sand


Deterministic InversionThin Sand Geobody


Stochastic InversionThin Sand Geobody 08


Thin Sand Volume

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000

Volume

Cu

mu

lati

ve P

erce

nta

ge

Deterministic Sand VolumeDeterministic Sand Volume

For a thin sand the deterministicvolume estimate is biased (±)

For a thin sand the deterministicvolume estimate is biased (±)


Deterministic InversionWell08 Connected Net Pay

Well08


Stochastic InversionMultiple Realisations

• Multiple realisations from stochastic seismic inversion can be used for– Volumetric uncertainty

– Connectivity Analysis

– Individual realisations reservoir model conditioning

– Net Pay estimation: P90 / P50 / P10


Stochastic InversionThin Sand Connectivity 08

Well08



Well08


Stochastic InversionWell08 P90 Connected Net Pay

Well08



Well08


Probability and Connectivity

• Probability maps by themselves are not always useful as they do not indicate possible connectivity

• Geobody (connectivity) calculations followed by net pay or probability calculation much more useful– Spatial geometry of pay analysed

– Volumetric uncertainty accounted for

– Swept/contacted volume uncertainty

– Evaluation of risk in well planning


Oil Sand Probability MapMax Below Top Res

0.15

1.0

Pro

ba

bil

ity


D3 Heel: Maximum Connected Probability

0.02

1.0

Pro

ba

bil

ity


D3 Heel: Connected Probability Section

W E

0.02

1.0

Pro

ba

bil

ity

Low Chance of ConnectivityLow Chance of Connectivity


Oil Sand Probability MapMax Below Top Res

0.15

1.0

Pro

ba

bil

ity


D3: Maximum Connected Probability

0.02

1.0

Pro

ba

bil

ity

Risk of Missed PayRisk of Missed Pay


Summary 1

• Significant scale changes are not properly accounted for in reservoir modelling software

• Deterministic inversion may be useful when:– Reservoir interval has strong reflectivity

– Lots of wells and good seismic horizon constraints

– Reservoir layering is relatively thick with well defined units close to the seismic resolution limit

– Minimal lateral or vertical trends


Summary 2

• Deterministic seismic inversion data is not suitable for conditioning reservoir models– Low frequency model is an artefact of well gridding

– Scale change between seismic and model cells

– Not suitable for connectivity / volumetric computations

• Stochastic inversion benefits– What if Scenario analysis eg well track risks

– Geophysical uncertainty input to reservoir modelling

– Scale change better handled in reservoir models

– Connectivity and volumes more reliable

• Further discussion available at Booth 137


http://www.sorviodvnvm.co.uk

4 June 2014© Earthworks Environment & Resources Ltd. All rights reserved1 Reservoir Connectivity...

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Transcript of 4 June 2014© Earthworks Environment & Resources Ltd. All rights reserved1 Reservoir Connectivity...