Integration of seismic data

1 - Classification: Internal 2010-06-10

Integration of Seismic Data and Uncertainties in the Facies ModelP. Nivlet*, S. Ng, M.A. Hetle, K. Børset, A.B. Rustad (Statoil ASA),P. Dahle, R. Hauge & O. Kolbjørnsen (Norwegian Computing Center)

72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 20102 -

Motivation: 3D reservoir modelling

3D reservoir model3D reservoir model

Reservoir Reservoir simulationssimulations

Production dataProduction data


The Snorre field

• Location:

Blocks 34/4 and 34/7 in the Tampen area, in the northern part of the North Sea (191 km2)

• Production start: 1992

• Production

(2009): ~180,000 bbl/day


Motivations: Data integration

3D reservoir model3D reservoir modelWell log data

seismic amplitudes (angle-stacks)

Structure, stratigraphy


Challenges in integrating the data

• Multi-scale issue


Challenges in integrating the data

• Non-unique relationship between seismic amplitudes and geology

• A multivariate problem

2.0

1.7

Vp/V

s

Shale

AI (g/cm3.m/s)6,000 10,000

Sand


The data uncertainty challenge

• Random noise• Acquisition / Processing footprint • Angle Misalignments• Imperfect physical model


Geological setting

1,000 m

•Reservoir depth:

2-2.7 km


Traditional workflow

Seismic attribute (depth)

Facies model

Reservoir grid (depth)

integration

Well facies+extracted seismic attribute

geometry

conditioning


Proposed workflow

Seismic attribute (depth)

Facies model


integration


geometry

conditioning


Workflow from inversion to facies prediction

Seismic (partial angle-stacks) Inversion

m

Bayesian wavelet extraction

Seismic facies analysis

Vp

Vs

ρ

m

Facies probability

Decreasing probability

of shale

Increasing probability of shale

BCUBCU

SN LL

OWCLunde

SN ML

Lomvi Fm

34/434/4--11


of shale


BCUBCU

SN LL

OWCLunde

SN ML

Lomvi Fm

34/434/4--11

Seismicmodelling

mBG mS mHF

=


Geostatistical seismic inversion

• 1D modelling of seismic amplitudes (Aki&Richards’ model): linear in m=(log(vp ), log(vs ), log))

• Normal distribution of elastic properties m

• Data (e ) stationary uncertainties estimated from analysis of amplitudes

• Prior (m ) stationary uncertainties estimated from well log analysis

nGmd

mm|d = mBG +m

G*(Gm

G* + e

)-1(d -

GmBG )

m|d

= m

-

m

G*(Gm

G* + e

)-1G m


Advantages/limitations of the technique

Lateral correlations

- Different stratigraphy settings

- Grid built from max. 2 horizons

Stationary uncertainty model:

- Global matrix

- Lateral correlations

- Vertical correlations


Inversion result: Elastic properties


Impact on elastic parameter uncertainties

Frequency (Hz)

0 20 40 60

0

-50

0 10 20 30

Prior Posterior uncertainty variation (%)

AI

Vp

Rho

SI

Vs

Vp/Vs

Prior Posterior uncertainty variation AI (%)

Seismic bandwidth (Near)


Inversion results QC

Multivariate correlation (RV ) between band- pass well-logs and inversion results

35% of wells RV > 0.8 33% of wells 0.8 > RV > 0.7 32% of wells RV < 0.7

Well

Inversion

AI SI Rhob

100

ms

Band-pass filtered


Workflow from inversion to facies prediction

Seismic (partial angle-stacks) Inversion

m

Bayesian wavelet extraction

Seismic facies analysis

Vp

Vs

ρ

m

Facies probability


of shale


BCUBCU

SN LL

OWCLunde

SN ML

Lomvi Fm

34/434/4--11


of shale


BCUBCU

SN LL

OWCLunde

SN ML

Lomvi Fm

34/434/4--11

Seismicmodelling

mBG mS mHF

=


Supervised seismic facies analysis

p(m

| Sand)p(Sand | m)

Kernel estimator


Supervised seismic facies analysis

μ

m|d = μm +(I- Σm/d Σm-1)(m

– μm ) + e*

Raw Well logsRaw Well logs

Filtered well logsFiltered well logs

Inversion results at well positionInversion results at well position

Inversion filtered well logsInversion filtered well logs

Different resolution scales


Cross plots: Inversion filtered well logs

Inversion frequency filtered Predicted SAND probability

Vp/V

s

AI (g/cm3 m/s)6,000 10,000

2.0

1.7V

p/Vs

AI (g/cm3 m/s)6,000 10,000

2.0

1.7

0

1

Shale

Sand


Seismic facies analysis: Sand probability results

Sand probability


Inversion results QC: Finding optimal well position

Confidence index (khi2): Vertical sand proportion from well compared with seismic sand probability100

ms

Seismic sand probability section


Facies probability QC

31% of wells: Good confidence 61% of wells: Medium 8% of wells: Bad confidence


Inversion results QC

Potential factors impacting mismatch

Stratigraphic level

Position with respect to OWC

Presence of faults

Average shale proportion

++

+

+

+


3D confidence index• Measurement of prediction

• Weighting function in facies modelling

0

1

WellInversion result

Confidence[0,1]


Proposed workflow

Facies model


integration


geometry

conditioning


Snorre: Average proportion of channelAverage map estimated from 8 realizations

0

1

0

1


Concluding remarks

• Integrated workflow from seismic inversion to consistent seismic constrained facies modelling

• Fast geostatistical inversion approach and facies prediction

• Consistent resolution between inversion results and facies probabilities gives realistic predictions and facies models


Concluding remarks: Further work

• How to refine the upscaling of elastic parameters from well log to seismic scales? How to have a more local approach?

• Constraining observed 4D signals by using predicted facies sand probability (Ayzenberg and Theune, “Stratigraphically constrained seismic 4D inversion” M017, Room 127/128, Wednesday, 9h30)

• Flow simulations of constrained facies models and history matching with 4D for more predictive production prognoses


Acknowledgements

Thanks to Statoil, Norwegian Computing Center and the Snorre partners

Petoro, ExxonMobil Norge, Idemitsu Petroleum, RWE Dea Norge, Total E&P Norge and Amerada Hess Norge

for discussions and permission to publish this work.


Integration of Seismic Data and Uncertainties in the Facies Model

Philippe NivletPrincipal Geophysicist –Petek [email protected], tel: +47 958 16 589www.statoil.com

Thank you

Integration of seismic data

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