lisa stright and alexandre boucher school of earth sciences stanford university

Stright, SCRF Affiliates Meeting: May 1, 2009

OBTAINING LOCAL PROPORTIONS FROM INVERTED SEISMIC DATA TOWARD PATTERN-BASED DOWNSCALINGOF SEISMIC DATA

Lisa Stright and Alexandre BoucherSchool of Earth SciencesSTANFORD UNIVERSITY


Multiple-point geostatistics - SNESIM

P(A = channel | B = TI ) = 4/5 = 80%P(A = non-channel | B = TI ) = 1/5 = 20%

Journel, 1992; Guardiano and Srivastava, 1992;

Strebelle, 2000, 2002

A = Categorical VariableB = Training imageC = Seismic Probability


Multiple-point geostatistics with soft data

P( A = channel | C = Seismic ) = 70%

P( A = channel | B = TI ) = 4/5 = 80%P( A = non-channel | B = TI ) = 1/5 = 20%

P( A | B, C ) - Combine with Tau Model - Use dual training images

1

0

Seismic Attribute

Pro

bab

ilit

y

0

1

A = Categorical VariableB = Training imageC = Seismic Probability


Scaling and probabilities?SeismicAttribute

Seismic Attribute

Pro

bab

ilit

y

0

1

47

%4

7%

47

%2

0%

20

%2

0%

PSand#1 #2 #3

47

%2

0%

Data Calibration Realization(s)


Assumptions – Scale???Probabilities and Facies can be scaled

to the model grid– Seismic informs a homogeneous

package– Homogeneous package can be

represented by “most of” facies upscaling in wells

Probabilities account for inexact relationship between wells and seismic attribute(s)

(1

0’s

)mete

rs

10’s of meters

Meters to 10’s of meters

1 m

Model scale

?

Seismic

Well

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190

after Campion et al., 2005; Sprague et al., 2002, 2006

~ 1

00

m

~ 100 m


Proposed approach or methodology

Assumptions challenged when:– System is heterolithic (more than two categories)– Heterogeneities are smaller than seismic resolution (always?)– Multiple seismic attributes lumped into probabilities

Proposed Solution:• Create a multi-scale, multi-attribute well to seismic calibration• Use calibration to obtain local facies proportions at each

seismic voxel location

Advantages of proposed approach– Can use any number of seismic attributes– Not dependent upon forward modeling (but can leverage forward

modeling)– Uncertainty in tie between data types– Considers underlying cause of fine scale heterogeneity on coarse

scale measurement response– Powerful when combined with knowledge of data

(rock physics response, depositional setting and patterns)


Local Proportions from seismic attributes

Seismic Attributes

Seismic Attribute #1

Seis

mic

Att

rib

ute

#2

1) Directly from calibration2) From forward modeling

Data Calibration Realization(s)

?


Validation: Upper Cretaceous Cerro Toro Formation, Magallanes Basin

WL1 Mud Matrix Supported - Top of Slurry

WL1 Sandy Matrix Supported Top of Conglomerate

WL1 Debris Flow

WL2 Clast Supported Conglomerate

WL2 Clast Supported Conglomerate - Base of Slurry

WL3 Thin Beds - Sandstone/Mudstone/Conglomerate

WL3 Fine Grained Sandstone

WL3 Medium Grained Sandstone

WL3 Coarse Grained Sandstone

WL4 Thin Beds - Sandstone/Mudstone

WL5 Mud


Wildcat Lithofacies

Channel fill– Clast supported

conglomerate

– Conglomeratic mudstone

– Thick bedded sandstone

Out-of-channel– Interbedded sandstone

& mudstone

– Mudstone with thin sand interbeds


Rock Properties: Late Oligocene Puchkirchen Formation, Molasse Basin, Austria

Bierbaum 1

AI (g/cm3m/s)

5000 13000

10km

17km


Multi-scale, multi-attribute calibration

1.7

1.8

1.9

2

2.1

2.2

6 8 10 121.4

1.5

1.6

4

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

4 6 8 10 12

Vp /

Vs

Acoustic Impedance (g/cm3 m/s)


0.8 0.043 0.106 0.014 0.029 0 0 0 0.002 0.006

0.083 0.781 0.005 0.01 0.005 0 0 0 0 0.115

0.018 0 0.952 0.003 0.011 0.002 0 0 0.002 0.012

0.006 0 0.014 0.956 0.011 0.002 0 0 0.002 0.011

0.005 0 0.012 0.002 0.978 0.002 0 0 0.001 0

0 0 0 0 0 0.978 0.022 0 0 0

0 0 0.006 0 0.004 0.001 0.989 0 0 0

0 0 0.017 0 0 0 0 0.983 0 0

0 0 0.004 0.002 0.008 0.002 0 0 0.984 0

0.003 0.012 0.022 0.001 0 0 0 0 0 0.962

Create synthetic properties: Markov Chains

Synthetics

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

4 6 8 10 12

Vp /

Vs

Acoustic Impedance (g/cm3 m/s)

10763630

10723630

10683630

10643630

10603630

10563630

10523630

10483630

10443630

10403630

10363630

10323630

10283630

XLIL

10763630

10723630

10683630

10643630

10603630

10563630

10523630

10483630

10443630

10403630

10363630

10323630

10283630

XLIL

1.7

1.8

1.9

2

2.1

2.2

6 8 10 121.4

1.5

1.6

4


50 Hz15 Hz25 Hz

Forward and Inverse Modeling

WL1 Mud Matrix Supported - Top of SlurryWL1 Sandy Matrix Supported Top of ConglomerateWL1 Debris FlowWL2 Clast Supported ConglomerateWL2 Clast Supported Conglomerate - Base of SlurryWL3 Thin Beds - Sandstone/Mudstone/ConglomerateWL3 Fine Grained SandstoneWL3 Medium Grained SandstoneWL3 Coarse Grained SandstoneWL4 Thin Beds - Sandstone/MudstoneWL5 Mud


Realizations

Conglomerate(s)

Sandstones(s)

ThinBeds(s)


0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7

Mean Thickness (m)

Ab

solu

te E

rro

r in

Pro

po

rtio

nOutcrop results: Local Proportions

Prediction “good” when mean bed thickness is at least 1/10 of seismic resolution

WL1 Mud Matrix Supported - Top of Slurry

WL1 Sandy Matrix Supported Top of Conglomerate

WL1 Debris Flow

WL2 Clast Supported Conglomerate

WL2 Clast Supported Conglomerate - Base of Slurry

WL3 Thin Beds - Sandstone/Mudstone/Conglomerate

WL3 Fine Grained Sandstone

WL3 Medium Grained Sandstone

WL3 Coarse Grained Sandstone

WL4 Thin Beds - Sandstone/Mudstone

WL5 Mud


Subsurface Application: Single Well

6000

13000


Subsurface application: log validation

Realization #

Vp/Vs

IsIp

Proportion


Subsurface Application: Single Well

0

1

6000

13000


Stratigraphic Layer 3

Prop( Sand | Ip, Is, Vp/Vs )

Prop( ThinBeds | Ip, Is, Vp/Vs )Prop( Conglomerate | Ip, Is, Vp/Vs )

Prop( Mud/Disturbed | Ip, Is, Vp/Vs )


Compiling patterns from each layer


Summary and Conclusions

• Multi-scale, multi-attribute calibration– Extract more information from well to seismic calibration

to define inhomogeneous seismic “packages”– Explicitly handling scale differences in data to get full

information content of each data source– Aid in calibrating inexact relationship between wells and

seismic• Facies from wells/core• Multiple attributes from seismic

• Gaps of unsampled events filled with forward modeling

• Proportions and stacking patterns (vertical and lateral) need to be considered together

• Underlying “patterns” linked to better search uncertainty space


Future Work

Methodology Validation with Outcrop Models– What is the effect of seismic resolution and/or noise on the

predictions?– What controls when a proportion set is prediction correctly?

• Number of facies?• Bed thicknesses?• Stacking patterns?• Surrounding facies?

Calibration and Realizations– More intelligent selection of proportions based on spatial

relationship with adjacent cells – Leverage the tie between the proportion and the underlying

“pattern”

Determine which proportions are consistently predicted with multiple realizations and “freeze”– Analyze to better understand seismic “packages”– Remaining components defined by the model (Training Image)

Training Image generation and modeling


Industry Sponsor:Richard Derksen and Ralph Hinsch (RAG)

SPODDS Students:Dominic Armitage, Julie Fosdick,

Anne Bernhardt, Zane Jobe, Chris Mitchell, Katie Maier, Abby Temeng,Jon Rotzien,

Larisa Masalimova

Advising Committee:Stephen Graham, Andre Journel,

Gary Mavko, Don Lowe Alexandre Boucher

Acknowledgements


References

Arpat, G. B., and Caers, J., 2007, Conditional simulation with patterns, Mathematical Geology, v. 39, no. 2, p. 177-203.

Chugunova, T. L., and Hu, L. Y., 2008, Multiple-Point Simulations Constrained by Continuous Auxiliary Data, Mathematical Geosciences, v. 40, no. 2, p. 133-146.

González, E. F., Mukerji, T., and Mavko, G., 2008, Seismic inversion combining rock physics and multiple-point geostatistics, Geophysics, v. 73, p. R11.

Krishnan, S., 2008, The Tau Model for Data Redundancy and Information Combination in Earth Sciences: Theory and Application, Mathematical Geosciences, v. 40, no. 6, p. 705-727.

Liu, Y., and Journel, A. G., 2008, A package for geostatistical integration of coarse and fine scale data, Computers and Geosciences.

Strebelle, S., 2002, Conditional simulation of complex geological structures using multiple-point statistics,

Mathematical Geology, v. 34, no. 1, p. 1-21.

Stright, L., 2006, Modeling, Upscaling, and History Matching Thin, Irregularly-Shaped Flow Barriers: A Comprehensive Approach for Predicting Reservoir Connectivity, SPE 106528, in Proceedings SPE Annual Technical Conference and Exhibition, ATCE.

Stright, L., Stewart, J., Farrell, M., and Campion, K. M., 2008, Geologic and Seismic Modeling of a West African Deep-Water Reservoir Analog (Black’s Beach, La Jolla, Ca.) (abs.), in Proceedings American Association of Petroleum Geologists Annual Convention, Abstracts with Programs, San Antonio, Texas.

Zhang, T., Switzer, P., and Journel, A., 2006, Filter-based classification of training image patterns for spatial simulation, Mathematical Geology, v. 38, no. 1, p. 63-80.

lisa stright and alexandre boucher school of earth sciences stanford university

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