OBTAINING LOCAL PROPORTIONS FROM INVERTED SEISMIC DATA
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OBTAINING LOCAL PROPORTIONS FROM INVERTED SEISMIC DATA TOWARD PATTERN-BASED DOWNSCALING OF SEISMIC DATA. Lisa Stright and Alexandre Boucher School of Earth Sciences STANFORD UNIVERSITY. Multiple-point geostatistics - SNESIM. A = Categorical Variable B = Training image C = Seismic Probability.

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Lisa Stright and Alexandre Boucher School of Earth Sciences STANFORD UNIVERSITY

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Lisa stright and alexandre boucher school of earth sciences stanford university

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

Lisa Stright and Alexandre BoucherSchool of Earth SciencesSTANFORD UNIVERSITY


Multiple point geostatistics snesim

Multiple-point geostatistics - SNESIM

A = Categorical Variable

B = Trainingimage

C = Seismic Probability

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


Multiple point geostatistics with soft data

Multiple-point geostatistics with soft data

1

Probability

0

Seismic Attribute

A = Categorical Variable

B = Trainingimage

C = Seismic Probability

P( A = channel | B = TI ) = 4/5 = 80%

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

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

1

0

P( A | B, C )

- Combine with Tau Model

- Use dual training images


Scaling and probabilities

Scaling and probabilities?

#1 #2 #3

PSand

47%

47%

47%

47%

20%

20%

20%

20%

SeismicAttribute

1

Probability

0

Seismic Attribute

Realization(s)

Data

Calibration


Assumptions scale

Assumptions – Scale???

Well

190

180

170

160

150

140

130

~ 100 m

120

110

100

90

80

70

Model scale

60

~ 100 m

50

Meters to 10’s of meters

40

30

?

20

1 m

10

0

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

10’s of meters

Probabilities and Facies can be scaledto 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)

(10’s)meters

Seismic


Proposed approach or methodology

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

    Local Proportions from seismic attributes

    ?

    Seismic Attributes

    Seismic Attribute #2

    Seismic Attribute #1

    • Directly from calibration

    • From forward modeling

    Realization(s)

    Data

    Calibration


    Validation upper cretaceous cerro toro formation magallanes basin

    Validation: Upper Cretaceous Cerro Toro Formation, Magallanes Basin


    Wildcat lithofacies

    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

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

    Bierbaum 1

    17km

    10km

    AI (g/cm3m/s)

    5000

    13000


    Multi scale multi attribute calibration

    Multi-scale, multi-attribute calibration

    2.2

    2.2

    2.1

    2.1

    2

    2

    1.9

    1.9

    1.8

    1.8

    1.7

    1.7

    1.6

    1.6

    1.5

    1.5

    1.4

    1.4

    4

    6

    8

    12

    10

    4

    6

    8

    12

    10

    Vp / Vs

    Acoustic Impedance (g/cm3 m/s)


    Create synthetic properties markov chains

    Create synthetic properties: Markov Chains

    2.2

    2.2

    2.1

    2.1

    2

    2

    1.9

    1.8

    1.9

    1.7

    1.6

    1.5

    1.8

    Vp / Vs

    1.4

    4

    6

    8

    12

    10

    1.7

    1.6

    1.5

    1.4

    4

    6

    8

    12

    10

    Acoustic Impedance (g/cm3 m/s)

    Synthetics


    Forward and inverse modeling

    Forward and Inverse Modeling

    15 Hz

    25 Hz

    50 Hz


    Realizations

    Realizations

    ThinBeds(s)

    Sandstones(s)

    Conglomerate(s)


    Outcrop results local proportions

    Outcrop results: Local Proportions

    Prediction “good” when mean bed thickness

    is at least 1/10 of seismic resolution


    Subsurface application single well

    Subsurface Application: Single Well

    13000

    6000


    Subsurface application log validation

    Subsurface application: log validation

    Realization #

    Proportion

    Is

    Ip

    Vp/Vs


    Subsurface application single well1

    Subsurface Application: Single Well

    13000

    6000

    1

    0


    Stratigraphic layer 3

    Stratigraphic Layer 3

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

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

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

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


    Compiling patterns from each layer

    Compiling patterns from each layer


    Summary and conclusions

    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

    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


    Lisa stright and alexandre boucher school of earth sciences stanford university

    Acknowledgements

    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


    References

    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.  


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