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What can classification of seismic wavelet information reveal with regard to reservoir size and qual

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Supervised Classification: Oribi-Oryx field ... Oribi-Oryx. Classification did not correctly predict oil column at new well location ... – PowerPoint PPT presentation

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Title: What can classification of seismic wavelet information reveal with regard to reservoir size and qual


1
What can classification of seismic wavelet
information reveal with regard to reservoir size
and quality? Presenter Adéle Groenewald
Authors J. Feddersen, A. Groenewald P. Young
2
Seismic Classification
  • Seismic attributes widely used over 3D seismic
    surveys
  • Seismic classification vs individual attribute
    maps
  • Enables interpreter to study
  • Combination of attributes
  • In a systematic way
  • To identify similarities in lithology
  • and
  • To study spatial variation of seismic waveform
  • Lateral variations in lithology, porosity, fluid
    content

3
Seismic Classification
  • 2 case studies used to illustrate Seismic
    Classification
  • Supervised classification of seismic attributes
  • Oribi-Oryx field
  • Unsupervised classification of seismic waveform
  • E-BD2

4
Oribi-Oryx field
  • Low impedance oil bearing sandstone reservoir
  • Encased in shale

Oryx
Oribi
5
Supervised Classification
  • When to use Supervised Classification?
  • Reliable information available at wells
  • A-priori information can supplement attribute
    data
  • The Method
  • Fischers Discriminant Cluster centers are
    computed within a pre-classified set of training
    and validation data

6
Supervised Classification Oribi-Oryx field
  • A combination of Seismic attributes extracted
    along a horizon were used
  • Acoustic Impedance
  • Amplitude
  • Integrated Fluid Factor magnitude
  • Isopach
  • Instantaneous frequency


Lithology indicators
7
Supervised Classification Oribi-Oryx
fieldCombination of Attributes
8
Supervised Classification Training Phase
2-attribute example
Attribute 2
Attribute 1
From Hampson Russel manual
9
Supervised Classification Result Oribi-Oryx 4
classes
10
E-BD2 Amplitude map
  • 2D seismic Amplitude anomaly
  • High fluid factor response
  • Water bearing low velocity sandstone
  • High amplitude/Fluid factor anomaly attributed to
  • Tuning
  • Low velocity of sandstone

Oil bearing channel sandstone
Water bearing sandstone E-BD2
11
Unsupervised Classification
  • When to use Unsupervised Classification?
  • No reliable a-priori information or
  • Un-biased cluster analysis to verify existence of
    natural clusters
  • The Method
  • Competitive learning method Unsupervised
    classification of seismic waveform (VRS)

12
Volume Reflection Spectrum analysis (VRS)
Trace Reconstruction S(t)C0 C1 t C2 t2 C3
t3 .. Cn tn (min lt t lt max)
13
Volume Reflection Spectrum analysis (VRS)
11 coefficients sufficient to exactly reconstruct
the trace
14
Coefficient maps Volume Reflection Spectrum
(VRS)
15
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
First input vector
16
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
First input vector
17
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Second input vector
18
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Second input vector
19
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Third input vector
20
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Third input vector
21
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Fourth input vector
22
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Fourth input vector
23
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
First input vector
24
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
First input vector
25
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Second input vector
26
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Second input vector
27
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Third input vector
28
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Third input vector
29
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Fourth input vector
30
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
Fourth input vector
31
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
32
Training phase Competitive Learning 2-attribute
graphical example
Euclidian Distance
33
Classification phase Competitive
Learning 2-attribute graphical example
Training data Other data
34
Unsupervised Classification
  • Conscience factor
  • One output vector could win all input vectors
  • Reduces greediness from algorithm (during
    training phase)
  • Output vector that wins often bad conscience,
    withdraws from competition
  • Results in even spread of input vectors over
    output classes

35
Coefficient maps are used as the attribute maps
for classification
Now lets look at the real thing The
N-dimensional case
(X1, X2, .. XN) Represents one attribute vector
36
Artificial Neural Network Determining the
cluster centers
X1
X2
X3
(X1, X2, .. XN) Represents one attribute vector
X4
X5
XN
Output Layer (Number of classes)
Input Layer (Number of attributes)
37
Wavelet Classification Result E-BD2
Oil bearing channel sandstone
Water bearing sandstone E-BD2
38
Conclusion What does classification of wavelet
information reveal about reservoir size and
quality?
39
Conclusion What does classification of wavelet
information reveal about reservoir size and
quality?
40
Thank you
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