U.S. patent application number 09/948070 was filed with the patent office on 2002-04-11 for method for seismic facies interpretation using textural analysis and neural networks.
Invention is credited to May, Steven R., West, Brian P..
Application Number | 20020042677 09/948070 |
Document ID | / |
Family ID | 22890071 |
Filed Date | 2002-04-11 |
United States Patent
Application |
20020042677 |
Kind Code |
A1 |
West, Brian P. ; et
al. |
April 11, 2002 |
Method for seismic facies interpretation using textural analysis
and neural networks
Abstract
Seismic facies are identified in a volume of seismic data,
wherein, first, a plurality of initial textural attributes
representative of the volume of seismic data are calculated. Next,
a probabilistic neural network is constructed from the calculated
initial textural attributes. Then, final textural attributes are
calculated throughout the volume of seismic data. Finally, the
calculated final textural attributes are classified using the
constructed probabilistic neural network.
Inventors: |
West, Brian P.; (Houston,
TX) ; May, Steven R.; (Missouri City, TX) |
Correspondence
Address: |
C. R. Schweppe
ExxonMobil Upstream Research Company
P.O. Box 2189
Houston
TX
77252-2189
US
|
Family ID: |
22890071 |
Appl. No.: |
09/948070 |
Filed: |
September 6, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60236577 |
Sep 29, 2000 |
|
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Current U.S.
Class: |
702/14 |
Current CPC
Class: |
G01V 2210/48 20130101;
G01V 1/32 20130101 |
Class at
Publication: |
702/14 |
International
Class: |
G01V 001/28 |
Claims
What is claimed is:
1. A method for identifying seismic facies in a volume of seismic
data, comprising the steps of: (a) calculating a plurality of
initial textural attributes representative of the volume of seismic
data; (b) constructing a probabilistic neural network from the
calculated initial textural attributes; (c) calculating facies
classifications in a portion of the volume of seismic data; (d)
repeating steps (a) through (c) until the calculated facies
classifications in the portion of the volume of seismic data are
satisfactory; and (e) calculating facies classifications throughout
the volume of seismic data using the constructed probabilistic
neural network.
2. The method of claim 1, wherein the step of calculating initial
textural attributes comprises the steps of: selecting at least one
cross-section of the volume of seismic data; constructing a
plurality of polygons on the selected cross-sections; and
calculating initial textural attributes from images in the
constructed polygons.
3. The method of claim 2, wherein the step of calculating textural
attributes comprises the steps of: constructing Gray-Level
Co-occurrence Matrices from the images in the constructed polygons;
and calculating initial textural attributes from the constructed
Gray-Level Co-occurrence Matrices.
4. The method of claim 3, wherein the step of constructing
Gray-Level Co-occurrence Matrices further comprises the step of:
constructing a volume of dip values from the volume of seismic
data; and applying dip-steering using the dip values.
5. The method of claim 1, wherein the step of calculating textural
attributes comprises the steps of: positioning a moving window
throughout the volume of seismic data; and calculating the textural
attributes in the moving-window.
6. The method of claim 1, further comprising the step of:
constructing a volume of confidence values from the constructed
probabilistic neural network.
7. The method of claim 6, further comprising the steps of:
selecting a confidence level; and adjusting the size of the
moving-window to keep the confidence values above the selected
confidence level.
8. The method of claim 1, further comprising the step of:
displaying the classified textural attributes.
9. The method of claim 1, wherein the seismic data comprises
seismic amplitudes.
10. The method of claim 1, wherein the seismic data comprises
seismic attributes.
Description
[0001] This application claims the benefit of U.S. Provisional
Application No. 60/236,577 filed Sep. 29, 2000.
FIELD OF THE INVENTION
[0002] This invention relates generally to the field of geophysical
prospecting. More particularly, the invention is a method of
characterizing and mapping seismic facies in seismic data.
BACKGROUND OF THE INVENTION
[0003] Seismic facies analysis is an important step in the
interpretation of seismic data for reservoir characterization.
Seismic facies interpretations play a significant roll in initial
basin exploration, prospect evaluation, reservoir characterization,
and ultimately, field development. A seismic facies is a
stratigraphic unit or region that has a characteristic reflection
pattern distinguishable from those of other areas. Regions of
differing seismic facies are usually delineated using descriptive
terms that reflect large-scale seismic patterns such as reflection
amplitude, continuity, and internal configuration of reflectors
bounded by stratigraphic horizons.
[0004] The application and scale of seismic facies analysis varies
significantly, from basin wide applications to detailed reservoir
characterization. On a basin-wide scale, reconnaissance seismic
facies analysis has been applied in the study of hydrocarbon
systems to broadly identify regions of source, reservoir, and
seal-prone regions. These regions are usually identified on the
basis of their reflection geometry as well as amplitude strength
and continuity. Regionally high-amplitude, semi-continuous
reflectors are often used to identify potential hydrocarbon-bearing
reservoirs, such as deep-water channels, while low-amplitude
continuous to semi-continuous regions can be used to identify
seal-prone units.
[0005] Seismic facies analysis can also be applied within a single
reservoir to help constrain a detailed physical-property
characterization. In these local-scale applications, definitions of
continuity and amplitude generally do not have strict definitions,
and are based on rock property calibration or environment of
deposition interpretations. Assuming a relationship between seismic
character and physical properties can be demonstrated, seismic
facies volumes can then be used to predict rock property
distribution and condition geologic models.
[0006] The standard technology used for seismic facies analysis and
mapping is a manual process where the seismic interpreter makes
visual decisions about the character of the seismic reflection data
within an interval of interest and plots these on a map. Seismic
facies are then used for a variety of purposes, but primarily to
interpret the distribution of lithofacies and rock properties. A
skilled interpreter's perception, intuition, and experience
contribute significantly to the success of seismic facies studies.
However, these same strengths can also cause seismic facies
analysis to be a subjective, time consuming, and often laborious
task. Several related techniques have been used in the oil industry
to automate and enhance the interpretation of seismic facies from
seismic data.
[0007] R. J. Matlock and G. T. Asimakopoulos, "Can Seismic
Stratigraphy Problems be Solved Using Automated Pattern Analysis
and Recognition?", The Leading Edge, Geophys Explor, Vol. 5, no. 9,
pp. 51-55, 1986 lay out a conceptual framework for training of an
algorithm, and thus automation, of the seismic interpretation
process. However, these authors do not demonstrate any working
prototype or describe any specifics of the possible attributes or
classification algorithms.
[0008] R. Vintner, K. Mosegaard, et al., "Seismic Texture
Classification: A Computer-Aided Approach to Stratigraphic
Analysis", SEG International Exposition and 65th Annual Meeting,
paper SL1.4, Oct. 8-13, 1995 and R. Vintner, K. Mosegaard, I.
Abatzis, C. Anderson, V. O. Vejbaek, and P. H. Nielson, "3D Seismic
Texture Classification", Society of Petroleum Engineers 35482,
1996, discuss textural analysis of seismic data as well as
classification of textural attributes using a version of
principal-component analysis and probability distributions. These
publications, while using textural analysis methods on seismic
data, do not take advantage of probabilistic neural networks or the
dynamic use of probability values to optimize the classification.
These methods also do not utilize an interactive training scheme
and the textural analysis is not dip-steered. The process of
guiding a calculation by the stratigraphic layering defined by the
dip of the seismic reflectors is called dip-steering.
[0009] D. Gao, "The First-Order and the Second-Order Seismic
Textures: Implications for quantitative Seismic Interpretation and
Hydrocarbon Exploration", 1999, describes the use of standard
textural analysis to produce seismic textural attributes that
quantify reflection strength, continuity, and geometry. This
abstract does not, however, describe methods of classification of
textural attributes. Specifically, Gao, 1999, this does not use a
probabilistic neural network nor interactive interpreter training
of the neural network. Additionally, the textural analysis is not
dip-steered.
[0010] Turhan Taner, in combination with Rock Solid Images and the
Consortium for Computation and Interpretive Use of Seismic
Attributes, employs a method in which various seismic attributes
are used to interactively train a neural network. However, textural
attributes are not used and the network employed is a
fully-connected back-propagation neural network, rather than a
probabilistic neural network.
[0011] P. Meldahl, R. Heggland, P. F. M. de Groot, and A. H. Brill,
"The Chimney Cube, an Example of Semi-Automated Detection of
Seismic Objects by Directive Attributes and Neural Networks: Part
I; Methodology", "The Chimney Cube, an Example of Semi-Automated
Detection of Seismic Objects by Directive Attributes and Neural
Networks: Part II; Interpretation", and British Patent with
International Publication No. WO 00/16125, "Method of Seismic
Signal Processing" use seismic attributes to interactively train a
neural network and produce a facies volume. However, in the
training and production of the chimney cube, only one class of
item, instead of multiple classes, is focussed on and classified at
a time. Accordingly, only two final output nodes are used in the
neural network architecture. A probability cube is computed and
then, as a post-processing phase, on-off thresholds are drawn to
decide if the object is of the class of interest or not. A complex
Wigner-Radon transformation scheme is used for dip-steering the
seismic attributes. The attributes are manually chosen for
individual classes.
[0012] Elf Acquitaine, "Automatic Seismic Pattern Recognition", FR
2738920 19970321 and EP 808467 19971126, describe a seismic
trace-based method for seismic pattern recognition. Each seismic
trace within a user-defined interval is decomposed into a
user-defined number of empirical-orthogonal functions. These
derived functions are then classified using a neural network based
classification algorithm, rather than interpreter-trained textural
analysis.
[0013] Thus, there exists a need to generate, in a computationally
efficient manner, a process that enables the rapid, objective
classification of seismic data so that it can be exploited in the
seismic facies mapping process. This process must also mimic the
process employed by and results obtained manually by the seismic
interpreter.Text
SUMMARY OF THE INVENTION
[0014] The present invention is a method for identifying seismic
facies in a volume of seismic data. First, a plurality of initial
textural attributes representative of the volume of seismic data
are calculated. Next, a probabilistic neural network is constructed
from the calculated initial textural attributes. Then, final
textural attributes are calculated throughout the volume of seismic
data. Finally, the calculated textural attributes are classified
using the constructed probabilistic neural network.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The present invention and its advantages may be more easily
understood by reference to the following detailed description and
the attached drawings in which:
[0016] FIG. 1 is a flow chart illustrating the method of an
embodiment of the present invention;
[0017] FIG. 2 is an example seismic cross-section from a regional
study;
[0018] FIG. 3A shows polygons selected for textural analysis of the
example shown in FIG. 2;
[0019] FIGS. 3B-3G are facies classifications corresponding to the
polygons in FIG. 3A;
[0020] FIG. 4 is a facies classification section for the example
shown in FIG. 2, as calculated by the method of the invention;
[0021] FIG. 5 is a dip section used in dip-steering the example
shown in FIG. 2;
[0022] FIG. 6 is a confidence section corresponding to the facies
classification section shown in FIG. 4; and
[0023] FIG. 7 is a seismic facies volume calculated for the example
shown in FIG. 2.
[0024] While the invention will be described in connection with its
preferred embodiments, it will be understood that the invention is
not limited thereto. On the contrary, it is intended to cover all
alternatives, modifications and equivalents that may be included
within the spirit and scope of the invention, as defined by the
appended claims.
DETAILED DESCRIPTION OF THE INVENTION
[0025] The present invention is a method of recognizing and mapping
seismic facies in seismic data, particularly in seismic amplitude
data, although the method is applicable to other seismic
attributes, as well. FIG. 1 is a flow chart illustrating the method
of an embodiment of the present invention. First, in step 101, a 3D
volume of seismic data is selected. Although 3D volumes of data are
discussed, the method works equally well for 2D data sets. This
seismic data volume will be used to calculate a seismic facies
volume and corresponding confidence volumes. Preferably, the
seismic data is seismic attribute or amplitude data, including, but
not limited to, near, far, and full-stack data.
[0026] Next, in step 102, at least one cross-section is selected
from the volume of seismic data from step 101. In step 103, a
plurality of polygons are constructed on the selected
cross-sections from step 102. The polygons need not be the same
size, orientation, or from the same stratigraphic interval, but can
be distributed throughout the cross-sections and the volume in any
appropriate orientation or geometry. Preferably, the polygons are
constructed by digitizing them on a display of the selected
cross-sections.
[0027] The polygons in step 103 are constructed to contain an image
representative of a facies type from the volume of seismic data.
Preferably, enough examples of each facies type of interest should
be provided to characterize the variation present in the input
volume of seismic data from step 101. The facies types will be
represented by seismic texture. Seismic texture is a characteristic
that quantifies many aspects of the standard seismic facies
description performed by a seismic interpreter. Seismic texture is
a quantitative measure of the reflection amplitude, continuity, and
internal configuration of reflectors. Seismic textures can be
described as smooth or rough, small-scale or large-scale and are
quantified through standard statistical methods, described as
textural attributes. Seismic texture is inherently a multi-trace
seismic attribute, and thus is significantly different from many
traditionally calculated seismic attributes based on single traces.
The analysis of seismic texture thus mimics the visually-based
analysis process of a seismic interpreter in a way that traditional
attribute analysis does not. An interpreter does not examine one or
two traces at a time. Rather, the interpreter examines an ensemble
of traces as an image to render a classification. This different
analysis approach offers the potential to capture reflection
geometry within an entire region of investigation
[0028] textural analysis techniques describe the spatial
organization of pixel values within a defined region, such as the
above polygons on the cross-sections. In general, this region, the
textural analysis window, is called a "texel". One such technique
used to quantify an image's texture in a texel employs an image
transformation that results in Gray-Level Co-occurrence Matrices.
Gray-Level Co-occurrence Matrices describe the spatial
relationships between pixels of a small region within the larger
image, the texel. In practice, Gray-Level Co-occurrence Matrices
are computed in overlapping texels so that any transition between
textural classes within the entire image can be fully observed. The
overlapping texels sweep across and down through the image until
the entire image is processed.
[0029] Gray-Level Co-occurrence Matrices are matrices with
dimensions N.times.N, were N is the number of gray levels used to
quantify the image. For example, 8 bit data has 28=256 gray levels,
and a Gray-Level Co-occurrence Matrix constructed from this image
will be a matrix that has 256 rows and 256 columns. Computation and
analysis of Gray-Level Co-occurrence Matrices is an expensive
computational task, with computational requirements proportional to
N2. Each element within the Gray-Level Co-occurrence Matrix
expresses the relative frequency of occurrence of two points, with
respective pixel values i and j, at a distance D(d, .theta.) from
one another within the texel. For example, if pixel A has value i
and is a distance D from pixel B with value j, then the Gray-Level
Co-occurrence Matrix position i, j will be incremented by one. This
process is performed for each existing pixel set within the texel.
In their most general application a Gray-Level Co-occurrence Matrix
calculation can reflect both a transition in pixel values and a
direction or "grain" within an image. Textural analysis via the
construction of a Gray-Level Co-occurrence Matrix from an image
texel is effectively the two- (or three-) dimensional extension of
one-dimensional Markov Chain analysis.
[0030] The structure of seismically-derived Gray-Level
Co-occurrence Matrices can be heuristically understood. In
homogeneous regions, where homogeneity or continuity is defined in
a given direction, differences between pixel values will be low,
and the elements close to the diagonal of the Gray-Level
Co-occurrence Matrices will therefore have higher values. Less
homogeneous regions will yield higher differences between adjacent
pixel values and resulting Gray-Level Co-occurrence Matrices will
therefore have higher values further away from the diagonal.
Average pixel value also expresses itself in the Gray-Level
Co-occurrence Matrix. Regions of low amplitude have Gray-Level
Co-occurrence Matrices with values clustered near the center.
Regions with higher amplitude, on the other hand, have more
distributed Gray-Level Co-occurrence Matrix values, either along
the diagonal for continuous textures or throughout the Gray-Level
Co-occurrence Matrix in more discontinuous textures.
[0031] In step 104, initial facies classifications are provided for
the polygons selected in step 103. Examples of facies
classifications typically used in the present invention include,
but are not limited to, high amplitude continuous (HAC), high
amplitude semi-continuous (HASC), moderate amplitude continuous
(MAC), moderate amplitude semi-continuous (MASC), low amplitude
continuous (LAC), low amplitude semi-continuous (LASC), chaotic,
and transparent. Examples of the first six classifications are
shown in FIGS. 3B, 3C, 3G, 3D, 3E, and 3F, respectively. In step
105, Gray-Level Co-occurrence Matrices are thus constructed from
the images in the constructed polygons from step 103 for the facies
from step 104. Statistical transformations of these matrices then
describe the spatial relationships between pixels of a small
region. In step 106, initial textural attributes are calculated
from the constructed Gray-Level Co-occurrence Matrices from step
104, using a user-defined moving window. This calculation can
generally be called the production of the seismic texture values in
the seismic data. In order to closely mimic the process followed by
a seismic interpreter, 2D textural attributes are preferably
calculated and then filtered in time slice to mimic a fully 3D
operation. Alternatively, 3D textural attributes can also be
calculated and used to characterize the seismic facies.
[0032] Gray-Level Co-occurrence Matrices are not efficiently
interpreted directly, and are more effectively described by scalar
statistical measures, called textural attributes. Textural
attributes can be divided into first- and second-order descriptors.
First-order statistics quantify the global distribution of pixel
values within an image, and can be calculated directly from a texel
using standard statistical techniques even without an intermediate
Gray-Level Co-occurrence Matrix transformation. Average absolute
amplitude and standard deviation of amplitude values within a texel
are examples of a first-order textural attributes, and are useful
in delineating amplitude anomalies and reflection strength. Derived
attributes such as instantaneous amplitude, phase, and frequency
can also be used to produce first-order statistics.
[0033] First-order statistics are a beginning approach toward a
detailed texture quantification, and although some geophysical
regions can be coarsely defined from distinct intervals of pixel
values, in general, an individual texel cannot be adequately
described on the basis of their first-order statistics alone. For
example, a high-amplitude chaotic region of a seismic image cannot
necessarily be separated from a high- or even moderate-amplitude
continuous region using only average amplitude values.
[0034] Second-order statistics of an image quantify the spatial
relationships of pixels within the image, and are calculated via
the intermediate transform to the Gray-Level Co-occurrence Matrix.
Second-order statistics, statistics of the Gray-Level Co-occurrence
Matrix, capture trace shape characteristics, reflection geometry,
and reflection continuity, in addition to amplitude strength.
Second-order statistics of a texel are a multi-trace, image
attribute, which allows reflection geometry and continuity to be
captured through analysis of the dip-steered Gray-Level
Co-occurrence Matrix.
[0035] Textural attributes preferably used in the present invent
include, but are not restricted to, textural homogeneity, inertia
(also knows as the element-difference moment or contrast), entropy,
and energy (also known as uniformity). The mathematical expressions
of these textural attributes are given as: 1 homogeneity = i j 1 1
+ ( i - j ) 2 c ij , inertia = 1 ( n - 1 ) 2 i j ( I - j ) 2 c ij ,
entropy = 1 2 log n i j c ij log c ij , energy = i j c ij 2 ,
[0036] where cij is the ith, and jth component of Gray-Level
Co-occurrence Matrix, c, and n is the size of the matrix (squared
number of gray levels within the image). Further, to avoid matrix
elements which are larger than 1, thereby statistically equalizing
components from any single matrix on a particular attribute
calculation, the input Gray-Level Co-occurrence Matrix, c is
normalized such that: 2 i j c ij = 1.0 .
[0037] The first textural attribute, textural homogeneity,
quantifies the amount of local similarities inside the texel.
Because it is inversely proportional to (i-j)2, local textural
homogeneity will be larger for Gray-Level Co-occurrence Matrices
with elements concentrated near the diagonal. These Gray-Level
Co-occurrence Matrices correspond to textures of organized and
poorly contrasted features with only a few gray levels at the same
distance and azimuth from one another. Lower values of textural
homogeneity will correspond to larger values of the Gray-Level
Co-occurrence Matrix further away from the diagonal of the matrix,
that is many differing gray levels that the same distance and
azimuth. These characteristics make textural homogeneity
particularly useful for quantifying continuity.
[0038] The second textural attribute, textural inertia, is
indicative of the contrast of the Gray-Level Co-occurrence Matrix,
and is the opposite measure to textural homogeneity. Whereas
textural homogeneity will be low for a highly contrasted image,
textural inertia will be high.
[0039] The third textural attribute, textural entropy, measures the
lack of spatial organization inside the computation window.
Textural entropy is high when all elements of the Gray-Level
Co-occurrence Matrix are equal, corresponding to a rough texture,
and low then the texture is more homogeneous or smoother.
[0040] The fourth textural attribute, textural energy, is also
indicative of the spatial organization within the computational
window. Textural energy is lowest when all elements of the
Gray-Level Co-occurrence Matrix are equal, the opposite of textural
entropy. In this case, all or most gray levels within the
computational window are equally probable. This is characteristic
of a rough texture. Conversely, the highest values of textural
energy show the presence in the Gray-Level Co-occurrence Matrix of
high values. In this case, only a few gray levels are dominant. The
region inside this computation window is more homogeneous, or
exhibits some regular character.
[0041] In step 107, a probabilistic neural network is constructed
from the initial textural attributes, along with their associated
initial facies classifications, from steps 105 and 106,
respectively. A neural network is an interconnected assembly of
simple processing elements. The processing ability of the neural
network is stored in the connection strengths, or weights, obtained
by a process of adaptation to, or learning from, a set of training
patterns. One of the advantages of neural networks is the ability
to train or modify the connection strengths within the network to
produce desired results. In a classification application, a neural
network can be thought of as special case of a supervised
classification scheme in that the training of a neural network is a
supervised exercise. Once sufficiently trained on a number of
calibration images, the neural network can then be applied to the
remaining images in a data volume.
[0042] Computationally, the connectivity of the nodes within a
general neural network, the weights, modify an input vector of
attributes and pass the modified values on to the next layer of the
network. Through training, the weights of the network are modified
such that on a specific set of training examples, modification of
the input attribute vectors produce a desirable outcome. The
training of a network and modification of connection weights
results in the production of a decision surface for the network. A
decision surface is an n-dimensional surface that allows the
network to separate the input training data into categories. One of
the advantages of a neural network algorithm over more standard
classification schemes is the ability to produce non-linear
boundaries. Typical classification or prediction problems commonly
have only three layers, an first, input layer; a second, "hidden"
layer; and a third, output layer.
[0043] Probabilistic neural networks are parallel implementations
of a standard Bayesian classifier. A probabilistic neural network
is a three-layer network that can efficiently perform pattern
classification. Mathematically, these probabilistic neural networks
are very analogous to kriging, where proximity to known points
guide the classification and prediction of unknown points. In its
standard form, the probabilistic neural network is not trained in
the same way as the more-traditional neural network described
above. Rather, the training vectors simply become the weight
vectors in the first layer of the network. This simpler approach
gives probabilistic neural networks the advantage of not requiring
extensive training. In seismic textural analysis, for example, the
textural attributes of the training images supply weight vectors in
the first layer of the network. This results in a dramatic speed
advantage in the training phase over more traditional types of
neural network architectures, such as fully-connected back
propagation architectures. Further, a probabilistic neural network
tends to generalize well, whereas more traditional networks, even
with large amounts of training data, are not guaranteed to converge
and generalize to data not used in the training phase.
[0044] When an input pattern is presented to a probabilistic neural
network, the first, or input, layer computes distances from the
input vector to the training input vectors, and produces a vector
whose elements indicate how close the input is to a training input.
The second layer sums these contributions for each class of inputs
to produce as its net output a vector of probabilities. This leads
to another advantage of using probabilistic neural networks. This
is the ability to extract classification probabilities directly
from the second, or hidden, layer, in addition to the
classification of the maximum probability from the third, or
output, layer.
[0045] In the present invention, the input training points for the
probabilistic neural network constructed in step 107 are the
initial textural attributes from step 105 and the associated
initial facies classifications from step 106. The output from the
probabilistic neural network will be facies classifications (and a
probability volume, to be discussed below). The probabilistic
neural network could then be used to classify the entire volume of
seismic data.
[0046] However, at this point it is preferred to make a quality
control check and, if deemed necessary, to modify or completely
retrain the probabilistic neural network.
[0047] Thus, in step 108, the initial probabilistic neural network
is used to classify the facies in a portion of the volume of
seismic data from step 101.
[0048] Preferably, this portion is one of the cross sections
selected in step 102. In step 109, a determination is made whether
the facies classification of the portion of the seismic data volume
is satisfactory. If the determination is that the facies
classification is not satisfactory, then the process returns to
step 103. The training set can be modified either through deletion
of existing polygons or addition of new polygons. The probabilistic
neural network is then re-created with the modified training set,
and again checked. This ability to train and quality check the
probabilistic neural network and then interactively modify a
pre-existing training set allows the present invention to reproduce
a facies classification that an interpreter would have produced
manually. Only then will the process continue to classify the
entire seismic data volume. Thus, if the determination in step 109
is that the partial facies classification is satisfactory, then the
process continues to step 110.
[0049] In step 110, final facies classifications are calculated
throughout the volume of seismic data from step 101 using the
probabilistic neural network constructed in step 107. This produces
a seismic facies classification volume, based on the seismic
texture attributes produced from the original, user-defined
polygons.
[0050] The quality of the seismic facies volume is dependent upon
the quality of the input data. Decreasing quality of input data
often occurs with increasing depth in the subsurface. Using a
single Gray-Level Co-occurrence Matrix calculation window size for
the entire volume contributes to this negative effect. Results are
improved by varying the window size throughout the volume.
Preferably, the window size is made larger as data frequency
decreases with increasing depth. This mode works in combination
with the dynamically adjusted window size based on a user-defined
confidence level. In a further alternative embodiment to deal with
decreasing quality of seismic data, the data can be initially
filtered with a convolution or median filter to smooth the data
prior to input.
[0051] Finally, in step 111, a confidence volume is also created
from the output of the probabilistic neural network. In an
alternative embodiment, the confidence volume can be used
dynamically during the calculation of the seismic facies
classification volume in step 110. If a confidence falls below a
user-defined level, the calculation window size can be
automatically adjusted until the confidence level rises above
acceptable levels, and the facies is recalculated and reclassified
accordingly.
[0052] In a further alternative embodiment, the production of the
Gray-Level Co-occurrence Matrices in step 104 can be dip-steered.
The stratigraphic framework of a particular geologic setting is an
important aspect that is always considered, albeit unconsciously,
by the seismic interpreter. Seismic facies interpreters, for
example, do not consider continuity solely in the time-plane.
Rather, they judge continuity following the stratigraphic layering
defined by dip of seismic reflectors. Texture analysis and
construction of a Gray-Level Co-occurrence Matrix for a texel, as
described above, is dependent on the look direction or azimuth,
.theta., in which the pixels within the texel are related. Textural
analysis applied to seismic data is extremely sensitive to the
stratigraphic framework of the texel, and must also follow the
stratigraphic dip of the reflectors to properly mimic the process
performed by the human interpreter. Following the stratigraphic dip
in a Gray-Level Co-occurrence Matrix calculation maximizes the
continuity of the image as expressed in the Gray-Level
Co-occurrence Matrix. The process of guiding a calculation by
stratigraphic dip is called dip-steering.
[0053] Texture analysis requires a high degree of resolution in
stratigraphic geometry to properly steer the Gray-Level
Co-occurrence Matrix calculation. To achieve the required
resolution, the multi-trace, image, nature of the texel is
exploited, and dips within an image are estimated via a
gradient-based technique. The first step in this technique requires
calculation of the horizontal (dx) and vertical (dy) gradient of
pixel values within the image. The local dip of the reflectors is
then calculated by 3 = tan - 1 ( y x )
[0054] The ratio dy/dx has units of time per cdp. However, for
reasons of convenience, these units can be ignored and the dip can
be expressed in terms of pseudo-degrees relative to a horizontal
time-slice.
[0055] Finally, as an optional step, a user-defined median filter
can be applied to remove noise. Once reflection dip is known
everywhere within the texel, the Gray-Level Co-occurrence Matrix
calculation utilizes the dip to guide the look-azimuth for each
pixel to pixel comparison. Areas of steep dip are poorly imaged
with the non-dip-steered Gray-Level Co-occurrence Matrix
calculation method. Alternatively, the negative effects of steep
dip can be minimized by flattening or dating the volume along a
stratigraphic layer, before performing the facies analysis.
[0056] The method of the present invention does not require the use
of well data as a calibration. This is an advantage in exploration
and early development arenas where few wells are available for
well-seismic calibration. A calibration can always be, and in
general is, performed after the calculation. However, it is not
required for application of the method of the present invention.
Other methods using seismic attributes and neural networks
generally require correlations between seismic and well data.
[0057] The present invention has been used to generate seismic
facies volumes from standard seismic amplitude data. It has also
been used on volumetric AVO (Amplitude Versus Offset) attribute
data such as slope-intercept volumes.
[0058] Although multiple textural attributes are calculated and
used for the facies classification, all required attributes are
calculated as needed (on the fly) in the present invention. Thus,
only the seismic volume being classified, the facies and
probability volumes are stored at any given time. No textural
attributes or other volumes are created. This provides an advantage
in not requiring large amounts of data storage space for the
present invention.
[0059] The present invention is capable of mapping seismic facies
on a single line or through a 3D volume. The ability to transform
standard seismic amplitude or attribute volumes into seismic facies
volumes results in significant time reduction, improved accuracy,
and reproducibility within the seismic interpretation process.
Seismic facies volumes are used for general analysis of reservoir
geometry and continuity, for well placement, and to condition
geologic models for use in development planning and reservoir
management.
[0060] Example
[0061] The results of a regional study illustrate the effectiveness
of the present invention. FIG. 2 shows a seismic cross-section, as
selected in step 102 of FIG. 1. FIG. 3A shows polygons selected for
textural analysis, as used in step 103. The corresponding facies
classifications are shown in FIGS. 3B-3G, as used in step 106. FIG.
4 shows a resulting facies classification section, as calculated in
step 109. FIG. 5 shows the dip section used in dip-steering the
FIG. 6 shows a corresponding confidence section, as calculated in
step 111. Low confidence values can be observed in and near fault
zones, where stratigraphic and structural interactions complicate
the facies interpretation. Finally, FIG. 7 shows the seismic facies
volume for this example, as calculated in step 110.
[0062] It should be understood that the invention is not to be
unduly limited to the foregoing which has been set forth for
illustrative purposes. Various modifications and alternatives will
be apparent to those skilled in the art without departing from the
true scope of the invention, as defined in the following
claims.
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