U.S. patent application number 10/434627 was filed with the patent office on 2003-10-23 for method of processing acquired seismic data.
This patent application is currently assigned to Den Norske Stats Oljeselskap A.S.. Invention is credited to Bril, A. H., Groot, P.F.M. De, Heggland, Roar, Meldahl, Paul.
Application Number | 20030200030 10/434627 |
Document ID | / |
Family ID | 10838776 |
Filed Date | 2003-10-23 |
United States Patent
Application |
20030200030 |
Kind Code |
A1 |
Meldahl, Paul ; et
al. |
October 23, 2003 |
Method of processing acquired seismic data
Abstract
A method of processing acquired seismic data which comprises
extracting seismic information from the acquired data in a
direction along the spatial direction of a body of interest thereby
producing directional seismic attributes. Any preferred embodiment,
the method incorporated unsupervised or supervised learning
techniques.
Inventors: |
Meldahl, Paul; (Forus,
NO) ; Heggland, Roar; (Stavanger, NO) ; Groot,
P.F.M. De; (Enschede, NL) ; Bril, A. H.;
(Enschede, NL) |
Correspondence
Address: |
Patterson, Thuente, Skaar & Christensen, P.A.
4800 IDS Center
80 South 8th Street
Minneapolis
MN
55402-2100
US
|
Assignee: |
Den Norske Stats Oljeselskap
A.S.
|
Family ID: |
10838776 |
Appl. No.: |
10/434627 |
Filed: |
May 9, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10434627 |
May 9, 2003 |
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09786905 |
Jun 15, 2001 |
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09786905 |
Jun 15, 2001 |
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PCT/GB99/03039 |
Sep 13, 1999 |
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Current U.S.
Class: |
702/14 |
Current CPC
Class: |
G01V 1/28 20130101 |
Class at
Publication: |
702/14 |
International
Class: |
G01V 001/28 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 11, 1998 |
GB |
9819910.2 |
Claims
1. A method of processing acquired seismic data which comprises
extracting seismic information from the acquired data in a
direction along the spatial direction of a body of interest thereby
producing directional seismic attributes.
2. The method as claimed in claim 1, characterized in that the
seismic information is extracted from a control volume, where the
control volume has a size, shape and orientation determined by a
typical size, shape and orientation of the body of interest.
3. The method as claimed in claim 1, characterized in that the
directional seismic attributes are used to enhance a texture of the
body of interest.
4. The method as claimed in claim 3, characterized in that the
texture enhancement step is the first step of a two step procedure,
and the second step comprises a post-processing procedure applied
to the enhanced texture volume in a direction along the spatial
direction of the body of interest.
5. The method as claimed in claim 4, characterized in that the
second step comprises applying geometrical constraints to the
enhanced texture volume of the body of interest.
6. The method as claimed in claim 1, characterized in that the
procedure is applied to multiple seismic volumes in an iterative
manner.
7. The method is claimed in claim 6, characterized by extracting
attributes in multiple volumes on each side of an extraction
point.
8. The method as claimed in claim, 3 characterized in that when
enhanced texture volumes are recognized they are extracted and
displayed for characterization.
9. The method as claimed in claim 1, characterized in that one or
more spatial filters are used to increase the signal to noise ratio
of the data.
10. The method as claimed in claim 9, characterized in that the
local direction at every seismic sample position is used to adapt
the orientation of the spatial filters.
11. The method as claimed in claim 1, characterized in that the
directional attributes are combined in an intelligent way to
enhance the difference between a particular body of interest and
background.
12. The method as claimed in claim 11, characterized in that the
directional attributes are combined using an unsupervised learning
approach.
13. The method as claimed in claim 10, characterized in that the
unsupervised learning approach is used to analyze the internal
structure of the acquired seismic data.
14. The method as claimed in claim 13, characterized in that an
algorithm used segments the data into a series of data segments,
each segment containing a combination of attributes for subsequent
interpretation.
15. The method as claimed in claim 1, characterized in that, after
a probable body of interest is detected, the size and shape of a
control volume is varied during extraction to find a control volume
to give a maximal contrast between attributes calculated over the
control volume and the same attributes calculated outside the
control volume.
16. The method as claimed in claim 1, characterized by the use of
edge detection algorithms to establish boundaries of bodies with
similar characteristics.
17. The method as claimed in claim 1 for mapping a generally
vertical feature, such as a fault or a gas chimney, which comprise
extracting seismic information from data acquired using generally
vertically orientated seismic control volumes sequentially in the
region of the vertical feature.
18. The method as claimed in claim 1, characterized by the use of
time lapse seismic techniques in order to detect changes in shape
over time of a feature, such as a reservoir.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of application Ser. No.
09/786,905 filed Jun. 15, 2001, which claims the benefit of PCT
Application No. PCT/GB99/03039, filed Sep. 13, 1999 and Great
Britain No. Application 9819910.2, filed Sep. 11, 1998.
FIELD OF THE INVENTION
[0002] The present invention is concerned with a method of
processing seismic signals in order to identify and characterize
subsurface features within geological formations. The invention is
applicable both to onshore and offshore exploration.
BACKGROUND OF THE INVENTION
[0003] In conventional 3-D seismic surveying, seismic data is
acquired along closely spaced lines to provide detailed subsurface
information. With such high density coverage, large volumes of
digital data must be recorded, stored and processed prior to
interpretation. The processing requires extensive computer
resources. When the data has been processed it is interpreted in
the form of a 3-D cube which effectively represents a display of
subsurface features. The information within the cube can be
displayed in various forms, such as horizontal time slice maps,
vertical slices or sections in any direction.
[0004] Generally, in traditional seismic interpretation, one or
more seismic events is identified and tracked to yield a set of
seismic horizons. Together these horizons are used to form the
structural framework of the subsurface in two-way time, or depth as
the case may be. All subsequent geological modeling and most of
today's seismic inversion schemes rely heavily on this framework.
For example, seismic attributes can be extracted around an
interpreted horizon and used to characterize a reservoir unit.
SUMMARY OF THE INVENTION
[0005] It is an object of the present invention to provide an
improved method of utilizing seismic data in order to provide a
more reliable means of detecting, separating and identifying
geological features.
[0006] According to one aspect of the invention, the extraction of
seismic information from the data acquired is directed or steered
along the object which is to be characterized.
[0007] This sense of directionality is lacking in current
conventional seismic volume interpretations; neither the direction,
nor the shape of the bodies is utilized in the current technology.
In general, in the present invention, the seismic volume may be
converted into a domain where a particular geological object can be
detected more easily. For example, shallow-gas sands may show up as
bright spots in an `energy` attribute volume. Volume attribute
transformations can be both single-trace and multi-trace.
[0008] Thus directional seismic attributes are used to enhance the
texture of the objects of interest. Directional attributes are here
defined as quantities derived from a set of seismic traces along
the spatial direction of the body of interest. In a subsequent
step, geometrical constraints might be applied to the enhanced
texture volume to improve the detection of the geological objects
of interest still further.
[0009] The procedure is particularly convenient for detecting gas
chimneys, but can be used equally well to detect faults, layers,
and any other type of geological objects with a spatial direction
and shape. The procedure is also suited for the detection of
reservoir changes by the use of time lapse seismic techniques.
[0010] Conveniently, the invention provides a two step procedure
aimed at detection and separation of objects from seismic data
volumes. The first step may enhance the texture of seismic bodies,
while post-processing the enhanced volume is the second step. In
both steps the spatial direction is utilized. After the enhanced
volumes are recognized they can be extracted and displayed for
characterization. The procedure can be applied to multiple seismic
volumes (reflectivity, impedance, near offset, far offset,
gradient, intercept, etc.) in an iterative manner.
[0011] According to the invention, therefore, seismic attributes
are extracted relative to the spatial direction of the objects
which it is desired to detect. For example, a gas chimney is
basically a vertical disturbance of the seismic response due to gas
seepage. To detect such an object, seismic data attributes would be
extracted in a vertical direction. This may be achieved e.g. by
extracting attributes in multiple time gates (actually multiple 3D
control volumes) above and below each extraction point.
Stratigraphic objects (layers, channels, sequences, etc.) and
faults cannot be detected as simply as vertical disturbances
because their direction varies spatially. However, if the
dominating direction in the seismic data at every sample position
is known, this direction can be used to orient the time gates or 3D
sub-volumes parallel to the direction, from which attributes are
extracted. The local dominating direction, expressed as dip and
azimuth, can be calculated at every seismic sample position in
different ways.
[0012] The number, size and separation distance of the extraction
volumes are parameters that control the importance of spatial
direction in the procedure (attribute directivity). The accuracy of
the spatial direction estimate and the attribute directivity can be
tuned to prevent degradation in the attributes. According to
another aspect of the invention, therefore, the direction and shape
of the control volumes from which attributes are extracted are
adjusted to provide an optimum combination, in dependence upon the
nature of the geological features which is to be detected.
[0013] Not only is the directivity of the attributes important but
also the type and combination of attributes may be an important
factor in the procedure. Preferably, only attributes that enhance
the difference between objects and background are elected. Multiple
attributes, possibly extracted from different seismic volumes may
subsequently be combined to yield optimal separation.
[0014] Hundreds of seismic attributes are nowadays available on
seismic workstations. These include the following types with
potential for use in the method of the present invention:
[0015] a) seismic amplitudes at sample positions (i.e. the raw
trace data)
[0016] b) instantaneous attributes: amplitudes, phase and
frequency
[0017] c) pre-stack attributes: intercept and gradient energy
[0018] d) trace to trace similarity
[0019] e) minimum and maximum amplitudes and areas
[0020] f) local dip & azimuth (used to steer the extraction
volumes)
[0021] g) the number of sign changes in the derivative of the
seismic traces (a new attribute).
[0022] Which of these or other attributes are chosen to enhance the
texture of an object will depend upon the nature of the objects and
its image quality. Gas chimneys and faults for example will
generally exhibit lower trace-to-trace similarity than
stratigraphic objects. This is because the images of faults and gas
chimneys are degraded due to limitations in acquisition and
processing. Complex overburden effects for example, cannot be
removed properly from the seismic image by current processing
technology. Also the spatial sampling in the acquisition pattern
degrades the resolution and signal to noise ratio of gas chimneys
and faults.
[0023] In general, stratigraphic objects tend to be less degraded
than other objects. This is mainly due to the fact that seismic
acquisition and processing techniques are currently tuned to focus
on horizontal and mildly dipping objects, rather than vertical, or
steeply dipping events. With these considerations in mind it is
logical to use trace-to-trace similarity as one of the attributes
to enhance the difference between gas chimneys (or faults) and
their surroundings. Other attributes with separation power could be
`energy` and `instantaneous frequency`.
[0024] In general, the selection of attributes would be based on a
study of the object and its characteristics and/or by an evaluation
of the separation strength in the attribute control volumes and/or
a combination of these. Each attribute in itself has separation
power but maximum separation may be achieved by optimally combining
the total set of attributes.
[0025] According to another aspect of the invention, a method of
mapping a fault comprises extracting seismic information from data
acquired using generally vertically oriented seismic control
volumes sequentially in the region of the fault.
[0026] According to a further aspect of the invention, a method of
mapping a gas chimney or other gas formation comprises extracting
seismic information from data acquired using generally vertically
oriented seismic control volumes sequentially in the region of the
surfaces of the chimney.
[0027] According to a still further aspect of the invention, a
method of mapping a stratum or layer comprises extracting seismic
information from data acquired using sequential seismic control
volumes oriented generally along the main spatial direction of the
stratum or layer.
[0028] Attributes are preferably combined in an intelligent way to
enhance the difference between bodies and background. Supervised
learning approaches can be used for this purpose. A supervised
learning approach requires a representative set of examples to
train an algorithm e.g. an artificial neural network. In this case
the seismic interpreter must identify a set of points in a control
volume representative of bodies and background. At these points the
directional attributes of choice are extracted and given to the
algorithm. The algorithm then learns how the attributes must be
combined such that an optimal classification into bodies and
background is achieved. The trained algorithm is subsequently
applied to the seismic volume(s). At every sample position the
directional attributes are extracted and given to the trained
algorithm. The output is then a classification in terms of bodies
and background.
[0029] An alternative way of combining directional attributes would
be to use an unsupervised learning approach. In unsupervised
learning, the internal structure of the data is sought. The
algorithm, e.g. an Unsupervised Vector Quantiser (UVQ) type of
neural network, segments or clusters the dataset into a number of
segments. Each segment represents a certain combination of
attributes. The geological significance of the segments then
remains to be interpreted.
[0030] The output of the first of the two preferred steps is a
texture enhanced seismic volume. This can be a single directional
attribute or a volume based on a combination of directional
attributes. These volumes can be used for interpretation. Several
post-processing options are feasible to enhance the separation
power, in the second step.
[0031] In the first step, only directivity is used to enhance the
texture of the object. According to a further aspect of the
invention, in a second step, geometrical constraints, such as shape
and dimension of the bodies, can be applied to enhance further the
separation between real objects and events with similar texture.
Spatial filters are one way of increasing the signal to noise
ratio. In the present invention, preferably, the local direction
(dip and azimuth) at every seismic sample position are used to
adapt the orientation of the spatial filter.
[0032] Another possibility to utilize existing knowledge about body
shapes and dimensions is to employ again neural network technology
or a similar technique based on supervised learning. The network
can be trained to recognize specific shapes from a subset of data
containing bodies to be detected. A catalogue of examples can be
constructed to carry over knowledge from one dataset to the next.
As with the spatial filter design, the local direction at every
sample position is preferably used when the trained network is
applied.
[0033] The final output of such geometrical constraints processing
is an object enhanced volume.
[0034] Edge detection algorithms are routinely used in image
processing to establish the boundaries of bodies with similar
characteristics. Such algorithms can be applied to both texture
enhanced volumes and object enhanced volumes. Edge detection
algorithms applied to volumes with enhanced stratigraphic bodies
provide an alternative to auto tracking of events in conventional
seismic interpretation. (Within extracted volumes, the horizon can
be "tracked" simply by defining the horizon to follow a seismic
event, such as maximum value, a zero crossing, etc.) The boundaries
can also be used as constraints for conventional auto tracking
algorithms. By the application of edge detection algorithms to
volumes with enhanced faults, the fault planes can also be mapped.
The method of the present invention also provides for the tracking
of several horizons simultaneously.
[0035] The output of edge detection algorithms are co-ordinates of
the body boundaries. Any data from any step in the entire process
according to the invention within these boundaries can be output
for display and characterization purposes. For example, some
directional attributes extracted from the volumes may show unique
patterns that can be used to tie geological units across faults. By
visual--and/or neural network based inspection of individual
bodies, the structural and stratigraphic interpretation of a.o.
layers, faults and gas chimneys can be finally determined.
[0036] After a set of volumes has been processed/interpreted, it
may be attractive to repeat the process using knowledge gained from
previous runs, or by simply focusing on special objects, regions,
etc. Thus it may be desirable to recalculate attributes in selected
bodies. This procedure is quite similar to generating horizon
consistent attribute maps, a standard function on conventional
interpretation workstations.
[0037] This form of processing/interpretation in an iterative
manner has also great potential for time lapse seismic monitoring
of, for example, reservoirs. Due to its very nature, time lapse
seismic monitoring is a repetitive process aimed at detecting
differences between volumes. In general, the volumes are recorded
at regular time intervals and the differences which are to be
detected are due to dynamic changes in a reservoir. Examples of
these changes are fluid movements, pressure changes, temperature
changes, etc. Such differences have a direction, shape and
dimension. In other words they are seismic bodies, that can be
detected and separated by the method according to the present
invention.
[0038] An important issue in the context of time lapse seismic
monitoring is repeatability. Seismic acquisition parameters, survey
parameters, environmental influences and seismic processing may
vary between successive recordings. This implies that small
reservoir changes may be virtually impossible to detect. To improve
repeatability may be very costly, or even impossible using current
technology. However, the method of the present invention is
expected to be able to cope with this problem more effectively than
conventional methods for two reasons.
[0039] Firstly, the knowledge of directivity is used to increase
detectability of changes between successive recordings and
associated difference volumes. Secondly, supervised learning
methods such as neural networks are employed, which in general
perform better than conventional techniques on noise contaminated
data. Moreover, these techniques can be used to remove the unwanted
non-repeatable noise by means of a matching process. A network can
be trained to predict the seismic response of the successive
recording whereby the training set is constructed from data points
outside the area where changes are to be expected.
[0040] The present invention is particularly suited to the
treatment of chimney cubes. In a preferred variant, the method
increases the detectability and mapping efficiency of the desired
objects by an iterative process comprising at least two steps:
contrasting (i.e. texture enhancement) followed by object
recognition.
[0041] Contrasting is performed by extracting several attributes
from multiple windows and feeding these to either a supervised, or
an unsupervised neural network. The size, shape and direction of
the extraction windows as well as the attributes are chosen in
relation to the objects we wish to detect. The windows may have a
fixed shape and direction, or they have data adaptive forms. In the
latter case they follow the local dip and azimuth of the seismic
events. The resulting output is a texture enhanced volume, which
can be interpreted manually, or used as input to the object
recognition phase.
[0042] Seismic attributes and supervised and unsupervised neural
networks have become increasingly popular in recent years in the
realm of quantitative interpretation. The present invention extends
the use of these techniques to seismic object detection. Moreover,
the concept of directivity is introduced in the attribute
extraction process.
[0043] Directive seismic source arrays have been used for may years
to attenuate unwanted signals hence increasing the contrast between
desired and unwanted energy. Since seismic acquisition must record
all desired energy the source directivity is generally weak. Also
in processing the concept of directivity is used to increase the
contrast between objects and their background. Also these
directivity processes are weak since they should not attenuate
energy from seismic objects of interest.
[0044] In this method seismic object is improved by: focusing on
one class of objects only; using directivity to extract the
attributes; and the use of neural networks to recombine the
extracted attributes into new attributes with improved separation
power. The target can be relections, faults, chimneys, seismic
anomalies or any other object of interest. The seismic texture, the
spatial extension and orientation of each of these objects is
different. Differences are both due to the seismic response and how
the data has been handled in acquisition and processing.
[0045] To detect seismic objects requires knowledge about texture,
size, shape and direction of the objects. One must ask which is
characteristic of a fault, chimney or seismic anomaly in order to
extract the best attributes. These attributes are then recombined
into even better attributes via neural network mapping so that the
objects can be detected in an optimal way. For example, faults are
in general dipping more steeply then reflectors and the seismic
response changes faster along fault planes than along reflectors.
Since fast spatial variations are mostly degraded by inaccuracies
in acquisition and processing we know that reflectors usually
contain higher temporal frequencies than fault images.
[0046] Seismic chimneys on the other hand appear as vertically
degraded zones in the seismic image. These zones can completely
mask the reflection energy from the sedimentary sequence.
[0047] Other examples of seismic objects and their characteristics
are: Direct Hydrocarbon Indicators (DHI) and stratigraphic units. A
DHI is a seismic anomaly, which is often characterized by a
horizontal component, a change in amplitude and phase and a
termination against other reflectors. A stratigraphic unit can have
many different responses. Usually the response changes along the
reflecting unit, due to changes in rock and fluid parameters.
Detecting these changes and relating these to
geological/petrophysical variations is the subject of seismic
reservoir characterization. However, if the general response of a
particular unit differs from the surrounding reflectors, this
information can be used in an alternative auto-tracking scheme.
[0048] Once the decision is made which objects are to be detected
an intelligent selection is made of attributes that have potential
to increase the contrast. Attributes can be amplitude, energy,
similarity, frequency, phase, dip, azimuth etc. Moreover,
attributes can be extracted (and merged) from different input cubes
e.g. near--and far offset stack, inverted Acoustic Impendance etc.
The attributes are made directive by the shape and orientation of
the extraction window. In chimney detection for example three
vertically oriented extraction volumes can be used to reflect that
we are looking for vertically oriented bodies of considerable
dimensions. Knowledge about the characteristics of chimneys is used
by calculating in each extraction volume such attributes as energy
and various types of trace-to-tract similarity.
[0049] In fault detection, static, vertically oriented calculation
volumes can also be used. To prevent non-vertical faults from
"falling out of" the extraction volume(s) the vertical directivity
can be reduced. Reducing the vertical extension and increasing the
horizontal extension of the extraction volumes does this.
[0050] To detect reflectors the calculation volumes may be oriented
horizontally. Again since reflectors are not perfectly horizontal
the directivity may be reduced.
[0051] Generally the extraction volumes are either cubes or
cylinders. Other forms may perform better, especially in the case
where the objects do not have a fixed direction. For example, to
detect faults, energy is an important attribute. In the ideal, it
is desirable to calculate the energy in a 2D window along the fault
plane. As the orientation of the fault plane is unknown the
directivity is reduced e.g. by using a cone shape extraction volume
to compute the energy attribute.
[0052] The ideal extraction volume follows the desired object at
every position. This implies that the extraction volume has a
flexible shape, which follows the local dip and azimuth of the
data. The local dip azimuth can be calculated in may different
ways. The inventors have found that the calculated local dip and
azimuth cannot only be used to steer the attribute extraction
volumes but it is also a perfect vehicle to remove random noise
prior to attribute extraction processes.
[0053] After the selected attributes have been extracted at a
representative set of data points these will be recombined into a
new set of attributes to facilitate the detection process. In this
step, supervised or unsupervised neural networks can be used. The
main difference between supervised and unsupervised learning
approaches lies in the amount of a-priori information that is
supplied. Supervised learning requires a representative set of
examples to train the neural network. For example networks can be
trained to find the (possible non-linear) relation between seismic
response and rock property of interest. In this case the training
set is constructed from real or simulated well data. In
unsupervised (or competitive learning) approaches, the aim is to
find structure within the data and thus extract relevant
properties, or features. The resulting data segments (patterns)
still need to be interpreted. An example of this approach is the
popular waveform segmentation method whereby waveforms along an
interpreted horizon are segmented. The resulting patterns are then
interpreted in terms of facies- or fluid changes.
[0054] In the object detection method the same principles are used.
With unsupervised learning approaches, attributes related to the
objects to be detected are used. With supervised learning approach,
not only are meaningful attributes used but locations in the
seismic cube are also identified where examples of the class of
objects to be detected are present. Seismic attributes are
calculated at these positions as well as at control points outside
the objects. The neural network is then trained to classify the
input location as falling inside or outside the object. Application
of the trained network yields the desired texture enhanced volume
in which the desired objects can be detected more easily.
[0055] Edge detection algorithms and pattern recognition tools can
then be applied to the texture enhanced volume to further improve
the detectability of the object. The concept of directivity can
also be applied in these processes.
[0056] The chimney cube is a new seismic entity. A chimney cube is
a 3D volume of seismic data, which highlights vertical disturbances
of seismic signals. These disturbances are often associated with
gas chimneys. The cube facilitates the difficult task of manual
interpretation of gas chimneys. It reveals information of the
hydrocarbon history and fluid flow models. In other words the
chimney cube may reveal where hydrocarbons originated, how they
migrated into a prospect and how they spilled from this prospect.
As such a chimney cube can be seen as a new indirect hydrocarbon
indicator tool.
[0057] Chimney interpretation is also used in geo hazard
evaluation. Correlating chimneys with mapped shallow gas indicators
may confirm the presence of shallow gas. As chimneys are signs of
partially degraded data, the cube can also be used as a quality
control tool in processing and in the evaluation of attribute and
depth maps.
[0058] Finally the cube can be used in determining acquisition
parameters. For example the success of 4C seismic depends on the
ability to undershoot gas, hence it depends on the interpretation
of chimneys.
[0059] The chimney cube whose interpretation will be described
below was created as follows:
[0060] 1. A seed interpretation was made with locations inside
manually interpreted chimneys and in a control set outside the
chimneys.
[0061] 2. At the seed locations various energy and similarity
attributes were extracted in three vertically aligned extraction
volumes around the locations (directivity principle).
[0062] 3. Step 1 and 2 were repeated to create and independent test
set.
[0063] 4. A fully connected Multi-Layer-Perceptron type of neural
network was trained to classify the attributes into two classes
representing chimney or non-chimney (output vectors 1,0 or
1,0).
[0064] 5. The trained network was applied to the entire data set
yielding outputs at each sample location. As the outputs are
complementary we passed only the output on the chimney node to
produce the final result: a cube with values between approx. 0
(no-chimney) and I (chimney).
[0065] Thus, a semi-automated method of detection of seismic
objects is provided. The method, which has wide applicability, is
seismic processing and interpretation preferably includes:
[0066] 1. Focussing on one class of objects at the time.
[0067] 2. Extraction of attributes with potential to increase the
contrast between desired object and the background.
[0068] 3. The use of directivity in the attribute extraction
process.
[0069] 4. The use of supervised and unsupervised neural networks to
recombine the attributes into new attributes with improved
separation power.
[0070] 5. The possibility to iterate the process by first enhancing
the texture of the objects then detecting them by either manual
interpretation, or automated detection after application of edge
detection and pattern recognition algorithms.
[0071] A specific application of the method is chimney cube. This
cube may add a new dimension to seismic interpretation as an
indirect hydrocarbon detector.
[0072] The mapping of seismic chimneys can be important in
exploration as hydrocarbon indicators. The chimneys indicate
present or previous vertical migration of fluids containing
hydrocarbons, and can indicate movement of hydrocarbons between
different geological sequences. There are seismic indications that
vertical migration of hydrocarbons appear periodically. The mapping
of chimneys at different levels may help to understand the
hydrocarbon migration history, the migration route between a source
rock and shallower prospects, as well as migration of hydrocarbons
between prospects, as well as migration of hydrocarbons between
prospects and shallower sediments.
[0073] As the upward migrating hydrocarbons may charge any
shallower reservoir, the mapping of chimneys also has significance
in shallow gas hazards evaluations for drilling.
[0074] Escape of fluids or gas through the seabed may generate
non-favorable conditions for seabed installations, like pockmarks,
and seabed instability. The mapping of shallow chimneys is
therefore important in field development projects.
[0075] In the past, CO.sub.2 resulting from petroleum production,
has been reinjected to the underground to prevent the release of
CO.sub.2 to the air. The mapping of possible chimneys is in such a
case important to find a suitable injection location with low risk
of CO.sub.2 migration to the seabed, as well as in time-lapse
seismic analysis for monitoring of possible CO.sub.2 migration to
the seabed during and after injection.
[0076] To better identify chimneys, seismic attributes which
increase the contrast between chimneys and the surroundings are
used. The amplitude values within chimneys are, in the majority of
cases, observed to be low, as compared to the surroundings.
Likewise, the seismic trace similarity is observed to be low within
chimneys. Attributes that can be used to increase contrast between
chimneys and the surroundings are amplitude, energy, trace
correlations, tract similarity etc. The different attributes are
input to a neural network which is trained to do a classification
into chimneys and non chimneys. The vertical extension of chimneys
is used as a criterion in the classification. As chimneys appear as
vertical disturbances in seismic data, all vertical disturbances
with the same seismic characteristics will be enhanced.
[0077] The final product is a cube where chimneys have been
quantified by assigning maximum values (high probability) to the
samples within the chimneys and minimum values (low probability) to
the samples within the surrounding volume.
[0078] Similar principles can be used to identify and quantify
fault planes and reflectors. The final cubes can be loaded into any
standard interpretation or mapping system for visualization like a
standard seismic cube. The method may be applied on 2D as well as
3D data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0079] The invention may be carried into practice in various ways
and some embodiments will now be described by way of example with
reference to the accompanying drawings, in which:
[0080] FIG. 1 is an outline procedure of a method in accordance
with the invention;
[0081] FIG. 2a is a schematic representation of extraction cubes
within a seismic control volume for the study of an inclined
object; and
[0082] FIG. 2b is a view similar to FIG. 2a for a gas chimney.
DETAILED DESCRIPTION OF THE DRAWINGS
[0083] FIG. 1 shows schematically a preferred system in accordance
with the invention. The procedure has effectively two steps. In the
first step, a seismic volume is defined and in dependence upon the
nature of what is known to be likely to be present, the appropriate
attributes are selected. These are processed using control volumes
within the seismic volume which are tailored in their shape and
directionality to suit the geological feature or body which is to
be studied. This results in an enhanced texture of the body; a
texture enhanced volume.
[0084] In the second step, shape and geometrical constraints are
applied, again using the known directionality. This results in an
enhanced separation; an object enhanced volume.
[0085] The process is then repeated on successive seismic volumes.
The entire process can also be repeated after the elapse of a
significant time interval. In this way, the development of a body,
such as a reservoir can be monitored.
[0086] The effectiveness of directional attributes can be
demonstrated with two examples. The first is horizon based, the
second is three-dimensional.
EXAMPLE I
[0087] Attributes extracted around a horizon are in principle
directional attributes. In conventional processing the orientation
of the 3-D cube used is not changed, however, in the present
invention, directionality is used to locate the extraction control
volume. For optimal use of directivity the orientation of the
control volume must be adapted to the local conditions. FIG. 2a
shows this principle. In practice, the top and bottom of the
extraction cube shown as the control volume would follow the
horizon; the extraction cube is not in fact a cube, nor a rectangle
but a flexible body with tops and bottoms parallel to the horizon.
This same concept is valid for the generalized 3-D case where the
extraction bodies follow the surface that is defined from a central
extraction point.
[0088] The difference between `conventional` and `true` directivity
for an attribute that expresses the similarity between trace
segments surrounding an extraction point can be shown by computing
the similarity in a time gate of -40 to +40 ms. In the conventional
case, the orientation of the extraction cube is constant, in the
true directivity case, the orientation follows the horizon and
results in an enhanced definition of the object.
EXAMPLE II
[0089] When gas seeps through the subsurface, it may leave a high
gas saturation trail which may show up on seismic data as a
chimney. Detection of chimneys is important from a drilling safety
perspective. Also, from an exploration point of view, there may be
a need to detect gas chimneys.
[0090] On seismic data, gas chimneys show up as vertical
disturbances. Within the chimney, the energy decreases as does the
trace-to-trace similarity (coherency). The shape of the chimney may
vary considerably. Some are cylindrical (above a mound). Others are
elongated or curved (along fractures and faults).
[0091] In this example a neural network is used to learn to
recognize chimneys from a representative set of data points which
are either inside, or outside a chimney. Input to the network is
the inline number plus a set of directional attributes extracted in
three 80 ms time gates. The direction is vertical, so the three
time gates are located above (-120,-40), around (-40,+40) and below
(+40,+120) each extraction point. In each gate, the energy of the
central trace is computed together with 4 multi-trace attributes
which express the similarity between traces surrounding the central
trace. The desired output is 1 for a chimney and 0 for a
non-chimney. The trained network is applied to the entire seismic
volume yielding a new control volume in which the texture of
chimneys has been enhanced, in this case, expressed on a scale from
0 (no chimney) to 1 (chimney). Chimneys appear in different shapes.
Shape information can now therefore be utilized, e.g. via spatial
filters and/or shape detection techniques to further improve the
chimney detection.
* * * * *