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PSAdvances
in Seismic
Fault
Interpretation
Automation*
By
Randolph Pepper 1 and Gaston Bejarano 1
Search and Discovery Article #40170 (2005)
Posted September 7, 2005
*Modified by the authors of their poster presentation at AAPG Annual Convention, June 19-22, 2005
1Schlumberger Stavanger Technology Center, Stavanger, Norway ([email protected]; [email protected])
Abstract
Since the first seismic
trace was computer-rendered, automatic
interpretation
has been the promised panacea of the geo-science community. Twenty years later,
we still struggle for a reasonable automatic
interpretation
methodology in
structurally challenging areas.
While automated horizon tracking has become quite elegant, correlating across significant fault displacements remains an obstacle. Algorithms require human intervention to guide the tracking in newly encountered fault blocks. Constraining the horizon tracking to honor pre-existing faults helps, and knowing the fault displacement further enhances this process.
Advances in edge-detection algorithms have allowed direct illumination of
faulting and seismically detectable fractures. These techniques improve manual
interpretation
, but only represent an entry point for automatic extraction of
faults.
For some geologic plays, re-sampling of the enhanced edge attribute into a
geologic model property is a simple and effective method of un-biased automated
fault interpretation
. Explicit methods to extract fault surfaces can utilize an
automatically picked horizon indirectly through analysis of “non-picks” and
gradient trends, followed by spatial correlation for vertical connectivity.
Alternatively, using the familiar techniques of seeded auto-picking, on an edge
volume, shows great promise. Flexible editing is essential with these methods.
Finally, we
examine the recent work on fault system interpretation
, which provides a
semiautomation of fault
interpretation
, elevating the interpreter’s task to the
analysis of fault systems. Incorporating new multi-horizon classification or
displacement attributes allow inference about surface connectivity with fault
throw. The final assembly of these advanced methods as “bread and butter”
interpretation
mechanics, while not completely in place, is visible on the
horizon
|
Historical Overview
The automatic tracking of
Most automatic horizon tracking applications include cross-correlation
or waveform based tracking algorithms to capture the
While the fault expression was made visible from the horizon
auto-tracking method alone, as shown in Figure 2,
the means to extract this fault information directly and automatically
was not available. A clever approach to isolate the fault information
from an auto-picked horizon is to take the inverse of the surface, i.e.
show only areas where the
An early effort for semi-automatic fault
A “seedless” approach to fault segment extraction was presented by van
Bemmel and Pepper (1999, US Patent Number 5,999,885), where the gaps and
sharp gradients from a horizon
These new edge attributes teach us that a vertical
The small additional step of executing seeded fault auto-picking on
these edge volumes is just entering the mainstream in terms of a
commercial software offering. The reason for this technology delay may
be in our historical approach of using the
The current generation of geological modelling packages treat fault
surfaces as legitimate objects in a 3D structural framework, and further
the cause of introducing more un-biased and automatic methods for the
identification and extraction of fault surfaces. Technologies for 3D
rendering, fast computation, and maturing signal processing workflows
may finally move us away from our “paper”
Enabling Technologies
Many emerging technologies contribute to our understanding of subsurface
faulting and fracturing. We recognize that much progress has been made
in the use of the shear-wave component for fracture identification, but
that’s a different story. For now, we shall focus on reviewing a
collection of enabling technologies, which highlight the advances toward
the
We hope that by this point you can accept that discontinuity processing
of
Fast volumetric signal processing is becoming a basic element of the
geoscientist’s toolkit, as evident in the barrage of technical papers
and patents related to advanced signal processing on post-stack
Marfurt et al. (1999) further develop
Many new signal-processing methods are being developed and entering
commercial packages, exploiting properties of local curvature (Roberts,
2001), local frequency variability (Partyka et al., 1999), and
Identification of faults by combining multi-attribute analysis with
neural network classification is another maturing area. Meldahl et.al.
(2001) remark that the trend is shifting from horizon-based towards
volume-based
A more sophisticated collection of attributes were used by Borgos et.al.
(2003) to isolate and capture the significant characteristic of the
Borgos, et.al., (2003) take the analysis further by including a fault displacement estimation by extrapolation of the classification results onto existing fault surfaces, and calculating the displacement as a distance along the fault surface to extrema class pairs from either side of the fault. The fault surface now contains an additional spatially variable property of displacement. Skov et.al., (2004) demonstrate the use of the fault displacement property as a component of fault system analysis. Admasu and Toennies (2004) produce a fault displacement model by performing discreet matching of prominent regions across fault planes. Aurnhammer and Tönnies introduce a genetic algorithm for non-rigid matching across faults.
These examples suggest another important element in our quest. The
integrated
S.I. Pedersen et.al. ( 2002, 2003) introduced a method known as ant-tracking, based on artificial swarm intelligence. This is an exciting method where many thousands of computational “agents” are deployed in a volume to extract a small patch of the discontinuity. The redundancy of agents over the same area reinforces and extends the extracted feature while increasing the confidence in estimate. Figure 9 shows the result of running ant-tracking on an edge volume to create both an enhanced edge volume and to automatically extract fault patches.
Another method offered by Goff et.al. (2003, US Patent Application 20030112704) extracts a fault network skeleton by utilizing a minimum path value and further subdividing a network into individual fault patches wherein the individual patches are the smallest, non-intersecting, nonbifurcating patches that lie on only one geologic fault. This introduction of a patch concept is exciting because it also introduces the idea of patch properties. We now have an additional means of segmenting our fault information.
|
Figure 10. Ant Cube viewed as time slice
to guide fault |
|
Figure 11. Fault |
|
Figure 12. Histogram and Stereonet
filtering of fault patch collections allow fault system level
|
|
Figure 13. Voxel information extracted
from structural smoothing, frequency filtering, discontinuity
processing (Variance), followed by fault enhancement
(Ant-tracking). The fault enhanced |
|
Interpretation
automation differs conceptually from automated
interpretation
. The goal of the first is to provide a tool to improve
the quality and turn-around time for
interpretation
, whereas the latter
implies a promise of providing an
interpretation
without human
intervention. While a few corporate executives may like the idea of
“click here to find oil”, the geoscientist needs a flexible software
toolset which can automate where appropriate, supplemented with manual
input when necessary, and most importantly offer a means of extracting
the desired information easily.
This desired fault information can be classified in two different forms,
implicit or explicit. An explicit representation means surfaces are
created and can then be used for framework and geologic model
construction. The simplest case here would be a traditional map of an
interpreted horizon, showing the intersection with the fault surfaces
and bounded gaps in the horizon surface, as previously shown in
Figure 3. True 3D geologic modeling requires
the additional step of fault surface intersection interpretation
to the
bound layers.
Looking at the explicit method in more detail, we can summarize an
approach to leverage the enabling technologies previously discussed. We
would like to move away from a basemap representation of our prospect to
a true 3D model representation. One limitation in the past has been the
difficulty to performing traditional interpretation
, i.e. horizon and
fault drawing, in a 3D canvas with the same ease they are currently
performed in a 2D canvas. When emulating paper
interpretation
, a 2D view
with polyline drawing functional is appropriate. If the
interpretation
paradigm changes from manual drawing to surface or volume extraction,
the 3D canvas becomes the premier choice. An efficient presentation
style for joint horizon/fault
interpretation
would be to show vertical
plane through the
seismic
amplitude cube and a timeslice view of the
discontinuity cube; see Figure 10.
For automatic extraction techniques, the seismic
data
must be
pre-conditioned either during the extraction process or as a preliminary
processing step. In addition, there may be multiple versions of the
seismic
data
or derived attributes required depending on the
interpretation
objective. For example, regional structure and major
fault
interpretation
can be performed on structurally smoothed
data
with
great benefit, but at the expense of small fault displacement expression
and a loss of subtle amplitude variations. Yet, once this regional
framework is in place, we can return to our original
data
, pre-condition
the
data
to emphasis the small features and interpret them in their best
light.
For fault extraction, the construction of a discontinuity volume allows
the direct detection of seismic
faults. We again have the option to
further condition the discontinuity
data
to emphasis large-scale
features and/or the subtle detail. Digital processing libraries that
offer directional filtering, connectivity filtering, volume
segmentation, morphology operations, and multi-volume operations can all
be utilized to further visually isolate our features of interest.
Post-processing of the discontinuity volume can further isolate the
interesting features. Processes such as skeletonizing, pruning,
thinning, and erosion (Gonzalez and Woods, 1992) can be powerful
filters. Other possibilities are iterative operations, such as running
Ant-tracking on the results of Ant-tracking.
While the commercial market has a wonderful inventory of signal
processing methods for seismic
volumes, the tools for surface extraction
from
seismic
volumes has been lacking. Seeded autotracking for faults is
not yet mainstream, but we can anticipate they will soon be widely
available. In addition, more sophisticated approaches for global
extraction of fault surfaces; e.g., AntTracking and neural net
classification methods, are also entering the marketplace and will
continue to mature. Parallel to these developments, hardware with enough
processing power to compute multi-trace attributes for larger
seismic
volumes and the corresponding disk space to persist those results have
become more affordable to users in general. If this trend continues,
then a carefully designed software platform that can host these
workflows and can provide a simple interface to control the different
steps, will surely contribute to make these newer techniques more
attractive. See Figure 11 for fault
interpretation
workflow.
These advances open the door for the geoscientist to work with the
derived fault information in more meaningful ways. One of the greatest
advantages of the migration from paper interpretation
to the workstation
was the opportunity to easily access the amplitude information from the
seismic
. This advantage can now be extended to faults. As previously
mentioned, extracted fault patches can be filtered based on their
properties (size, quality, orientation, average throw…) but this concept
can also be extended to all fault objects regardless of the method used
to extract them. Automatic and manual fault
interpretation
can be
managed on a fault system level by filtering on one or more of the
derived properties associated with the collection. New properties can be
added to estimate fault connectivity, strike length, etc., which will be
useful in support of well-based fracture network density analysis.
Schlumberger Stavanger Research developed and presented
interpretation
workflows based on system level
interpretation
of faults by utilizing
these collection of properties associated with extracted fault patches
as visual filters, S.I. Pedersen et.al. ( 2002), Borgos, et.al. (2003),
and Skov et.al. (2004). Simple histogram and orientation filtering allow
the interpreter to reduce an automatically derived collection of fault
patches into meaningful fault systems (Figure 12).
The second form of extracting the fault information is an implicit
representation, where the seismic
is re-sampled into the geologic model
as the container for the fault knowledge. A simple example here would be
to take the fault expression from discontinuity processing (or further
enhancement processing of faults), then re-sample this voxel information
into the 3D property grid model (Figure 13).
Incorporating implicit fault definitions with seismically constrained
layer property population will yield high-resolution geologic models.
Obviously, a voxel representation of a fault could be converted to an
explicit surface representation through surface modeling options, i.e.,
gridding. Implicit methods can be made more sophisticated through
advanced signal processing and custom workflows. It is not a great leap
to appreciate that the
seismic
displacement field itself would be a
valuable
seismic
attribute.
The 3D displacement field means that at any x,y,z location, we could
determine the geologically equivalent position at all other locations in
the prospect area. A novel means of constructing an implicit geologic
model would be to stochastically populate a model at log resolution, but
structurally guide the statistics along coherent orientation and across
fault breaks from the displacement estimate. The displacement field
would also be a welcome addition to volume restoration studies in
support of structural geology interpretation
. Dee et.al. (2005),
acknowledge fault correlation from
seismic
as having immediate impact on
structural geologic analysis best practices, but their perspective is
from primarily manual
interpretation
methods, and does not include the
orientation estimate available from
seismic
and the automation
processes.
An automated means of producing this displacement field would require
the combination of two separate elements. We could determine the
displacement of a continuous seismic
event by computing the local
orientation of the horizon. With the dip and azimuth computation at a
point, we could predict where the event will on the neighboring traces.
But this only will work for continuous events. When we encounter a
fault, the orientation estimate will not give us the fault throw, and in
fact we will not get a reliable orientation estimate in the vicinity of
a fault. Here we must introduce the second element of our automation
approach, which is to compute the fault throw via some method of
correlation of
seismic
events across the fault boundary. This step has
made the bold assumption that we have a priori knowledge of where these
faults are.
Much of this paper has been devoted to documenting the efforts to date in isolating the position of faults and a means of measuring the displacement across faults. See Figure 14. The various tools seem to be available to construct a workflow for creating the displacement field:
Determine the location of faults
Determine the areas of event continuity
Compute the orientation in continuous areas
Compute fault throw along fault planes
Combine orientation displacement with fault throw displacement to get 3D
displacement
Quality control to correct erroneous estimates will be necessary, but
could potentially be reduced to manual intervention in a sub-set of the
data
set, focusing the interpreter’s time and energy on the difficult
regions and let automation help us where appropriate.
Besides the attribute workflows, advances in 3D visualization and 3D
interaction capability are going to commoditize volume or geobody
extraction functionality which will include some combination of fault
extraction, horizon extraction, layer extraction, and confined volume
objects such as salt, carbonate build-ups, channels, fracture zones,
etc. These voxel bodies can be directly realized into our 3D geologic
models to freely share across the seismic
to simulation activity. For
those that wish to continue with explicit representations, these can be
derived from the voxel presentation either as surfaces or closed
volumes. The next generation workstations offering fault
interpretation
automation will combine interactive signal processing, classification
and automatic extraction of features, powerful 3D editing capabilities,
and advanced tools for property filtering at a system level. But not to
worry, we are confident that the familiar cursor crayon will still be
available for emergencies.
Conclusions
We hope that this paper has yielded some insight into the state of the
art for geoscience interpretation
automation in general, and also
highlight the advances that are going to impact our ability to quickly
and accurately interpret fault systems. Our limitation is not the
computer hardware or visualization technology at the moment, but a lack
of logical integration of the necessary interactive tools to
intelligently extract the structural field from the
seismic
volume.
While the technical pieces are all available, the commercial software
offerings still lag behind. Many advances have been made and the
research continues for both explicit and implicit methods of
representing faulted structures. New algorithms for discontinuity
estimation and subsequent feature identification are constantly arriving
at the patent office and presented at international conferences. Let’s
hope the wait is not long for these marvelous tools to reside on our
workstation desktops.
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