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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
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Historical Overview
The automatic tracking of seismic horizons has been widely available in
commercial software since the early 1990s providing our first insight
into the problem of
Most automatic horizon tracking applications include cross-correlation
or waveform based tracking algorithms to capture the seismic character
over a user controlled window length. These methods also compute a
“quality factor” attribute associated with the horizon pick position,
which give us a further indication on areas of faulting. The combination
of
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
Seismic signal process advanced rapidly during the 1990s, allowing us to
approach the problem of fault
These new edge attributes teach us that a vertical seismic section may
not be the best background canvas for fault
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 seismic
The current generation of geological modelling packages treat fault
surfaces as legitimate objects in a 3D
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 seismic data, via signal processing of the entire cube, or as a
by-product of horizon auto-tracking, enable us to visually isolate fault
features in the seismic data, particularly in a horizontal format
(either surface slices or time slices). This acceptance opens the door
that
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 seismic
volumes. A good example of incorporating signal processing and seismic
Marfurt et al. (1999) further develop seismic discontinuity processing in the presence of local structure using a smoothed local estimate. Chen et al. (2003) offer an alternative method for imaging discontinuities using dip-steering. Both are examples of processes, which benefit from a priori knowledge of the local structure. Sudhakar et al. (2000) familiarize us with the advantage of incorporating azimuthal variation into our methodology for detecting faults and fractures. They demonstrate the superior results obtainable by using restrictive azimuthal volumes during processing and attribute generation. Most commercial seismic attribute packages today offer some version of a seismic dip and seismic azimuth attributes or attributes that derive local structure during calculation.
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 seismic textures (Randen and Iske, 2005) for example. With this vast array of seismic attribute volumes, classification and neural network analysis are natural solutions for extraction or isolation of seismic objects.
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 seismic events at extrema positions only. Using a trace decomposition, a reflector can be represented with one-point support. The output is a spare cube with class values only at the minimum or maximum positions of the original input seismic data. Notice the consistent vertical sequence of classes across the fault boundaries in Figure 8.
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.
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Figure 10. Ant Cube viewed as time slice
to guide fault |
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Figure 11. Fault |
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Figure 12. Histogram and Stereonet
filtering of fault patch collections allow fault system level
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Figure 13. Voxel information extracted
from |
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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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