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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
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 interpretation does not exist. Figure 3 shows an example of the inverse operation on a surface. The fault boundaries for the structural extent of the horizon are clearly visible. This technique must be applied to each surface and then linked from between one surface to the next, if a complete fault surface is required. Not really an automatic process, but it does allow an un-bias extraction of faults from a statistically consistent auto-tracker. Surface operations can be a powerful tool set for deriving additional information from surfaces and surface properties. Workflow or process managers and object calculators are technologies not yet fully exploited by geoscientists.
An early effort for semi-automatic fault interpretation came from
Landmark Graphics Corporation when they introduced FZAP! technology in
1997 (Hutchinson, Simpson et al., US Patent Number 5,537,320). This
technique allowed users to begin their fault interpretation task by
simply “seeding” one or more fault segments (sticks) on a vertical
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 interpretation are subjected to a connected body analysis followed by feature testing to deduce likely fault candidates. Through the analysis of multiple horizons, the entire fault framework could be extracted.
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” interpretation mindset. Let’s now examine some key contributors towards the advancement of fault interpretation automation.
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 interpretation automation of seismically resolvable faults and
fractures. Our working definition of “seismically resolvable faults are
fractures” means those features that express themselves through a
spatially coherent measure derived from a typical 3D compressional-wave
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 interpretation. We are replacing surface and fault drawing
with seismicobject detection methods, combining fit-for-purpose
attribute processing with pattern recognition technologies. Others
continue to exploit the horizon-based methods, but adopt a more global
approach by simultaneously operating on a collection of derived
surfaces. Alberts et.al., (2000) demonstrate a neural net method for
multi horizon tracking across discontinuities. This method is attractive
because it allows multiple input volumes (i.e.
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 interpretation of faults and horizons, through iterative
interpretation or simultaneous interpretation will help us converge on a
more accurate structural framework. Tingdahl et al. (2002) offer one
example of mapping faults and horizons concurrently, extending the work
of Statoil’s
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. Interpretation Automation
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
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
For automatic extraction techniques, the
For fault extraction, the construction of a discontinuity volume allows
the direct detection of
While the commercial market has a wonderful inventory of signal
processing methods for
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
The second form of extracting the fault information is an implicit
representation, where the
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
An automated means of producing this displacement field would require
the combination of two separate elements. We could determine the
displacement of a continuous
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
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
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