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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
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 interpretation gaps, large gradient trends, and connected regions of low quality factor can produce an excellent visual isolation of the fault geometry, relative to the background horizon structure.
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 seismic section, and the automatic operation would perform a cross-correlation on a series of slanted traces derived parallel to the seeded fault segment. The method could be used for both tracking, where no previous fault interpretation existed, or snapping, where an existing fault interpretation would be corrected based on the slant trace cross-correlation algorithm. Each fault surface extracted would need an initial seed point.
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
Seismic signal process advanced rapidly during the 1990s, allowing us to
approach the problem of fault interpretation automation in a similar
vein as we attack horizon interpretation. Bahorich and Farmer (1995)
present The Coherency Cube (US Patent Number 5,563,949), a seismic
attribute for imaging discontinuities. They note that fault surfaces are
distinctly separated from neighbouring
These new edge attributes teach us that a vertical seismic section may
not be the best background canvas for fault interpretation. By
visualizing seismic discontinuity volumes as time slices, the major
seismic interpretation systems are well suited for fault interpretation,
as seen in Figure 4. Seismic attribute
processing highlight the spatial extent of each fault, allowing accurate
manual fault
The small additional step of executing seeded fault auto-
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 seismic survey. This measure could mean either a measure of discontinuity or another seismic attribute that allows cognitive identification and isolation of the fault feature.
We hope that by this point you can accept that discontinuity processing
of seismic
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
interpretation are a pair of papers by Fehmers and Hocker (2002, 2003)
on fast structural interpretation with structure-oriented filtering.
They making a convincing argument that
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 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. seismic attributes) to be
directly incorporated in the training and the estimation. As the authors
point out, classifying and tracking several
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
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
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 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
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
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
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.
ConclusionsWe 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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