A review of “global” interpretation methods for automated 3D horizon ...

R e s e r v o i r c h a r a c t e r i z at i o n

936 The Leading Edge January 2011

SPECIAL SECTION: R e s e r v o i r c h a r a c t e r i z at i o n

A review of “global” interpretation methods for automated 3D horizon picking

Traditionally, 3D seismic interpretation has been achieved through extraction of 2D sections around closed loops

such that the beginning and end are coincident, a process known as loop tying (Lomask and Guitton, 2007). Using this approach it is often found that errors in picking result in mis-ties. A more advanced method uses seed-based autotacking to extract horizons by correlation of local wavelet amplitude between neighboring traces; however, mis-ties may also occur here as two paths converge, again due to errors in picking. Additionally these traditional amplitude-based autopickers can fail if the horizon being tracked has significant lateral amplitude variation or polarity reversal or in the presence of a large fault throw.

Generally, these methods are labor-intensive, time-con-suming, and often limited to regions with clear signal quality and relatively simple geology. Their weakness is that they use only a small fraction of the data at a time, solving only a series of local problems. Often, these programs simply require pick-ing in a 2D vertical section as humans are most proficient at this; however, computers are not limited to two dimensions and may be able to take advantage of the full dimensionality of the data and offer solutions based upon all of the data. In this respect, the use of computers in 3D volume interpreta-tion has not reached its full potential (Lomask and Guitton).

Recently, however, global approaches have been proposed to compute geological models directly from the seismic data without the need to pick all horizons manually (Pauget et al., 2009). Unlike methods, which track a horizon away from a seedpoint and are therefore prone to mis-ties, these algo-rithms are global in nature since they simultaneously track every surface throughout the volume (Lomask and Guitton). They are able to exploit the full dimensionality of the data to interpret multiple horizons in parallel, using the whole data set to find a minimum misfit solution, potentially offering a more accurate solution. In this way they attempt to capture several geological features such as faults and horizons simulta-neously. They may be subdivided into three major categories termed here dip-driven, horizon patches, and global opti-mization methods. In this communication, we will describe these methods and discuss their domain of application and current limitations.

Dip-driven methodsDip-driven methods use local dip and azimuth information at each grid position within the volume to autotrack all seis-mic events within the volume in parallel by fitting surfaces to the local dip in an optimal configuration.

Chevon method. Lomask and Guitton developed a meth-od for dip-based global interpretation as a means of flattening seismic data cubes along particular horizons. In this method,

Jack Hoyes, University of LeedsTHibauT cHereT, BG Group

seismic events are autotracked by following the local dip and azimuth calculated from the gradient of the seismic data at every sample position. At each point in the image, two dips are estimated in the x and y directions. Because each point in the image has two dips, each horizon is estimated from an overdetermined system of dips in a least-squares sense. This is equivalent to automatically loop-tying every possible loop simultaneously. To further exploit the dimensionality of the data, all horizons are estimated at once in 3D to conform to one another. If there is noise in the data, the resulting model will have significant errors and so hard constraints must be introduced in the form of traditional semi-automatically tracked horizons.

SSIS. The Sequence Stratigraphic Interpretation System (SSIS) plug-in in OpendTect is a relatively new interpreta-tion software package which is based upon the dip-driven interpretation method and is able to autotrack numerous chronostratigraphic events per sequence bounded by con-ventionally mapped horizons (de Bruin and Bouanga, 2007). In the SSIS workflow, major horizons are first mapped over the 3D volume using a traditional, semi-automated, horizon tracker. These horizons provide the bounding constraints on the intermediate chronostratigraphic events to be inter-preted using this new method. The intermediate events are tracked through each seismic sample position using either a model- or data-driven approach. The data-driven approach considered here is required for relatively complex geometries and is based upon dip steering. First, a steering cube contain-ing local dip-azimuth information at each sample position is calculated from the input seismic cube after processing such as filtering of the data to improve dip information (i.e., re-

Figure 1. Summary of the steps involved in OpendTect’s SSIS method of automated horizon interpretation.

January 2011 The Leading Edge 937

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move noise). This is done in the inline direction by locating a minimum or maximum in the trace and then looking for a similar event on the two neighboring traces in the inline di-rection. The gradient at that sample location is the difference in the time values of the extrema across these neighboring traces divided by the horizontal distance between the traces (Figure 1a). This process is repeated in the crossline direc-tion. Next, the dip-azimuth information is followed outward from a seed position and horizons are created between sample positions following the local dip (Figure 1b). The result is a set of tracked chronostratigraphic horizons going through each sample position and bounded by the mapped horizons (Lightenberg et al. 2006).

Horizon patches methodsWithin these methods, a subsurface model may be derived from classification of topological relationships between small surfaces of similar seismic attributes which may be merged to form larger horizons ordered chronologically (Pauget et al. 2009).

Extrema. In the Extrema method from Schlumberger, seismic events are automatically grouped into classes based on similarities in the waveform of the seismic signal. First, a polynomial function is fitted to each seismic trace and minima and maxima along each trace detected with sub-seismic precision using this function (Figure 2a). Next, a set of waveform attributes are calculated at these extrema posi-tions, again using the polynomial expression. These attributes contain overall information about the waveform in a verti-cal window around the extrema and are constructed using reconstruction techniques on the seismic trace such that the seismic signal locally is described through a limited number of coefficients (Borgos et al., 2003). Horizons of similar at-tributes along a lateral series of extrema are then constructed and from this data cube, continuous, class consistent, surface segments can be extracted (Figure 2b). These constitute pieces of seismic horizons which can then be combined by the in-terpreter. The algorithm aids in this interpretation process by mapping relative vertical positions of surfaces and therefore computing chronological relationships between them which may be used to highlight only a small number of surfaces representing a similar age or as an attribute to merge patches.

Cognitive vision. IFP has developed a method of seismic interpretation through the construction of horizon patches using an approach based upon cognitive vision. Cognitive vision combines computer-based vision with cognition to achieve functionalities of detection, localization, recognition, and understanding—abilities well suited to the seismic in-terpretation problem (Verney et al., 2008). The method first involves detecting first-order reflector continuity, character-izing seismic parameters of each reflector such as amplitudes, thickness and 2D dip. Next, chronological relationships be-tween reflectors are mapped. The last step of the workflow involves forming geological horizons corresponding to the various identified reflectors by linking nodes of reflector patches which have similar attributes and are located at simi-lar distances above or below at least one other reflector. Since

the result is a set of chronologically ordered horizons, these may be used for sequence stratigraphic interpretation and the construction of a Wheeler diagram (Verney et al.) although the latter is not currently incorporated into the software. A fault detection and enhancement feature has also been built into this method.

Seisnetics. The Seisnetics software uses a genetic algorithm to generate horizon and fault patches. Genetic algorithms are adaptive methods which are able to solve optimization prob-lems based on stochastic search methods (see www.seisnetics.com).

Global optimization methodThis method models data using cost-function minimization based on links between seismic samples within a 3D data set. Sets of links between seismic samples or bins are detected using correlation images obtained from couples of seismic traces. The degree of correlation between each link is esti-

Figure 2. Summary of the steps involved in Schlumberger’s Extrema method of automated horizon interpretation.

Figure 3. Summary of the steps in the Ellis Paleoscan method of automated horizon interpretation.


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mated using a cost function which measures the consistency of the model according to the underlying seismic signal. The cost function is related to the seismic similarity and geologi-cal consistency and also depends on distance. The optimal model is then constrained by moving links locally to achieve a global minimum (Pauget et al., 2009).

This is the fundamental approach behind the Paleoscan algorithm developed by Eliis (www.eliis.fr). Firstly, seismic traces are sampled to construct a regular grid in the x-y plane (Figure 3a). This process uses every point in each trace which may require binning of samples for larger surveys to reduce computational cost. Links between these gridded points are then determined via the computation of a correlation image for each pair of neighboring traces (Figure 3b). The corre-lation image of two seismic traces, X1 and X2, is an array of correlation values between each pair of samples on each trace in the X1(t) - X2(t) plane such that every point on the image corresponds to a link between a point on X1 and a point on X2. A point with a high correlation value corre-sponds to a link of high probability and when a set of high probability links is drawn on a line segment a “correlation comb” is obtained which links several seismic reflectors. The algorithm aims to detect the correlation comb with the great-est correlation value. This correlation comb provides a set of links which may then be used to compute a global position for every point on the sampling grid (Figure 3c). This results in an initial configuration of the geomodel block. However, this model must then be enhanced by determining the con-figuration with the lowest cost function which still represents the consistency of the model according to the underlying seismic signal (Figure 3d). This cost function is the weighted sum of the seismic vector distances between couples of points (Pauget et al.)

where N is the number of points inside the grid G, p(i) and P(j) are the positions of the points i and j, Dst(Vi, Vj) is the seismic vector distance between couples of points and σ is the

approximate distance between seismic points in the volume. This function is minimized by moving links locally until a global minimum is achieved.

Geological constraints such as faults and surfaces can be inserted into the optimization process to further constrain the model by reducing the number of solutions. Once the best configuration is obtained, it is then possible to establish weighted relationships between every seismic sample in the volume and from this a continuous geomodel is computed (Gupta et al., 2008).

Limitations and existing improvementsGeneral limitations. These global interpretation methods rely on the stratified nature of the seismic image and will therefore struggle to handle nonstratified objects such as salt, igneous intrusions, gas chimneys, and chaotic deposition. These features could simply be worked around (Gupta et al.). Volume interpretation (combination of 3D attributes) is the most appropriate approach to characterize nonstratified ob-jects. Acquisition and processing artifacts, such as multiples and migration hyperbolae, will also disrupt these algorithms and so, in order to produce a geologically accurate interpre-tation, all reflections must be geologically meaningful. This highlights a potential limitation of these methods in that it requires high-quality data in order to produce a valid geolog-ical model of the subsurface. Finally, faults with large throw typically make correlation of horizons across faults difficult. However, some of these methods offer improvements over traditional techniques in doing this.

Stratigraphic interpretation. By classifying the seismic sig-nal along reflectors according to similarity in its shape, the Extrema interpretation method performs well in structur-ally complex regions or on sparse 3D volumes (Borgos et al., 2003). A principal assumption of the Extrema method is that the seismic signal along a reflector does not vary significantly laterally such that class-consistent horizon patches are likely to be a continuous part of a specific horizon. This assump-tion enables grouping of reflector segments with similar char-acteristics in structurally complex regions since the method

Figure 4. Extraction of the geomodel across a fault using a global optimization algorithm (Pauget et al.). This figure summarizes the three main steps: (a) link creation in the model grid, (b) computation of positions, and (c) creation of the geomodel in block 3D.



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does not assume continuity in the geometric primitives. In this way, for example, the method allows faulted reflectors to be represented, even allowing for automatic quantification of fault displacement through a separate Ant tracking algorithm (Borgos et al., 2003). However, the assumption of lateral con-tinuity of the reflection for a given horizon is also a weakness of the method. For example the presence of hydrocarbons often causes lateral discontinuities in reflector characteristics

such as bright spot, dim spot, or phase reversal. This assump-tion does, though, mean that the algorithm deals well with stratigraphic lateral discontinuities such as truncations rather than attempt to force a lateral continuation of horizons which is not actually observed.

Fault handling. Faults are discontinuities within the seis-mic signal and as such they prevent easy correlation between different blocks. Most global methods therefore require major

Figure 5. Neural network result mapped on a horizon. The algorithm has classified gas sands in red, brine sands in blue, the thin bed in brown, and the surrounding shale in white.

Figure 6. Mapping of (a) thickness variations, (b) coherency, and (c) fault throw on the surface stack at the scale of the entire volume.



Figure 7. Features in the seismic such as this circular anomaly which are not easily seen in a vertical cross section (a) may be clear if the seismic amplitude is mapped onto strata slices (b).

faults to be imported for use as discontinuity constraints. This is a particular weakness of the dip-driven methods since faults are regions of anomalous dip and often accompanied by much wider regions of deformation than the fault plane itself. To ad-dress this problem, Lomask and Guitton applied a weighted inversion scheme allowing dips to be summed around faults. These weights may be obtained from a previously determined fault model, if their location and orientation are known, or by using iteratively reweighted least squares. By applying con-straints such as fault and horizon picks to the dip-based algo-rithm, it was demonstrated to work in faulted and noisy 3D field data examples. Because the method picks many horizons within a data set at once, globally, in a least-squares sense, it minimizes the effect of locally poor dip information so that the interpretation is more reliable in these areas.

Schlumberger has proposed a workflow combining the Extrema and Ant Tracking algorithms to build the structural model. The PaleoScan interface allows the user to create links to correlate horizons on both sides of a fault (Figure 4). Eliis (PaleoScan), Seisnetics and IFP have adapted their algorithms to extract fault patches. Schlumberger (Borgos et al.) and Eliis (PaleoScan) can also calculate the vertical throw. The vertical throw can be used to distinguish genuine faults from other discontinuities.

Volume interpretation. We define volume interpretation as the grouping of seismic voxels into classes of similar seis-mic signature using 3D seismic attributes. Those classes can describe depositional environment, lithology, fluid or any

combination of the above. There are several methods to per-form a volume interpretation: crossplot, multiregression, any mathematical combination of seismic attributes, neural net-work, and Bayesian classification using inversion products. The choice of techniques depends on the quality and avail-ability of the data. Volume interpretation is particularly well equipped to compensate for the shortcomings of the global interpretation to characterize nonstratified objects (Figure 5 and Figure 6).

ApplicationsGlobal interpretation methods are opening a new dimension in seismic characterization and offer a large range of poten-tial applications which would otherwise be time-consuming or very difficult to perform. A number of these are reviewed here.

Strata slicing. PaleoScan and SSIS allow the user to slice a volume along geological boundaries (Figure 7). This slic-ing reveals geological features unseen using traditional slicing methods (Figure 8) and offers an improved understanding of the depositional history independently from the tectonics (Gupta et al.), the ability to image shallow subsurface drilling hazards, for example.

Sequence stratigraphy. SSIS is a mature tool to detect the different elements of a stratigraphic sequence (Figure 8). Ulti-mately, the sequence stratigraphy analysis helps to determine in 3D areas of high prospectivity related to the sand quality distribution. PaleoScan proposes to detect sequence bound-




Figure 8. Result of interpretation of a synthetic seismic channel (top) using PaleoScan (center) and SSIS (bottom).

Figure 9. Example of vertical throw mapped on a seismic horizon and on fault plan in 3D view (via the PaleoScan method).

aries using the thickness attribute (vertical derivation of the geomodel). Area of low relative thickness could be associated with condensation layer, erosion or channel incision.

The SSIS, Chevron and Schlumberger methods all pro-duce a Wheeler diagram through flattening chronostrati-graphic horizons in time (in which the vertical dimension becomes geological time). For example in the SSIS workflow, local dips calculated over the entire seismic volume are re-solved into time or depth shifts relative to the reference trace using a nonlinear Gauss-Newton iterative approach allowing horizons to be flattened in time to generate a 3D Wheeler cube (Lightenberg et al., 2006). Stark (2004) achieves a simi-lar result to the algorithm of Lomask and Guitton (2007) by unwrapping using instantaneous phase rather than dips; however, the advantage of using dip rather than instanta-neous phase reduces problems with cycle skipping.

Fault throw. Global interpretation algorithms can be used to extract fault patches and calculate the vertical throw (Fig-ure 9). Potentially, this vertical throw could be used to guide well placement, help 3D reconstruction and perform fault seal analysis.

Well correlation. Due to the large number of finely spaced horizons that may be extracted using these methods, they of-fer potential for well correlation if tied to a number of wells intersecting these horizons (Figure 10).

Modeling. These interpretation methods can be used in the construction of background geological models for low-frequency models in seismic inversion or prior models in Bayesian classification (Figure 11). This is because such methods are potentially faster. A primary advantage of glob-



Figure 10. Application of PaleoScan for correlation between wells. A shale layer with thickness of 15 m can be correlated from well 1 to well 2.

Figure 11. Application of PaleoScan to the generation of a prior geological model for use in Bayesian facies classification. (Top) Prior gas probability distribution and (bottom) resulting Bayesian gas probability volume.




al interpretation algorithms is that they are able to extract many horizons within large volumes relatively quickly and efficiently.

SummaryThe horizon patches methods provide the most signal-con-sistent results. Schlumberger (Extrema), dGB (SSIS) and Chevron have developed sequence stratigraphy functional-ities. The Chevron method is the most direct way to derive a Wheeler diagram from a seismic volume. Generating a Wheeler doesnít seem out of the reach of the other meth-ods. Eliis (PaleoScan), dGB (SSIS) and Chevron can perform strata slicing. The horizons patches methods don’t seem to be the best equipped to develop toward strata slicing. PaleoScan seems to be the most advanced tool for handling and extract-ing faults. Eliis (PaleoScan) and Schlumberger (Extrema) can also calculate the vertical throw; Schlumberger requires an additional algorithm to perform this task (Ant Tracking). PaleoScan has gone a step further by allowing the user to modify the resulting geomodel interactively.

FutureThe global interpretation methods assume that the seismic data are a “perfect” representation of the Earth’s geology. Therefore, these methods will fail to converge when the seis-mic signal is degraded or in the presence of artifacts such as multiples, migration wings or smiles. In order to overcome these shortcomings, it has become obvious that the addition of “manual” constraints, based on more traditional meth-ods of interpretation, must be added to help the algorithm to converge to the underlying “geological truth.” As shown in Figure 10, global interpretation can be used to correlate any depth location of a well to the same geological layer in another well. In conjunction with traditional seismic well-tie techniques, this could help in building more accurate reser-voir and velocity models. Another challenge is the capabil-ity of those methods to capture sequence boundaries, which are not clear seismic horizons (low acoustic contrast), such as channel incision base. Finally, the global interpretation algorithm should be scalable and handle an ever increasing seismic volume.

ConclusionIn the last decade, various groups have developed global in-terpretation algorithms. Some of the algorithms are coming to maturity, enabling geophysics to extract even more infor-mation from the seismic volume at a scale unseen before. Combined with the traditional local and volume interpreta-tion, we foresee that the global interpretation will become part of the routine workflow for exploration and reservoir model building.

ReferencesBorgos, H. G., T. Skov, T. Ramden, and L. Sonneland, 2003, Auto-

mated geometry extraction from 3D seismic data: 73rd Annual International Meeting, SEG, Expanded Abstracts, 1541–44.

de Bruin, G., and E. C. Bouanga, 2007, Time attributes of stratigra-phy surfaces, analyzed in the structural and Wheeler transfrom domain: 69th EAGE Conference and Exhibition.

Gupta, R., T. Cheret, F. Pauget, and S. Lacaze, 2008, Automated geomodelling: a Nigeria case study: 70th EAGE Conference and Exhibition.

Lightenberg, H. J., G. de Bruin, N. Hemstra, and C. Geel, 2006, Sequence stratigraphic interpretation in the wheeler transformed (flattened) seismic domain: 68th EAGE Conference and Exhibi-tion.

Lomask, J., and A. Guitton, 2007, Volumetric flattening: an in-terpretation tool: The Leading Edge, 26, no. 7, 888–897, doi:10.1190/1.2756869.

Pauget, F., S. Lacaze, and T. Valding, 2009, A global interpretation based on cost function minimization: 79th Annual International Meeting, SEG, Expanded Abstracts, 2592-96.

Stark, T. J., 2004, Relative geologic time (age) volumes —Relating ev-ery seismic sample to a geologically reasonable horizon: The Lead-ing Edge, 23, no. 9, 928–932, doi:10.1190/1.1803505.

Verney, P., M. Perrin, M. Thonnat, and J. F. Rainaud, 2008, An ap-proach of seismic interpretation based on cognitive vision: 70th EAGE Conference and Exhibition.

Acknowledgments: The authors thank BG Assets for their permis-sion to publish their data. Sequence Stratigraphic Interpretation System is a trademark of dGB. PaleoScan is a trademark of Eliis. Extrema and Ant Tracking are marks of Schlumberger. Seisnetics is a trademark of (get from author).

Corresponding author: [email protected]

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