Identifying Faults and Gas Chimneys Using Multiattributes and Neural Networks

Modern visualization and image processing techniques arerevolutionizing the art of seismic interpretation. Emergingtechnologies allow us to interpret more data with higher accu-racy in less time. The trend is shifting from horizon-basedtoward volume-based. New insights are gained by study-ing objects of various geologic origins and their spatial inter-relationships. The standard way of highlighting objects isthrough seismic attribute analysis. Various attributes aretested in a trial-and-error mode, and one is selected as theoptimal representation of the desired object. The selectedattribute, which may be a mathematical composite of sev-eral attributes, is not sensitive to a particular geologic objectbut highlights any seismic position with similar attributeresponse.

We set out to develop a seismic-object detection methodthat in our opinion produces more accurate results and doesnot require expert knowledge. The method recombines mul-tiple attributes into a new attribute that gives the optimalview of the targeted object. Including specific spatial knowl-edge about the targeted object allows us to separate objectsof different geologic origin with similar attribute character-istics. The method comprises an iterative processing work-flow using directive seismic attributes (i.e., attributes steeredin a user-driven, or data-driven, direction), a neural net-work, and image processing techniques (Meldahl et al., 1999).Figure 1 is a generalized workflow of the object detectionmethod, which has a worldwide, patent-pending status.Objects that can be detected by the method include faults,reflectors, seismic chimneys, time-lapse differences, strati-graphic features, and direct hydrocarbon indicators. Thefirst products from simple application of the method arenamed TheChimneyCube and TheFaultCube.

This paper presents the basic concepts of the technology.Examples of the method to detect faults and chimneys areshown. Special emphasis is given to seismic chimneys andtheir interpretation, because the detection method has pro-duced fresh insights in hydrocarbon migration and trappingmechanisms and has resulted in a new and powerful explo-ration tool (Heggland et al., 1999).

Computational method of classifying objects using neuralnetworks. Seismic objects are 2- or 3-D bodies characterizedby a certain seismic response that differs from the sur-rounding response. The difference in response can be high-lighted by various attributes. Each attribute containsinformation on the object that we wish to detect. None ofthe attributes is expected to be sensitive to the targeted objectonly. This means that other objects are highlighted by thesame attribute. In our method we can separate bodies fromdifferent origins by careful selection of attributes and extrac-tion windows. We do this by training a neural network torecognize objects that we have identified in a seed interpre-tation. The network transforms all attributes into one newattribute, which indicates the probability of the object at theseismic position. The resulting object-probability cube canbe further enhanced by image processing techniques and iter-ating the process. Knowledge of object shape and orienta-

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Identifying faults and gas chimneys usingmultiattributes and neural networks

PAUL MELDAHL and ROAR HEGGLAND, Statoil, Stavanger, NorwayBERT BRIL and PAUL DE GROOT, de Groot Bril Earth Sciences, Enschede, The Netherlands

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Figure 1. The iterative detection workflow using direc-tive seismic attributes, neural networks, and imageprocessing techniques.

Figure 2. Size, direction, and shape of attribute extrac-tion windows are chosen in relation to the object wewish to detect.

Figure 3. Seismic input section and corresponding sec-tion from the chimney cube. The network uses 16attributes to classify each data point into chimney ornonchimney classes. Only the value for the “chimney”node is passed to produce the output cube. Warm (red)colors indicate high chimney probability.

tion can be fed back to the process, either to increase the detec-tion strength or to increase the resolution of highlightedobjects. The method, in essence, is composed of the follow-ing:

1) Only one type of geologic object is targeted at the time. 2) A selection of attributes with the potential to enhance the

objects is made.3) A neural network is trained on attributes extracted at

example object and nonobject positions selected by theinterpreter.

4) The trained network is applied to the entire seismic cubeto produce a new cube with high values at positions wherethe object is recognized.

5) Optionally, image-processing techniques are applied toimprove the neural network generated output cubes.

6) The sequence can be improved by iterating steps 1-5 usingknowledge gained in the process.

The sequence is very intuitive. The interpreter targets aparticular object (response) that should be highlighted. Then,the basic information is supplied in the form of examplesand attributes. Default attribute sets exist for different typesof bodies. The network takes over the role of the expert bycombining the information in an optimal way. Networks arenot very sensitive to redundant information, which meansthat a few additional attributes do not affect the end result.Application of the network to a small area surrounding anidentified example body is an effective way to QC the net-work’s performance.

A similar procedure has been developed for unsuper-vised neural networks. The output of this procedure is a clus-tered data volume. However, this unsupervised approach isbeyond the scope of the current paper.

Seismic attributes play a key role in the method describedabove. In principle, single- and multitrace attributes areselected that have a potential to increase the contrast between


Figure 4. Seismic input cube and corresponding“chimney cube.”

Figure 5. Seismic line (left) and fault cube image (right).

Figure 6. Composite display of 3-D seismic section andtime slice at seabed (left), and the full time slice(right). Chimneys and buried craters on top of themare surrounding a deeper oil reservoir.

Figure 7. High-resolution sparker line through aburied crater. High reflectivity in the water columnindicates gas escape above the crater.

Figure 8. Chimney cube showing tie between chim-neys and deeper faults.

objects and their surroundings. The detection power of a com-bination of attributes is greatly improved if the extractionwindows are designed to match the size, orientation, andextent of the targeted objects. If this is the case, we are map-ping directive attributes. Directivity in attributes increasesthe signal-to-noise ratio, sometimes dramatically. Unlikesimilar procedures used in acquisition and processing(Meldahl, 1998), we do not need to limit the directivitystrength to avoid smearing. We can focus on specific objecttypes and positions. Windows for directive attributes canhave fixed shapes and orientations, or they can have dataadaptive shapes and orientations (Figure 2). In the lattercase they follow the local dip and azimuth of the seismicobject.

This philosophy can be applied to detect seismic chim-neys. Chimneys are vertical disturbances in seismic dataoften characterized by low amplitudes and low trace-to-trace similarity (Figure 3). We use this knowledge by includ-ing energy and similarity as input attributes for the neuralnetwork. Knowledge about the vertical dimension is gainedby extracting energy, similarity, and other attributes in threevertically aligned extraction windows. The final product isa cube in which chimneys are visible by high sample values(high probability) and the surrounding volume by low sam-ple values (low probability, Figure 4). Restricting the detec-tion criteria in a second iteration can further refine the results.The interpreter should be aware that the network enhancesall vertical disturbances with similar seismic characteristics,like steep faults, fractures, diapir and channel edges, and

merged data zones.The same method can transform a seismic data cube into

a fault cube (Figure 5). In the example shown, a neural net-work was trained on seismic attributes at selected fault andnonfault locations. Twenty-two total attributes were fed tothe neural network. These were the reference time, dip (amultitrace attribute calculated by local, 3-D Fourier-Radontransformation), several dip-variance, energy, similarity, andamplitude calculations. The network made a classificationof the entire data set into fault and nonfault samples. Asbefore, high output values indicate a high probability of theobject (fault) being present, while low values mean lowobject probability. The result of Figure 5 can be improved byiterating the detection process using information on thedirection of faults as calculated from the first-pass result.Image processing techniques and special purpose extractionalgorithms can then be applied to extract fault planes in auser-controlled way.

Examples of chimney interpretation. Seismic chimneys arevertical disturbances of the seismic response often associatedwith upward fluid migration. They can be expressions ofhydrocarbon migration pathways between a source rockand a reservoir and between reservoirs at different depths.Chimney concentrations appear to be less distinct in NorthSea areas with dry wells, although components of lateralmigration may confuse the image. We also recognize thatchimneys can be caused by gas released from upward mov-ing water, due to pressure release. Chimneys frequently tiein with faults, which would be expected if hydrocarbonmigration is taking place in the subsurface. Chimneys alsotie in to features related to fluid or gas seepage, like pock-marks, mud volcanoes, and carbonate formations (Heggland1998, 1999, 2000). Amplitude anomalies indicating shallowgas accumulations are often located near the top of chim-neys. Due to their diffuse appearance, seismic chimneys arevery difficult to map on conventional seismic images.

Shallow chimneys sometimes surround deeper, hydro-carbon-filled reservoirs (Heggland, 1998). Figure 6 shows anexample from the Danish North Sea using standard 3-D seis-mic data. In this case, buried craters are located on top ofthe chimneys close to the seabed. This circular group ofchimneys and buried craters are located around a Tertiaryreservoir where an oil and gas discovery has been made. Thecraters have been formed during a period of sustained gasseepage and buried after the gas seepage has declined. Ahigh-resolution sparker line through one of the craters showshigh reflectivity in the water column above the buried crater,indicating active gas seepage (Figure 7). Features indicatingperiodic gas migration in this area are described in Heggland,1998.

Making time slices through the chimney cube is a wayto generate maps of chimneys. In the standard cube, chim-neys are not visible on time slices. In this example, taken fromthe Norwegian North Sea in the vicinity of a major oil andgas field, isolated chimneys are observed as well as clustersof chimneys on top of a major fault trend (Figure 8). The baseof these chimneys ties to the top of the fault system. Thisseismic evidence shows that deeper fluid migration takesplace through the fault into shallower sediments where themigration is vertical up to the seabed. The chimneys are partlysurrounding the deeper oil and gas field.

Within the Upper Tertiary sediments, vertical distur-bances are visible below amplitude anomalies (Figure 9). Thechimney cube reveals that these disturbances have differentshapes and lateral extents than the amplitude anomalies. Thismakes it less likely that the disturbances are caused by the


Figure 9. Composite display of standard seismic sec-tion and time slice (left) and corresponding chimneycube (right). Chimneys and faults below amplitudeanomalies are detected by the chimney cube.

Figure 10. Standard seismic section and correspondingchimney cube section. Chimneys are passing throughan Eocene and an Oligocene reservoir. Amplitudeanomalies are at the top of the chimneys.

amplitude anomalies and more likely that they are actualchimneys. The high amplitudes on top of them are inter-preted to be carbonate buildups above gas seepages to theTertiary seabed. The distribution of chimneys is aligned withmid-Miocene faults over which chimneys appear. The chim-ney cube also reveals small faults in the Tertiary along chim-ney lineaments.

Chimneys over the top of faults are demonstrated onFigure 10, a comparison of a standard seismic section withthe corresponding section from the chimney cube. Jurassic-age faults are passing through an Eocene and an Oligocenereservoir, in this example. Both these reservoirs contain oiland gas, proven by two wells. Figure 11 shows four mapsat different levels through these events. The lower left is atime slice at 3 s TWT from the chimney cube. The base ofthe chimneys align with the top of Jurassic faults. The nextfigure is a time map of the top of the Eocene reservoir atapproximately 2 s TWT, in which the two wells are dis-played. The third map is a time slice through the chimneycube at 1 s TWT. It shows chimney distribution above theEocene reservoir. A comparison with the top Eocene surfacereveals a high correlation between reservoir closure andchimney distribution.

The chimneys are interpreted to represent hydrocarbonmigration from the top of Jurassic faults, charging the Eoceneand the Oligocene reservoirs. The upper right figure is a vol-ume amplitude map from the standard seismic cube, show-ing amplitude anomalies at 250 ms above the chimneys. Theamplitude anomalies may represent shallow gas accumula-tions, at approximately 100 m below seabed, caused by thehydrocarbon migration from a deeper source. Alternatively,they may represent ancient carbonate formations generatedon top of the gas seeps.

Figure 12 is a 3-D visualization. Chimneys are yellow andthe background is transparent. The Jurassic faults are visi-ble on an interpreted base Cretaceous surface displayed inblue. The green surface is the time map corresponding tothe top of the Eocene reservoir. Amplitude anomalies fromthe standard seismic cube are red, and lower amplitudes aretransparent. The well in the right-hand corner, where nochimneys are present, was dry.

Other examples from the Norwegian North Sea showsimilar correlations. Chimneys have been observed near drywells in some cases; however, these are shallower than thedrilled reservoirs and may arise at this level as a result ofshallow lateral fluid migration. We realize that chimneys alsomay be caused by gas released from upward-moving water,due to pressure release, as well as gas released from upward-migrating oil or free gas.

Gulf of Mexico example. The final area of study is the deep-water slope of Green Canyon in the Gulf of Mexico in a waterdepth of about 2000 m. A3-D seismic cube covering approx-imately 18 miles2 was transformed into a chimney cube. Thestandard seismic cube and the chimney cube were used incombination, along with mapped horizons, to evaluate pos-sible hydrocarbon migration. Figure 13 compares a stan-dard seismic section (left), a single-attribute similarity section(middle), and the chimney probability section (right). Itdemonstrates that a single-attribute approach (similarity,which is a type of coherency) is inferior to the multiattributeneural network approach when the goal is to detect a cer-tain kind of object and not just any object with a similarattribute characteristic.

Figure 14 shows a 3-D visualization of the chimney cube,the standard seismic cube, and two mapped surfaces. Thetwo mapped surfaces are time maps—the upper one repre-

senting the seabed and the lower one approximately 1320-ft subseabed. From the standard seismic cube, only the high-est amplitudes are displayed in red. Lower amplitudes aretransparent in this display. Chimneys from the chimney cubeare yellow. These correspond to the high values in the cube(meaning high chimney probability). Lower values are trans-parent. The deeper cloud of high amplitudes correspondsto the outline of a salt dome, while the shallow cloud of highamplitudes is interpreted to represent a hydrocarbon-chargedreservoir.

Chimneys surrounding the salt dome indicate upwardfluid migration from a deeper reservoir. The high density ofshallower chimneys indicates charging of the shallow reser-voir. The subseabed surface exhibits a radial fault patterncaused by the upward movement of the salt dome. Chimneysare visible up to the seabed, and a small mound is presentat the seabed close to the top of the shallowest chimney on


Figure 11. Composite display comparing Jurassic faults(lower left), top of an Eocene reservoir (middle left),chimney distribution at 1 s TWT (middle right), andamplitude anomalies at the top (upper right).

Figure 12. 3-D visualization of an interpreted baseCretaceous surface (blue) revealing top of Jurassicfaults, interpreted top of an Eocene reservoir (green),chimneys from the chimney cube (yellow), and ampli-tude anomalies (red) from standard seismic volume.The two wells within the chimney area encounteredoil and gas in the Eocene reservoir as well as in anOligocene reservoir (not shown). The well to the right,where no chimneys are present, was dry.

the right side. This may be a small mud volcano generatedby the transport of sediments, fluid, and/or gas to the seabed.

The presence and distribution of the chimneys mappedin this area make the presence of a deep and a shallow hydro-carbon-charged reservoir more likely. Amanual mapping ofchimneys would have been difficult, less precise, and moretime-consuming. The chimney cube helped to visualize andefficiently evaluate the possible hydrocarbon migration.

To further advance this technology, Statoil, dGB, and theNorwegian visualization specialists GeoCap are currentlydeveloping d-Tect, a seismic object detection system in whichdirective attributes, neural networks, and image processingtechniques are combined with modern visualization tech-niques for flexible, interactive experimentation, processing,and interpretation of seismic data. It is believed that thetechnology has the potential to improve the standard inter-pretation workflow by reducing cycle times and producingmore accurate results. d-Tect is sponsored by the Dutch gov-ernment and six major E&P companies.

Conclusions. Seismic attributes and a supervised neuralnetwork can transform seismic input data into a new 3-Ddata cube in which one type of object is highlighted. Thisapproach has been successful in detecting chimneys andfaults, but the method has general applicability for detect-ing many different types of seismic objects. Other examplesare reflectors, hydrocarbon indicators, and 4-D time-lapsedifferences. Application of the method on chimney detec-tion has great potential within fluid-flow research, shallowgas detection, and prospect evaluations. It allows quantita-tive comparisons of chimneys and their distributions in dif-ferent areas. The method is currently being used in researchprojects, as well as in prospect evaluations as a risk reduc-tion tool and in the ranking of prospects. In the North Sea,in general, a high correlation has been observed betweenchimneys and the presence of oil and gas fields.

Suggested reading. “Gas Seepage as an indicator of deeperprospective reservoirs: A study based on exploration 3-D seis-mic data” by Heggland (Marine and Petroleum Geology, 1998).“The chimney cube, an example of semiautomated detection ofseismic objects by directive attributes and neural networks: PartII; Interpretation” by Heggland et al. (SEG 1999 ExpandedAbstracts). “Seismic chimney interpretation examples from theNorth Sea and the Gulf of Mexico” by Heggland et al. (TheAmerican Oil and Gas Reporter, 2000). “The chimney cube, anexample of semiautomated detection of seismic objects by direc-tive attributes and neural networks: Part I: Methodology” by

Meldhal et al. (SEG 1999 Expanded Abstracts). “Survey evalua-tion and design: Prediction of resolution versus line interval”by Meldahl (TLE, 1998). LE

Acknowledgments: Den Norske Stats Oljeselskap a.s. (Statoil) is acknowl-edged for the permission to present its technology and for data examples.

Corresponding author: P. Meldahl, [email protected]

Paul Meldahl is a staff geophysicist for Statoil in Stavanger Norway. Hismain work interests are survey evaluation and design, seismic imaging,and sea bottom seismic. Meldahl is involved in Statoil’s exploration andresearch and development programs. He holds a degree from the Universityin Bergen.

Roar Heggland is a staff geophysicist for Statoil in Stavanger, Norway.He has served at Statoil since 1984, working on geohazards interpretationand research and development, based on 2-D high resolution and 3-D seis-mic data. For the past eight years, his research has been focused on apply-ing 3-D seismic to hydrocarbon migration. He earned a degree from theUniversity of Bergen. His email address is [email protected].

Bert Bril is cofounder and head R&D of dGB (de Groot-Bril Earth SciencesB.V.), which specializes in quantitative seismic interpretation, stratigraphicanalysis, seismic inversion, neural networks-based reservoir characteriza-tion and object detection. He began his career in 1988 as an acquisitiongeophysicist with Delft Geophysical. He switched to software developmentin 1991, working for Jason Geosystems until 1992 and TNO Institute ofApplied Geoscience before cofounding dGB in 1995. He holds a master’sin geophysics from Utrecht University. His email address is [email protected].

Paul de Groot, cofounder and managing director of dGB, began his careerin 1981 with Shell. He then became director of Quest Geophysical Servicesand subsequently served as senior geophysicist for TNO Institute ofApplied Geoscience before cofounding dGB in 1995. He holds a master’sand doctorate in geophysics from the University of Delft. His email addressis [email protected].


Figure 13. Comparison of a seismic section (left) with asingle-attribute similarity section (middle) and a mul-tiattribute neural-network-generated chimney proba-bility section (right).

Figure 14. 3-D visualization of chimneys, amplitudeanomalies, and mapped surfaces from a Gulf ofMexico data set. Chimneys indicate leakage from areservoir trap against the flank of a salt dome (deepred cloud), feeding other reservoirs with associatedamplitude anomalies above the salt dome (deep bluesurface and shallow red cloud). From there, they spillto the surface, forming a mud volcano at the sea bot-tom (upper blue surface).

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Identifying Faults and Gas Chimneys Using Multiattributes and Neural Networks