gas chimmney detection thru seismic attribute analysis[1]

896 The Leading Edge August 2010

INTERPRETER’S CORNER Coordinated by ALAN JACKSONINTERPRETER’S CORNER Coordinated by ALAN JACKSONINTERPRETER’S CORNER Coordinated by ALAN JACKSONINTERPRETER’S CORNER

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Chimneys are vertical chaotic disturbances in seismic sections related to the propagation of fluids (especially

gas) through fissures and fractures in rocks. They can be indicative of mud diapirism, active gas seepage, migration pathways or hydrocarbon reservoirs themselves (Aminzadeh et al., 2002).

Chimneys can also be indicative of seal bypass systems. When a seal is breached, the overlying formations fracture and allow the reservoir fluids to pass to shallower levels. A combination of these fractured rocks with some degree of fluid saturation accounts for the seismic signature of what is referred to as a chimney.

Seismic attributes can help detect these pathways and their extent. Such information can provide a better under-standing of the petroleum system that produced the hydro-carbons in the first place.

Attribute study and chimney detectionSeismic attributes are properties derived from seismic data to get a better understanding of the physical properties of strata (porosity, permeability, bed thickness, etc.). Attributes can be used to extract qualitative or quantitative features from seismic data. Analog studies and mapping of similar geobod-ies (such as chimneys) are examples of qualitative usages of attributes, while estimation of reservoir properties like poros-ity and permeability are the subject of quantitative studies.

Many attributes introduced in the industry are confusing to those dealing with the subject, and even experienced geo-physicists question the differences and similarities between them.

Seismic attributes are best thought of as being math-ematically derived from seismic data, in much the same way that arithmetic mean, minimum, maximum, and standard deviation are statistically derived from raw mathematical data. Such types of information are more understandable to us than the raw numbers; likewise, seismic attributes can be more useful to us than the raw data. A seismic section or volume brings valuable information about structure and stratigraphic changes within the subsurface. However, if we remember that seismic data are geophysical representations of subsurface geology in the form of traces and "wiggles," we then need to think of the relationship between these wiggles.

When we interpret seismic data, we follow the "wiggles" (or shades of colors) that have the highest correlation. Seismic attributes try to establish such relations between traces. For example, a fault is a discontinuation of reflectors; hence, if we mathematically calculate the continuity of reflectors, we will get an indication of where faults might be.

A well-known fault detector is a seismic attribute that measures event continuity. Instantaneous phase dip, better

HADI NOUROLLAH, JEFF KEETLEY, 3D-GEO Pty LtdGEOFFREY O'BRIEN, DPI Victoria

known as dip, is computed for three adjacent traces. A curve is fitted to the phase values and the dip at the center trace is then calculated:

The direction of this dip, a seismic attribute known as dip azimuth, is expressed as:

By using these calculations, we are essentially measuring the dip of a curve by calculating the rate of change in the y direction (dt/dy) and the rate of change in the x direction (dt/dx).

Other attributes utilize more complicated mathematics and are not as straightforward as the one discussed above. However, the user usually does not need to know the details of these mathematical procedures and need use them only insofar as they understand what an attribute measures and how it does so.

A useful way to understand attributes is to group them first. Such a grouping can be found in the Seismic Micro Technology (SMT) nomenclature:

• Geometric attributes give a better picture of underlying seis-mic geometry, such as faults or the connection between traces. The well-known attributes similarity, variance, and bedding belong to this group.

• Instantaneous attributes, as the name suggests, are calcu-lated based upon the difference from trace-to-trace, or sample-to-sample. First or second derivatives and instan-taneous frequency or amplitude are in this category.

• Wavelet attributes form a specific subgroup of instanta-neous attributes. According to Taner (2001), they are in-stantaneous attributes computed at the peak of the trace envelope, and have a direct relation to the Fourier trans-form of the wavelet in the vicinity of the envelope peak.

• Spectral decomposition is a technique by which to extract the range of frequencies constituting a seismic section. A seismic section is a composition of different bands of fre-quencies. Certain of these bands may provide a better fit to a geological feature.

The commercial software used for this study facilitated the production of dip-steered attributes. A dip-steering cube is a unique, innovative way of calculating attributes guided by apparent geophysical dips. The idea is to calculate the dip to adjacent traces from every single point in the cube. Attributes

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based on a dip-steered volume show a better continuity in 3D and make it easier to map desired events.

Noise, and its relationship to useful signal, is important. To help remove ar-eas of noise that might otherwise have been identified as chimneys, sea-floor expression of the subsurface chimney should be analyzed.

Chimneys are mostly associated with washed-out or chaotic features in the data. The reason is that the gener-ated gas diffuses between fractures, and between clusters of fracture net-works, containing different levels of gas saturation (Arntsen et al., 2007). This affects the wave velocity field and distorts it, because the mere presence of gas in a network of fractures will af-fect the compressional- and shear-wave velocities. Furthermore, the compres-sional velocities of rocks fall with the presence of only minor amounts of gas in fissures and porosities, leading to er-roneous results in the detection of eco-nomic gas based on an anomalously low Vp/Vs ratio.

In the case of chimneys, the con-verted compressional-to-shear veloc-ity is more helpful because we are not necessarily assessing the commerciality of the chimney as the primary target. Rather, as stated earlier, chimneys are most useful as a proof of the existence of active petroleum systems, gas-mi-gration pathways, and hints to com-mercial accumulations.

The Gippsland BasinThe Late Jurassic-to-Tertiary Gipps- land Basin, in Victoria in southeast Australia (Figure 1), is one of Australia's most prolific and mature petroleum provinces. Most of the basin is offshore in shallow waters (less than 200 m). Most oil and gas dis-coveries have been made offshore at the Late Cretaceous to Tertiary Intra- and Top-Latrobe levels; however, some mod-est hydrocarbon accumulations occur onshore in the Early Cretaceous Strezelecki Formation. While lower members of this formation are host to hydrocarbon reserves, the younger sequences act as a partial seal to the existing reservoirs.

Studies of the offshore Gippsland Basin reveal two phases of rifting (Power et al., 2001):

1) The first phase that resulted in at least 30% crustal exten-sion and played the major role in the formation of graben system of the Gippsland Basin.

2) The second phase, which is a much less pronounced event,

accounting for nearly 5% of crustal extension.

Power et al. and O'Sullivan et al. (2000) report a wide-spread uplift and erosion of Strzelecki Formation during Albian-to-Cenomanian times. This event is more obvious on-shore because the gravity data indicate that the basement is relatively shallower than offshore.

The Gippsland Basin is known for its active chimneys both offshore and onshore. Active gas seepage onshore has long been detected and well imaged through field work and radiometric images (Figure 2). It would therefore be reason-able to suggest that chaotic features offshore might be gas chimneys, because the offshore basin shares a close geological history with the onshore portion.

This study attempts to characterize the offshore chimney fingerprints based on their seismic expressions, and to relate

Figure 1. Map of G01a and GAP04a.

Figure 2. Fault leakage and traceable geobodies on the reflection-intensity cube.



INTERPRETER’S CORNER

them to the geology of the area (although this is not addressed in great detail in this paper).

Chimneys in Gippsland BasinThe offshore Gippsland Basin is relatively mature in terms of hydrocarbon exploration and production compared to other Victorian basins, such as the Otway Basin. However, as re-serves dwindle, and proposals for CO2 injection gather mo-mentum, new exploration techniques are required not only to identify areas for exploration, but also to select reservoirs that can retain injected CO2. Clearly identifying leaking faults and the occurrence of shallow gas by the identifica-tion of gas chimneys is therefore important in demonstrating risk to both hydrocarbon entrapment and the retention of injected CO2.

Over time, the petroleum operators in the offshore Gippsland Basin have covered the area with numerous 2D and 3D seismic surveys. However, the conventional approach to processing and interpretation has not been adequate to ef-fectively detect and map gas chimneys.

Due to the significant sizes of the 3D seismic surveys, pro-cessing for a single seismic attribute often requires 1–2 days to complete. Two major seismic cubes were given specific at-tention in this study. Together, they covered a large portion of the offshore Gippsland Basin (surveys GA01 and GAP04). The smaller cube (survey GAP04) was subjected to further validation analysis for the attributes used for the basin.

A general interpretation was performed to identify the

structural elements of the area of study. This helped identify the relevant tectonic regime and provide a better answer from attribute results.

Geometrical attributes were used to identify more subtle faults and, with a further calibration of these groups of ampli-tudes, two different sets of faults emerged. Both sets showed a reasonable continuity; however, while one set seemed to drag a property along the fault plane, the other set showed no such a signature (Figure 2).

As mentioned earlier, chimneys are mostly associated with vertical or subvertical trends of washed-out seismic data. While geometrical attributes of dip and azimuth type delin-eate more fault-like features, similarity and variance attributes can give a better measure of low-amplitude patches due to gas chimneys.

The resultant cube can be entered into a median attribute and overlain with wavelet attributes to enhance the visualiza-tion. The goal should always be to delineate washed-out sub-vertical trends. Reflection-strength or envelope attributes are strong highlighters for patchy low-amplitude areas.

The user can feed a large number of attributes into the neural network model and check, in the training-validation process, which attributes correlate better with known chim-ney bodies in the area of study. This approach could be im-mensely time-consuming especially when used blindly with large data sets.

A key element in constraining the analysis is to match the seabed and subsurface expressions of the chimney. This helps

Figure 3. Seismic section overlain by chimney detected through neural network analysis.




Figure 4. (a) Instantaneous amplitude section of seismic and the shallow-level signature of a chimney. (b) Horizon slice at seafloor on instantaneous amplitude cube matches detected chimneys.




remove noisy areas that might otherwise have been identified as chimneys. This is not necessarily true for all chimneys be-cause not all of them reach the sea floor; but for those which penetrate to this level, sea-floor expression and geochemical "sniffer" data should match the subsurface attribute. The for-mer is effectively used as a QC method in our study.

The reflection-intensity cube (Figure 2) depicts possible fault leakage and traceable geobodies in the shallower hori-zons. Some events have meaningful proximity to the leaking faults and can be interpreted to be shallow-gas accumulations, while others seem to be water-bearing (though care should always be taken not to confuse such events with hard-kick limestone layers).

One obstacle to any attribute study may be the deeper events that produce noise and affect the quality of resulting attributes. Efforts were made, through time-variant filtering and amplitude balancing, to fade out the effects of such noise as much as possible. Having achieved this, a chimney cube was produced based on a median filter of the previously made variance cube.

A simple chimney attribute is defined as a median filter of three adjacent dip-steered attributes: Simple chimney attri-bute = median (x0,x1,x2) where the xs are similarity attribute values.

This form of chimney attribute did not prove to be as

Figure 5. 3D chimney structure and the corresponding sea-floor signatures

effective as expected, and the similarity values were replaced with corresponding variance figures.

Instantaneous amplitude, similarity, simple chimney, variance, and reflection strength showed the highest regres-sion and relevant values to the chimneys and were fed to an artificial neural network (ANN).

Any neural network should rely on some initial definitive answers and a validation stage. Several "pick sets" of points of opposite attributes (chimney versus nonchimney) were picked to initiate the first layer of the ANN. The criteria by which to choose chimney and nonchimney points were based on the most meaningful attributes previously produced. These attribute sections were overlaid on top of each other using a transparency technique and the most reliable points were "cherry-picked" by the interpreter (Figure 3).

Care should be taken not to overtrain the neural network. Normally, when the training error curve reaches a plateau and seems to be stable, the training should be terminated. A verification of the regression result chart can reveal which attributes are more likely to show the chimney. In order to save computation time, low regressive attributes can be dis-carded from the training and the software allowed to rerun the network.

Two different results were obtained on the chimney mod-eling of the two cubes (GA01 and GAP04). However, in both




cubes, the model helped delineate subtle faults and a better understanding of charge-escape history.

G01ANo obvious classic chimney is present within G01A, al-though the chimney model helped in detection of gas-seep-age and seal-bypass systems. Fingerprints of chimney signa-tures are found within and around faults, and above such faults at shallow levels. The sea-floor signatures of chimneys were also investigated in this volume. Although indications of chimney were present at the sea floor, we failed to match any strong evidence in seismic sections. There is also a logi-cal structural tie between strong shallow amplitudes and the presence of leaked or leaking gas and/or liquid.

• Several structural styles are present including broad fault

zones with associated amplitudes and more narrow and through-going fault zones with weak amplitudes.

• Strong amplitudes can also be found, together with un-conformity zones, when a seal is present.

• Crestal leaking of gas/liquids is more common as a result of thinner seals (onlaps) across the structural crest, and an increase in fault instability due to greater column height in this area. This effect is well documented onshore where an active seepage of hydrocarbon is recorded both in field data and on radar images due to the thinning of the Latrobe marly member and to fault reactivation.

• Structural compartments within this volume, which have formed with larger fault offsets and a good vertical seal, do not have strong amplitudes above or within them. This in-dicates juxtaposition of sealing members against potential reservoir levels, or abrasion and smearing of shale along the fault zone that seals it off. Due to existence of an element of inversion in the Gippsland Basin, further complication is added to the sealing potential of faults. Therefore this analysis can help to evaluate the active sealing potential of faults and define the real baffles and barriers to potential flow.

GAP04A classic chimney is present within the GAP04 volume. This body appears as a continuous washy area of amplitude in the middle of the cube.

• A combination of some attributes like simple chimney, similarity, lateral continuity, and dip/azimuth variance were used to help define the boundary and seismic charac-ter of the chimney pipe. The strong chimney signature in the middle of the cube was mapped and used as the basis for neural network training (Figure 3).

• Care must be taken to use an appropriate set of seismic attributes in order to brighten chimney areas and distin-

guish them from nonchimneys (it is always beneficial to use a colorful palette to ensure that the user is not simply covering the true attribute fingerprint of the seismic) and avoid calculating a linearly dependent system of attributes. It is really not beneficial to calculate a large set of attri-butes blindly. Instead, with a little insight into the nature of attributes and understanding what they approximately represent, a minimal set of attributes can be chosen. This ensures a linearly independent set of attributes.

• Chimneys in the subsurface can have a corresponding ex-pression on the sea floor. Through picking the sea-floor reflector or the reflector immediately beneath it, and cal-culating a suitable surface attribute, chimneys and gas seepages should turn up in patches. In this study, instan-taneous amplitude appears to be a good indicator of the seabed expression of gas seepage (Figure 4).

• Faulting is a possible mechanism for gas leakage and for-mation of chimneys; however, nonleaking faults were common and detected through the adapted network. The polar dip variance (+40,–40) attribute volume further sup-ports the understanding of chimney development.

• Based on the chimney results (Figure 5), a new play fair-way could be built and sites for further drilling pinpointed in the isolated structural compartments.

ReferencesAminzadeh F., D. Connolly, and P. de Groot, 2002, Interpretation of

gas chimney volumes: 72nd Annual International Meeting, SEG, Expanded Abstracts, 440–443.

Arntsen, B., L. Wensaas, H. Loseth, and C. Hermanrud, 2007, Seis-mic modeling of gas chimneys: Geophysics, 72, no. 5, SM251–SM259.

Meldahl, P. and R. Heggland, 2001, Identifying faults and gas chim-neys using multiattributes and neural networks: The Leading Edge, 20, 474–482.

O'Sullivan, P., M. Mitchell, A. O'Sullivan, B. Kohn, and A. Gleadow, 2000. Thermotectonic history of the Bassian Rise, Australia: Im-plications for the breakup of eastern Gondwana along Australia's southeastern margins: Earth and Planetary Science Letters, 182, 31–47.

Power, M. R., N. Hoffman, T. Bernecker, and M. Norvic, 2001, The structural and tectonic evolution of the Gippsland Basin: Results from 2D section balancing and 3D structural modeling: PESA Eastern Australian Basins Symposium.

Taner, M. T., 2001, Seismic attributes: CSEG Recorder, 26, no. 9, 48–56.

Acknowledgments: We thank the Department of Primary Industries Victoria for the support and permission to publish this work. We are also in debt to Jim Preston for his corrections and helpful com-ments. Thanks also to Alan Jackson for his constructive review.

Corresponding author: [email protected]


gas chimmney detection thru seismic attribute analysis[1]

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