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1 Seismic geometric attributes and information integration techniques for interpretation of small faults Wanhui Liu *, BGP, CNPC, Yawen He, University of Texas at Austin Summary Exploration geophysicists must deal with an increasing number of unconventional reservoir types such as tight sandstone, lacustrine shaley dolomite, calcareous mudstone, and volcano. These reservoirs are often characterized by low porosity, very low permeability, high heterogeneity and even anisotropy sometimes. The productivity of these reservoir types is strongly related to the development of fractures. Prediction of fracture properties (direction, density, porosity, permeability and connectivity) remains a difficult task. Therefore, the interpretation of small or so called seismically sub-resolution faults, which are the equivalent of the fractures in a larger scale and formed in the same structural stress field, can be an important alternative approach for fractured reservoirs prediction. In this paper we calculate some seismic geometric attributes and develop several methods of information integration to highlight the faults. These attributes prove useful in small faults interpretation, particularly when combined with other information through appropriate integration techniques. Introduction Small fault is a seismologic term referring to a fault with a vertical displacement of less than half a seismic wavelength. Small faults do not cut seismic reflectors (peak or trough) or completely displace both sides in the vertical direction. They are more precisely termed seisimically sub-resolution fault. Geometric attributes, including volumetric estimates of dip and azimuth, curvature, changes in waveform shape, and lateral changes in seismic amplitude, can be used to measure the geometry or shape of seismic reflectors. Coherence is an excellent tool for delineating geological boundaries. It is the most effective and popular method for detecting faults through quantitative estimates of fault/fracture presence. Faults that have drag, are poorly migrated, separate two similar reflectors, or do not appear to be locally discontinuous, will not appear on coherence volumes. These faults do appear in curvature images if there is a change in reflector dip across the fault. Volumetric curvature extends a suite of attributes previously limited to interpreted horizons to the entire uninterpreted cube of seismic data. It is thus more reliable because it is not influenced by subjective judgment of the interpreter. Attributes computation is performed with the goal of deriving or emphasizing those elements of the original seismic data that represent linear geology boundary such as faults. We found that integration of those lineament characters enhanced attributes and that amplitude data could become more useful in small fault interpretation. This is particularly true for strike slip faults with steep surfaces and very small horizontal extent and vertical displacement. Here, we develop several integration methods for highlighting such faults. Theory and method Structure Oriented Filtering with Edge Protection The goal of structure oriented filtering with edge protection is to smooth the reflectors along its dip in 3 dimensions between any two neighbouring faults while simultaneously sharpening the small faults. There are two key points: 1) to avoid the destructive interference of peaks and troughs for inclined or highly deformed strata, filtering must be processed along the reflectors; and 2) to avoid blurring the small faults, filtering must stop at the end of the reflector segment and jump over the fault to proceed. Unlike simple trace mixing performed by early version of commercial interpreting software, the first step for structure Figure 1. Comparison of the original seismic profile (a) and the corresponding structural oriented filtered with edge protection one (b) oriented filtering is to estimate the reflector orientation (dip and azimuth) which is also the most crucial component for all kinds of other volumetric attributes, and then the major discontinuities which is very important for edge (or, less formally, fault) protection. The reflectors are then smoothed SEG Las Vegas 2012 Annual Meeting Page 1

2 along the dip/azimuth unless there is no significant discontinuity in the analysis window, (this step is why the process is called structure oriented.) In most cases, this type of filtering will greatly emphasize the small faults and increase the apparent signal-to-noise ratio of the input data (Fig. 1). Taults will be illustrated more clearly with a more cleaner background in the volumetric time slices computed from the filtered data than those computed using original data. Sum filters, mean filters or median filters can be implemented; to date we have found no significant differences among them. Theoretically, filtration can be iterated as many times as needed. In practice, twice or three iteration has been adequate. Structure oriented filtering with edge protection is more useful for high angle or vertical fault enhancement, but it fails to enhance or even obscures very low angle faults. Coherence Seismic attributes such as coherence cubes, variance cubes, ant tracking cubes are frequently used in fault interpretion. Four popular methods of measuring similarity were developed in the past decades, all of which have been adopted by commercial software. These include the cross correlation algorithm used to compare well logs; the semblance algorithm used in velocity analysis; eigenstructure analysis which, like cross correlation, is insensitive to amplitude; and the gradient structure tensor. Cross correlation is the original algorithm used for estimating similarity of seimic traces. In the cross correlation algorithm, we first cross correlate the target trace in analysis window with the inline trace over a suite of temporal lags. We then repeat that process between the target trace and the crossline trace. The coherence estimate is obtained by computing the geometric mean of the two estimates. This method is no longer used because of the limitation in noise suppression, but it formed the foundation for the concept of coherence. In the semblance and eigenstructure algorithms, we first estimate dip and azimuth, and then calculate either the semblance or covariance matrix between the target trace and its nearest neighbors. The neighbors include two traces in the inline direction, two traces in the crossline direction and four traces in the oblique direction. These sets of nearest neighbors give rise to either five-trace (only the inline and crossline traces) or nine-trace ( inline,crossline and obilique traces are all involved in the computation) coherence algorithms, respectively. In semblance estimations of coherence, we first calculate the energy of the five input traces within an analysis window, 安 and then calculate the average trace. Finally, we replace each trace with the average trace and calculate the energy of the five average traces. The semblance is the ratio of the energy of the average trace to the energy of the input traces. If each windowed input trace has an identical waveform and amplitude, the semblance equals 1.0; otherwise, it will be less than 1.0. For eigenstructure estimation of coherence, we first calculate the energy of the input traces within an analysis window, and then calculate the seismic waveform that best approximates the waveform of each input trace. Lastly, we replace each trace with a scaled version of the calculated waveform that best fits the input trace. The eigenstructure coherence is the ratio of the energy of each replaced traces to the energy of the input traces. If each windowed input trace has the exact same waveform but perhaps a different amplitude, the coherence equals 1.0; otherwise, it will be less than 1.0. Eigenstructure coherence is thus less sensitive to amplitude than semblance coherence (Fig. 2). Figure 2. Coherence timeslice of Semblance (a), Eigenstructure from original data (b), and from the structural-oriented filtered data with edge protection (c) Due to the destructive interference of peaks and troughs, the energy of the average trace computed along the horizontal window will be small compared to the energy of the corresponding input traces, resulting in unexpected low coherence anomalies. It is thus very important that the energy of the average trace be computed along the dipping reflectors. The vertical analysis window should be also considered. Larger analysis window usually improve the resolution of SEG Las Vegas 2012 Annual Meeting Page 2

3 high angle or vertical faults, but smear the low angle ones. Optimal window size depends on both the data quality and the faults properties. Volumetric Curvature Curvature is defined of the degree of crook at a specific point on a curve. It can be described mathematically as the reciprocal of the radius of the osculating circle at that point. For a Curved surface in 3d space, curvature can be measured in any direction, so there is infinite number of potential normal curvatures. Roberts (2001) defines more than 12 variations of curvature. Geologic structures can be abstracted as a combination of different wavelength curvatures at some level, with long wavelength folds and short wavelength faults. For the identification of seismically sub-resolute faults that have a vertical displacement of less than half a event, especially those with drag to the plane, or with over- or under-migrated fault edge diffraction, curvatures analysis has advantages compared to coherence-based analysis (Fig 4). Unlike horizon-based curvature, the curvatures used in this work are generated directly from the three dimensional seismic volume using dip and azimuth. The most negative and most positive curvatures are the mostly useful one for fault imaging. Information integration Original seismic data includes abundant visual information about the structure patterns that are closely related to faults information. The more we try to highlight the faults, the more structural information we lose. However, faults and structural patterns are intrinsically interconnected; neither is as useful on its own as when both are studied. This is particularly true for small faults and very complicated fault zones, which can be difficult to understand if relying on the fault enhanced attributes without contrasting them with reflector orientation in the seismic data. It is important to combine the fault-related attributes with the 3D seismic amplitude data properly in order to accurately characterize the fracture zone. Three methods of information integration techniques are used: jointing, merging and fusing. Each has its advantage, disadvantage and applicability. Selection of the specific method to use depends on the type of attributes that are to be combined. Jointing combines two types of data (e.g seismic amplitude and coherence) into a single dataset in the distribution of one data type which we term base data, referring to the seismic amplitude in general. We term the fault-related attribute the second data. First, we must decide which part of the secondary data in distribution should be used in jointing, then compress that part to a single digit and use it to replace one of the digits of the base data in the distribution. It is easy to display and process data as needed using this method, which is simple and software independent. However, information content is reduced because of the limitation of the data distribution, and some extremely small faults may be missed. This technique is suitable for ant tracking fault-enhanced attribute (Fig. 3). Figure 3. time slice of Jointing data from the original seismic and Ant Tracking attribute Merging does not generate a new dataset; rather, it relies on the simultaneous display of two attributes, one defined by color and the other by shading. For displaying and interpretating faults, we use seismic amplitude data as a base volume (defined by color), and shade it using coherence as the second volume (Fig. 4). Figure 4.Time slice of the original seismic data (a), Most positive curvature (b) and their merging (c). SEG Las Vegas 2012 Annual Meeting Page 3

4 By setting the embossment height to a negative value, the faults appear as valleys as people thought them conventionally. The amplitude represents the undeformed strata as a background. We can also change the direction of light in order to highlight those faults which were difficult to define at any scales using either amplitude or coherence volumes respectively. This technique is superior to jointing in that it conserves the full amount of information in the two data sets to be combined; this is especially true for the display of highly fractured zones lacking a main fault plane. Moreover, no additional storage space is required because no new data volume is created with this method. It is also very flexible; Both the base and the second data can be assigned or replaced as needed. One disadvantage to this method is that it requires s specific visualization software platform, such as GeoProbe. Fusing uses a 2D color bar to combine two types of data. One dataset is shown by hue while the other is defined by brightness of the color. This method also generates a single data volume that uses the assigned distribution of both attributes. The new data, however, do not have clear physical meanings; rather, be shown with the specific 2D color table. It is most suitable to combine data sets when one is cyclic, such as frequency or azimuth (Fig. 5). Figure 5. Time slice of dip azimuth (a), dip magnitude (b) and their fusing (c). Conclusions 1. It is the foundation to accurately estimate the orientation (dip/azimuth) of the reflectors in the seismic volume for volumetric geometric attributes computation. 2. Performing structure-oriented filtering with edge protection on the original seismic data prior to computation of coherence type attributes will significantly improve the mapping of small faults. 3. Coherence is the most frequently used and efficient technique for small fault detection. Coherence should always be calculated along the dip of reflectors. 4. The volumetric curvature has an advantage over coherence when delineating small faults when they have high angle planes and extremely small vertical displacement, as well as drag phenomena, and when the seismic data are poorly migrated. 5. Small fault interpretation benefit from an integration of the fault-enhancing attributes with original seismic data, rather than relying on one type of them independently. 6. Three different information integration methods were employed in this work: jointing, merging and fusing. Each of them has its own advantages, disadvantages and applications. Acknowledgements Most of the attributes were computed on GeoEAST software of BGP, some of others were computed on Petrel of Schlumberger and displayed on GeoProbe of Landmark. The data are courtesy of Xinjiang Oilfield Company, PetroChina. SEG Las Vegas 2012 Annual Meeting Page 4

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2012 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Al-Dossary, S., and K. J. Marfurt, 2006, 3D volumetric multispectral estimates of reflector curvature and rotation: Geophysics, 71, Chopra, S., and K. J. Marfurt, 2009, Detecting stratigraphic features via crossplotting of seismic discontinuity attributes and their volume visualization: The Leading Edge, 28, Refunjol, X. E., K. J. Marfurt, and J. H. Le Calvez, 2011, Inversion and attribute-assisted hydraulically induced microseismic fracture characterization in the North Texas Barnett Shale: The Leading Edge, 30, SEG Las Vegas 2012 Annual Meeting Page 5