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An Introduction to Seismic InterpretationChapter 4: Data Display

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4.1 OverviewIn the past, i.e. before computer-based interpretation systems became the norm, the fi nal task of the seismic processor was to print out paper copies of the (2-D) seismic data that would be used for the interpretation. The vertical and lateral scales were defi ned at that time, as well as the display type (black and white wiggle displays, see below, were the norm) and other aspects of the display. The interpreter could not subsequently change those parameters. Commonly, only one copy of each seismic line was available, and extreme care needed to be taken not to damage it in any way (e.g., coffee spills, torn paper).

Nowadays, seismic interpreters (at least in the petroleum industry) usually work with digital data that has been loaded into a computer-based seismic interpretation system. The interpreter is free to interactively change the display in order to bring out features of interest. However this fl exibility raises an important concern: how best to view the data?

This chapter explores ways to view and interpret seismic data. It contrasts 2-D seismic data with 3-D seismic data, and explores some of the ways that software companies have made it possible to slice and dice through 3-D seismic volumes. The terms defi ned in this chapter (e.g., arbitrary line, horizon slice, volume rendering) will be employed in later chapters.

4.2 Data Display Before proceeding to a discussion, and comparison, of 2-D and 3-D data certain basic options for displaying seismic data need to be discussed. The workstation-based interpreter has much fl exibility for viewing and interpreting the data, and at least some concepts and techniques can be applied to both digital 2-D and 3-D data.

Digital Data FormatSeismic data loading is a topic that needs some discussion. As it is received from the seismic processors, the digital seismic data is likely to be in SEG-Y format, a standard that was established

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An Introduction to Seismic Interpretation Chapter Four Data Display

by the Society of Exploration Geophysicists (Norris and Faichney, 2002). A SEG-Y fi le consists of several parts (FIGURE 4.1), the fi rst being the EBCDIC1 header. There is one EBCDIC header per SEG-Y fi le. The EBCDIC header commonly, but not always, contains general text information about the seismic data such as acquisition and processing parameters, geographic coordinate information, etc. The coordinates of the four corners of a 3-D seismic survey may be stored here. The EBCDIC header should be printed out or otherwise stored (i.e. digitally) somewhere where it can be accessed throughout the interpretation. Some seismic interpretation packages allow the information from the EBCDIC header to be stored for later access. Sometimes the EBCDIC header is referred to as the ASCII header, if the text is stored in ASCII format. The binary header (one per SEG-Y fi le) follows the EBDIC header and contains information about the sample interval (e.g., 2 ms), the number of samples per seismic trace and the data format (e.g., integer or fl oating point).

The seismic data is stored following the binary header on a trace-by-trace basis. The data for each trace consists of two parts, the trace header (one header per trace, which generally includes the inline and crossline numbers for 3-D surveys, the CMP number for 2-D surveys, the coordinates of the seismic trace and other information) and the trace data itself, the seismic amplitudes for the trace stored in binary format. Although nominally a tape standard, SEG-Y has been adopted as a disc fi le format and a degree of variability in the byte positions of some data in the trace headers has evolved between different companies. These differences can make data loading problematic.

Multiple versions of the seismic data, perhaps consisting of different migration methods (CHAPTER 3), different AVO angle stacks (CHAPTER 8), seismic attributes(SECTION 4.6) or other variations, might be loaded. Some software packages allow post-stack data to be linked to pre-stack data so that an interpreter can click on a trace in a 2-D or 3-D transect and the CMP gather will become visible for inspection (for example, perhaps the NMO correction was poorly done, resulting in amplitude anomalies in the post-stack data).

FIGURE 4.1:Schematic representation of a SEG-Y fi le

FIGURE 4.2:Different ways of representing seismic data

Figure 4.1: Schematic representation of a SEG-Y fi le

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The EBCDIC header commonly contains information about the data acquisition and processing parameters and other information. The Binary header contains information about the way the data is stored on tape. Subsequently, each trace in the seismic data is stored in two parts, a trace header (potentially containing trace numbering, geographic coordinates for the trace location, etc.) and the seismic data (a time-series of amplitude measurements) for each trace.

Figure 4.2: Diff erent ways of representing seismic dataBack to Chapter

At left, the column of numbers shows how seismic amplitude values might be stored in the computer (albeit usually in binary format) for part of a seismic trace. The same seismic data is shown as a variable-area wiggle display in the middle. The amplitude values are shown as a continuous curve (the wiggle). Positive values (peaks) are fi lled in black (variable area) whereas negative values are left open. At right, the seismic data is shown as a variable density display. Positive amplitude values are shown in blue (the stronger the amplitude, the darker the blue), zero amplitude is shown in white and negative amplitude values are shown in red (the stronger the amplitude, the darker the red).

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1 EBCDIC - Extended Binary Coded Decimal Interchange Code, an 8-bit character encoding format for mainframe computers that is a relict from computer punch cards.




Wiggles and Color DisplaysOne of the fi rst choices an interpreter will have to make is how to view the data. The two most common options are the variable area wiggle display (VAW) and the variable density (VD) display. Remember that the seismic data is stored in digital format on the computer, with a sampling rate that is typically 2 or 4 ms (left side, FIGURE 4.2). The variable area wiggle display shows the data as a continuous curve (the wiggle part) with the peaks fi lled in as black (the variable area part; middle, FIGURE 4.2). This is the way in which most seismic data were plotted prior to the advent of computer graphics systems. However, a problem with this type of display is that the eye tends to see the variation in the peaks (because they are black) whereas variability in the troughs (white) is harder to detect, especially when looking at a large portion of seismic data. In principle, there is no reason why peaks should be more important than troughs. For example, and as discussed in CHAPTER 2, the top of a sandstone bed might be a peak or a trough depending on the state of compaction (SECTION 2.4). As such, Backus and Chen (1975) and Galbraith and Brown (1982) used a dual polarity display in which troughs were colored red and peaks in black, but both peaks and troughs were shown on the same (right) side of the seismic trace. The dual polarity display is no longer used by most seismic interpreters. Balch (1971) presented an early attempt at color display, with the wiggle trace representing the the amplitude and a color variable-area display that showed the frequency spectrum of the wavelets.

The advent of computer displays led to two enhancements for interpretation. First, the traces were displayed in variable density format (right side, FIGURE 4.2) that could be blended laterally to give the appearance of data continuity (instead of viewing separate wiggles). Second, although a dual polarity display could be used to approximately give peaks and troughs equal weighting visually, the subtle color changes visible in the variable density display convey changes in amplitude better it has better dynamic range. The red-white-blue color scheme shown on the right in FIGURE 4.2 was devised, wherein blue colors represent peaks, red colors troughs and zeros are white and the intensity of the blue (or red) depends on the amplitude of the signal. The red-white-blue color bar is an example of a diverging color scheme in which the colors representing the quantitative values (amplitudes in the seismic world) progress outward from a light-colored midpoint (zero amplitude; Brewer, 2007). This is an appropriate choice for seismic data in which positive and negative amplitude values can both be meaningful.

FIGURE 4.3 (A, B, C. D. E) shows different types of display for a larger portion of seismic data. The variable area wiggle display (FIGURE 4.3A) shows the traditional view of the data. Individual wiggles represent the stacked traces at the CMP locations. The variable density display in FIGURE 4.3B has a blue-white-red color bar, as described above. Because the interpretation software blends adjacent traces in the variable density display, the individual traces comprising the data are not recognizable and the seismic image appears to show a laterally continuous transect. Although this color bar is useful for displaying changes in amplitude, users soon sought to design other color bars in order to highlight other aspects of the seismic data. For example, the black-white (also known as a grayscale) color bar used in FIGURE 4.3C shows the strongest peaks in black, the strongest troughs in white, and intermediate amplitudes as a continuous grayscale between the two extremes. Grayscale color bars are an example of a sequential color scheme (Brewer, 2007) that is usually employed to represent differences between low and high values (e.g., elevation). Although this does not truly represent the nature of seismic data, experience has shown that the greyscale color bar is useful for highlighting faults, and it is therefore a good choice for structural interpretation. On




the other hand, the grayscale color bar does not adequately emphasize subtle changes in amplitude. The rainbow (also known as a spectrum) color bar shown in FIGURE 4.3D emphasizes subtle changes in amplitude, however the abundant changes in color make it unsuitable for detecting faults. Other color bars are available, and interpreters can learn how to create their own color bars to highlight features of interest. Other discussions of color-related issues, some directly related to seismic display and others that are best thought of as relevant to seismic display, are presented by Russell (1992), Brown (2005), Welland et al. (2006) and Zhang and Montag (2006). Chopra and Marfurt (2007) discussed color displays, and how color can be used when an interpreter wishes to view more than one seismic attribute at a time.

FIGURE 4.4 compares the effects of two different color bars on a vertical transect through some 3-D seismic data from the Gulf of Mexico. The blue-white-red color bar clearly shows some high-amplitude areas, but the grayscale color bar makes the faults much more apparent. Structural features of interest in this image will be examined in CHAPTER 6.

Although variable density displays are useful, and somewhat more aesthetically pleasing than variable area wiggle displays, there are times when an interpreter will want to look at the shapes of the wiggles. In this case, the interpreter may wish to overlay wiggle traces on the variable density display (FIGURE 4.3E). In later chapters, we will see examples of displays in which wiggle traces are overlain on color displays of attributes, inversion results, velocities or other derivatives of the seismic data. Backus and Chen (1975) were among the fi rst to display seismic data this way.

Sometimes interpreters get into the bad habit of using one color bar for all interpretation purposes. Clearly, different color bars are better for certain purposes than others, and the interpreter should choose the type of display that best emphasizes the features being interpreted. It should also be noted that a signifi cant proportion (approximately 7% of the population, and generally males) of the population has some form of color blindness, which generally translates as an inability to distinguish red from green, or to perceive those two colors differently from the majority

FIGURE 4.3 A:Different display types for seismic data

Figure 4.3 A: Diff erent display types for seismic data

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A) The variable-area wiggle display. Peaks areshown in black and troughs are left unfi lled, as per FIGURE 4.2. Although the human eye can detect changes in the peaks (because they are fi lled in black), changes in the troughs are more diffi cult to detect visually.

FIGURE 4.3 B:Different display types for seismic data

Figure 4.3 B: Diff erent display types for seismic data

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B) This variable density display represents positiveamplitudes in blue, zeros white and negative amplitudes in red, as per FIGURE 4.2 and the color bar at lower right. This type of display gives approximately equal weight to peaks and troughs, and conveys subtle changes in amplitude better than the variable-area wiggle display.

FIGURE 4.3 C:Different display types for seismic data

Figure 4.3 C: Diff erent display types for seismic data

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C) Another variable density display with thestrongest peaks in black, the strongest troughs in white, and a gradational gray scale between the two, as per the color bar at lower right. This type of color bar is particularly useful for detecting faults, although changes in amplitude are more diffi cult to detect.

FIGURE 4.3 D:Different display types for seismic data

Figure 4.3 D: Diff erent display types for seismic data

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D) The rainbow (spectrum) color bar shownin this display (and shown at lower right) has the strongest peaks in yellow/red, the strongest troughs in purple and a spectrum of colors between. Subtle changes in amplitude are well defi ned using this color bar, although it is clearly inferior to the grayscale color bar for detecting faults.




of the population. As such, a grayscale color bar is a safe choice when the interpreters intent is only to show structural or stratigraphic geometries.

Data Scaling and PolarityThe amplitude values we see as interpreters are generally arbitrary units that are not scaled to the possible range of values represented by refl ection coeffi cients (theoretically -1 to 1). The range of amplitudes depends on the format in which the data have been stored on the computer. There are three commonly used formats: 8-bit data, 16-bit data and 32-bit data. The fi rst of these, 8-bit data, has 28 (256) possible values of amplitude and so amplitudes vary between -128 and 127 (0 is counted as a value). In 16-bit data, the data can have 216 (65,536) possible amplitude values (-32,768 to 32,767) and 32-bit data can have 232 (4,294,967,296) possible amplitude values (-2,147,483,648 to 2,147,483,547). Obviously, the 16-bit and 32-bit data formats will have greater dynamic range than the 8-bit data, meaning that subtle changes in amplitude will be better preserved. However, those data will also require proportionately more disk space for data storage.

Whatever the format our data may be loaded as, computer monitors are typically restricted to showing 256 possible color values. Visually, therefore, the data are being scaled to 8-bit format, and there is no interpretive advantage to having the data stored in 16- or 32-bit format. In fact, storing the data in one of those formats may slow down the softwares performance (e.g., scrolling through 3-D cubes), at least for large 3-D seismic volumes, because the data are typically stored in memory while being visualized. If the only use of the seismic data will be for mapping of structural or stratigraphic geometries, then 8-bit format is acceptable. The principal advantage of working with 16- or 32-bit data comes when quantitative analyses (e.g., attribute studies, inversion, amplitude-variation-with offset) are to be undertaken.

Scaling of the data during data loading can be an important consideration. Histograms of amplitude values in a seismic dataset typically show a nearly normal distribution, consisting mostly of near-zero amplitudes and progressively fewer high-amplitude peaks or troughs (FIGURE 4.5). Commonly there are some outliers, a few extreme values that might be related to noise, or

FIGURE 4.5:Histogram of amplitudes from a seismic dataset

FIGURE 4.6:Variable-area wiggle display showing data that have been clipped

Figure 4.5: Histogram of amplitudes from a seismic dataset

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Most of the data consists of relatively low amplitude values, with a mode near zero. There are very few of the strongest (positive or negative) amplitude values. The yellow and red areas represent data that might be clipped during data loading.

Figure 4.6: Variable-area wiggle display showing data that have been clipped

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Note the truncated appearance to the highlighted troughs. Clipping also aff ects some of the peaks, although this clipping is diffi cult to see when the peaks are fi lled in black as in this fi gure.

FIGURE 4.4:Comparison of a blue-white-red and grayscale color bars for structural imaging

Figure 4.4: Comparison of a blue-white-red (above) and grayscale (below) color bars for structural imaging

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The only diff erence is the color bar. The faults are much easier to see in the lower image.

From Hart (2007).

FIGURE 4.3 E:Different display types for seismic data

Figure 4.3 E: Diff erent display types for seismic data

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E) This variable density display (with grayscalecolor bar) has a wiggle overlay so that an interpreter can examine changes in trace shape.




potentially useful things like hydrocarbons. As the data are loaded, the ranges of values are scaled to the ranges associated with 8-, 16- or 32-bit data. If the outliers are retained during data loading, most of the amplitude values will be constrained to a narrow range of values, represented by a narrow range of colors, and the data will have a washed-out appearance. In this case it will be better to not include, or clip off, those extreme values during data loading. After clipping, all amplitude values above a user-defi ned value are arbitrarily assigned the maximum amplitude value (e.g., -128 or 127 for 8-bit data). Excessive clipping can lead to saturated variable density displays (lots of intense colors). Even worse, there will be problems during advanced analyses (e.g., inversion, attribute studies) if amplitudes are to be used to predict rock properties.

Although it may be diffi cult to detect data clipping by looking at variable density displays, variable area wiggle displays can be helpful (FIGURE 4.6). Bacon et al. (2007) illustrated the use of a color scale designed to help the interpreter identify data clipping in variable density displays. Badachhape (2001, 2002) discussed scaling and other data-loading issues. Some software packages require the user to scale (and therefore potentially to clip) the data during data loading, whereas others allow the user to load the full data range and interactively adjust the range of amplitude values being observed.

A key decision, usually made during processing, is to defi ne the polarity of the seismic data. In its simplest terms, this means deciding whether a positive refl ection coeffi cient should be represented by a peak (positive amplitudes) or a trough (negative amplitudes) in the seismic data. The Society of Exploration Geophysicists polarity standard is that a positive refl ection coeffi cient should be represented by a peak in zero-phase data (Sheriff, 2002; FIGURE 4.7A). For most seismic interpreters in North America, this convention is considered to be normal polarity and is henceforth referred to as North American polarity in this text. In many places elsewhere in the world, the convention is the opposite and representing a positive refl ection coeffi cient by a trough (FIGURE 4.7B; henceforth referred to as International polarity) is considered to be normal polarity.

The difference between these two polarity conventions is important when interpreting seismic data. An interpreter needs to understand, for example, whether the top of sandstone beds (or some other lithology) should be picked as a peak or a trough. Maps of amplitudes or other attributes that are derived from picking the wrong horizon will be meaningless. Understanding the polarity of seismic data is important when looking for hydrocarbons (CHAPTER 8). If the polarity of a seismic dataset is unknown, then an interpreter should look for prominent refl ections that should correspond to known types of refl ection coeffi cients. Some examples:

The seafl oor in a marine survey will always correspond to a positive refl ection coeffi cient. Unfortunately the seafl oor refl ection is sometimes cropped off during processing, preventing the interpreter from applying this approach. Strong sidelobes can also sometimes be mistaken for the seafl oor refl ection.

Knowledge of the local stratigraphy can be useful. For example, if unconsolidated sediments overlie consolidated rocks (basement), the contact should correspond to a positive refl ection coeffi cient. Perhaps it is known that a limestone bed underlies a shale. In such a case the top of the limestone should nearly always correspond to a positive refl ection coeffi cient.

A fl at spot, the refl ection from a hydrocarbon-water contact in a reservoir




(CHAPTER 8) should always correspond to a positive refl ection coeffi cient.

In all of these cases, the interpreter identifi es the known refl ection coeffi cient and determines whether it corresponds to a peak or a trough, thereby establishing the polarity of the data. Generating synthetic seismograms(CHAPTER 5) can be a more rigorous, but unfortunately still sometimes ambiguous, way of establishing the polarity of a seismic dataset.

Adjusting Vertical and Horizontal Scales (VerticalExaggeration)

An interpreter working with a computer-based interpretation software package has the freedom to interactively adjust the vertical and lateral scales. He/she can fi ll up the computer screen with as much, or as little, of the seismic data as desired. During the initial stages of an interpretation it might be useful to see the big picture, to zoom out and look at the broad-scale stratigraphic and structural context. Later, perhaps during the horizon interpretation stage, the interpreter may wish to zoom in and focus on a particular stratigraphic level because that will help to ensure that the horizon picking is accurately done.

A note of caution is warranted here. By changing the lateral and vertical scales we are effectively changing the vertical exaggeration of the display. If we take a long 2-D or 3-D seismic transect and squeeze it horizontally so that it fi ts on a computer monitor, we may be signifi cantly distorting the appearance of structural and/or stratigraphic features. For example, faults that really dip at relatively low angles can appear to be vertical in the seismic display. Likewise, gently dipping stratigraphic surfaces can appear to be quite steep (a bad thing), although refl ection terminations (onlap, downlap, etc.) might be highlighted by increasing the vertical exaggeration (a good thing). As discussed in a later chapter, structural analyses (i.e. what do the faults mean in terms of structural history?) should be undertaken on images that have as little vertical exaggeration as possible, even though horizon picking might more reasonably be done using sections displayed with a greater vertical exaggeration. FIGURE 4.8 shows the effect of changing the vertical exaggeration on some

FIGURE 4.8:Example of changing the vertical scale on the appearance of a Permian carbonate shelf margin

FIGURE 4.9:Example of changing the vertical scale on the appearance of structural features

Figure 4.8: Example of changing the vertical scale on the appearance of a Permian carbonate shelf margin, from Pranter et al. (2004)

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A) Seismic transectwith ~ 4 X vertical exaggeration.

B) Seismic transect withapproximately no vertical exaggeration. The view in part A would be easier to interpret horizons on, but erroneous interpretations of depositional processes might ensue.

Figure 4.9: Example of changing the vertical scale on the appearance of structural features

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These images, from Sagan and Hart (2006) show the same upward-branching fl ower structure aff ecting Lower Paleozoic strata of the Appalachian Basin. With high (5:1) vertical exaggeration the faults appear to be vertical, whereas they are obviously less steeply dipping with approximately no vertical exaggeration (upper image). The lower image is a variable-density display with a wiggle overlay(c.f. FIG. 4.3E).

FIGURE 4.7:Polarity standards for seismic data display

Figure 4.7: Polarity standards for seismic data displayBack to Chapter

A) Most interpreters in North America prefer to view seismicdata such that an increase in acoustic impedance (positive refl ection coeffi cient) corresponds to a peak (here colored blue) in seismic data. This is the SEG-Y standard.

B) Elsewhere in the world, interpreters prefer to view data such that an increase in acoustic impedance (positive refl ection coeffi cient) corresponds to a trough (here colored red) in seismic data.




stratigraphic features, while FIGURE 4.9 shows the effects of changing vertical exaggeration on some faults.

Structural Dip: Flattening Seismic Displays andIsochron Maps

Geologists correlating wireline logs commonly generate stratigraphic cross sections in order to remove the effects of structural deformation (FIGURE 4.10). A stratigraphic datum is chosen, perhaps a maximum fl ooding surface or some other horizon that was originally close to being both planar and horizontal, and all wells on the cross section are shifted up or down so that the log pick forms a datum on the cross section. By removing the effects of structure, the interpreter seeks to be able to more clearly focus on stratigraphic features. Bend (2007) discussed the difference between structural and stratigraphic cross sections.

Digital seismic data, both 2-D and 3-D, can be fl attened in the same way, once a reference horizon has been picked. All traces on the seismic line being displayed are shifted up or down so that the reference horizon is horizontal. As with wireline log cross sections, the usual intent is to remove the effects of structural deformation in order to focus on stratigraphic features. FIGURE 4.11 shows two versions of a seismic transect that corresponds to the log cross section shown in FIGURE 4.10. The upper image (part A) shows the current structure and the lower image (part B) shows the seismic transect fl attened on the same datum as used in FIGURE 4.10B.

FIGURE 4.12 shows another seismic transect that has been fl attened. In this case, an unconformity separates the Lower Cretaceous Dakota and Burro Canyon formations from the underlying Jurassic Morrison Formation. Folding subsequently deformed the succession. To better examine the relief on the unconformity, the data were fl attened on a seismic refl ection which corresponds to a fl ooding surface at the top of the Dakota. The fl attened image better depicts the original relief on the unconformity at the top of the Morrison Formation.

It is important that the horizon used to fl atten the data corresponds to a stratigraphic surface that was originally (at least nearly) planar and horizontal. A marine fl ooding surface, like that chosen to fl atten the data in Figures 4.11

FIGURE 4.10:Two versions of a log cross section in Cretaceous clastic strata from Wyoming

FIGURE 4.11:Two versions of a seismic transect that correspond to the log cross section shown in Figure 4.10

Figure 4.10: Two versions of a log cross section in Cretaceous clastic strata from Wyoming

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A) Structural wireline logcross-section showing log correlations in Cretaceous clastic strata from Wyoming. Correlations show current structure.

B) The same well dataas shown in part A, but displayed as a stratigraphic cross section. One of the log picks has been used as a stratigraphic datum, and the wells have been shifted up or down to make this datum horizontal.

Figure 4.11: Two versions of a seismic transect that correspond to the log cross section shown in Figure 4.10

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A) Seismic transect correspondingto the cross section shown in FIGURE 8A. The red line is a seismic refl ection that corresponds to the fl ooding surface used as a stratigraphic datum in 8A.

B) The same seismic line fl attenedon the refl ection corresponding to the stratigraphic datum inFIGURE 4.8B. The software shifts the traces up and down so that the seismic horizon is fl at. Vertical black lines in both A and B correspond to the four wells shown in FIGURE 4.8. Note that the section is imperfectly fl attened when the horizon pick does not exactly follow the seismic refl ection, such as at far left. have been shifted up or down to make this datum horizontal.

FIGURE 4.12:Seismic transects showing the Lower Cretaceous Dakota Formation at Ute Dome in New Mexico

Figure 4.12: Seismic transects showing the Lower Cretaceous Dakota Formation at Ute Dome in New Mexico

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A) Seismic transect showing theLower Cretaceous Dakota Formation at Ute Dome in New Mexico. The top of the Dakota is a marine fl ooding surface, which was originally relatively planar and horizontal over the small area shown in the fi gure. It has now been folded. The Morrison horizon corresponds to an unconformity at the base of the Dakota.

B) Once the seismic transect hasbeen fl attened on the Dakota, the original relief on the unconformity becomes easier to evaluate. Note that noise in the original Dakota seismic pick causes the fl attened section to be slightly distorted. From Hart et al. (2001).




and 4.12, might represent a good choice for some areas. On the other hand, fl attening the seismic data on an unconformity (which can commonly be associated with signifi cant erosion) or the top of a carbonate buildup (which would originally have had some relief) would be an unwise choice. Unfortunately in some settings, such as continental slopes, there might never have been stratigraphic surfaces that were close to being horizontal.

One fi nal consideration is of importance when working in folded or steeply dipping areas. In the presence of structural dip, maps and stratigraphic cross sections made from well and/or seismic data can have different types of artifacts. Three types of maps are:

Isopach maps show the true stratigraphic thickness of an interval, (i.e. the thickness of a unit measured perpendicular to bedding; FIGURE 4.13A)

Isochore maps show the true vertical thickness of an interval (e.g., the thickness that would be seen by a vertical well; FIGURE 4.13A).

Isochron maps show the separation in time between two horizons in a seismic dataset, and are typically closer to isochore maps than isopach maps because the mapping calculations typically do not unfold the stratigraphy. Instead, they map the vertical separation between two horizons along each trace. This can lead to apparent thickening on the fl anks of folds, as illustrated in FIGURE 4.13.B,C.

In Praise of PaperMuch, and usually most or all, of a seismic interpretation (3-D or 2-D) will be undertaken on a computer workstation in the petroleum industry. However, it is important to recognize the benefi ts of, at least occasionally, printing out seismic data on paper. This can be an important exercise when working with long (regional) seismic transects. For example, an interpreter working with a 20 km-long seismic transect on a workstation can either squeeze the data laterally so that it all fi ts on the monitor, or he/she can choose to view only portions of the data at a time. In the

FIGURE 4.13:Possible geometric pitfall associated with fl attening folded strata

Figure 4.13: Possible geometric pitfall associated with fl attening folded strata

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C) The seismic image has beenfl attened on the top of the bed. Note that the bed appears thicker where the fl ank of the fold would be because the software does not unfold the bed (to show true stratigraphic thickness), it simply shifts the traces up.

A) The geologic image at left shows the fl ankof a fold. The yellow layer has the same stratigraphic thickness on the crest and fl ank of the fold. However the layer would appear thicker to a vertical well penetrating the fl ank of the fold (true vertical thickness) because it is dipping.

B) Conceptual seismic image of thesame structure shown in part A.




fi rst case, the vertical exaggeration will be extremely distorted and the true geometries of structural or stratigraphic features will not be visible. In the second case, the vertical exaggeration is reduced, but the interpreter will only be able to view a small portion of the long line at a time. As such, the big picture (i.e. the larger stratigraphic and/or structural context) may be missed. Plotting the line out on a long piece of paper can help the interpreter to see the line all at once, and at a scale that does not unduly distort the image. Experience has shown that posting and repeatedly viewing such images on a wall (perhaps in a work room, or an offi ce) can help the interpreter to recognize subtle features that might otherwise be missed. Paper copies of seismic data are also used for certain types of structural analyses, such as making jump correlations and for structural reconstructions (see CHAPTER 6).

4.3 Characteristics and Limitations of 2-D Seismic DataAlthough most seismic data collected nowadays in the petroleum industry are 3-D, and outside of that industry there is an increased desire to use 3-D methods, there is still a need to collect and interpret 2-D lines. Some cases where 2-D data may be collected and used include:

Frontier areas or other settings where the objective is to cover large areas and defi ne the big picture from a stratigraphic and/or structural perspective. In the petroleum industry, this might be the case when no working petroleum system has yet been defi ned in an area (i.e. it is not clear that hydrocarbons were ever generated or trapped in quantities suffi ciently large to make them commercially exploitable), or perhaps when a company wishes to understand the relationships between two widely spaced 3-D surveys.

Old pools, or other areas where 2-D data were collected prior to application of 3-D methods. In this case the 2-D data are available and need to be incorporated into an interpretation, provided that the quality of those older data is suffi ciently good.

Diffi cult terrain or other areas where logistical diffi culties make collection of 3-D datasets unfeasible. For example, laying out a grid of shotpoints and receiver lines might not be possible in mountainous or hilly terrain with dense vegetation.

Testing new seismic acquisition methods or systems. Tie lines that cross two or more well locations, and are used to tie those wells to the

seismic data via synthetic seismograms or other methods (CHAPTER 5). Inadequate budgets for 3-D acquisition, processing, and/or interpretation. Although

this is commonly the case with academic groups2, even some oil companies (large and small) still collect 2-D data rather 3-D data because of economic constraints. This exercise in fi scal restraint can be counter-productive. For example, budget-minded companies sometimes decide to collect a few 2-D seismic lines each year in an area of interest instead of spending more to collect a 3-D volume in that area. Each year they collect more 2-D lines (while maintaining a stable seismic acquisition budget) with the hope that the addition of new lines will improve their ability to map stratigraphic and structural features in the study area. However, because of imaging and mapping problems discussed in CHAPTER 3 and below, they may never be able to adequately map those features using the 2-D data. The grid of 2-D data they come to acquire may end up costing more than the 3-D seismic data, but it will

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2 Understanding how to properly acquire and process 3-D volumes, and getting access to expensive 3-D seismic interpretation packages, can also be issues for many Academic research groups




never be as useful. Competitors who had invested in 3-D data from the outset would probably have had a competitive advantage by making better drilling decisions (fewer dry holes, better well placement). Unlike the profi t-driven petroleum industry where companies will spend large amounts of money on seismic technology if the potential rewards are perceived to be great enough, the environmental geoscience industry (hydrogeology, geotechnique, etc.) tends to be cost-driven. This means attempting to deliver reports to clients at the minimum cost. As such, and despite the advantages of 3-D seismic methods, 2-D data tends to be the norm because it is cheaper to collect and process3.

Sometimes combinations of factors infl uence the decision to collect 2-D seismic data. For example, Austin (2004) presented an example that demonstrated the utility of high-resolution 2-D seismic data for looking at the shallow part of the section (i.e., above conventional petroleum industry drilling targets) in areas where conventional 3-D seismic data volumes do not have adequate vertical resolution because of their relatively lower frequency content (FIGURE 4.14). Rather than collecting high-resolution 3-D seismic data in the marine areas illustrated in that paper, he illustrated the benefi ts of acquiring and analyzing targeted, high-resolution (i.e., higher frequencies than typical industry 3-D surveys) 2-D data proved useful for identifying drilling hazards and other features of interest in the shallow part of the stratigraphic section. In principle, high-resolution 3-D survey can be collected or generated from existing 3-D data using specialized processing fl ows, but the cost-benefi t ratio may make this option impractical.

Typically, an interpreter will work with a grid of 2-D seismic lines, including perhaps several grids of different vintages. The grids are designed in response to considerations such as: 1) Ideas about the orientation of structural and/or stratigraphic features to be studied. Typically this means collecting strike- and dip-oriented 2-D profi les. 2) The size of the area being covered. 3) The

FIGURE 4.14:Comparison of a high-resolution 2-D seismic line with an equivalent portion of data from a petroleum industry 3-D seismic volume

Figure 4.14: Comparison of a high-resolution 2-D seismic line (top) with an equivalent portion of data from a petroleum industry 3-D seismic volume

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3-D data were acquired using lower frequency source that would give better penetration (to see the deeper exploration targets). As such, there is considerable stratigraphic detail in the high-resolution 2-D data that is not seen in the 3-D data. Reproduced with permission from Austin (2004). Try to estimate the dominant frequency of each dataset by counting peaks, as presented in CHAPTER 2.

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3 Lack of familiarity with 3-D seismic technology is undoubtedly another contributing factor.




amount of time/money available to acquire the data. 4) The need to complement existing 2-D coverage (e.g. infi lling grids or different line orientations).

At times, individual lines of 2-D seismic data may be acquired. This might be the case when the purpose of the data acquisition is to tie the seismic data to some type of ground truth, such as an outcrop exposure or core. Rarely can a series of parallel 2-D seismic lines (without any tie lines) be adequate for mapping, because it may not always be clear how refl ections observed on one line correlate to those on a non-intersecting line.

Modern seismic interpretation software typically allows the interpreter to assemble composite transects (also known as multi-panel displays) that show portions of different 2-D seismic lines. This can be a useful exercise, for example, when no single 2-D line is long enough, or has the proper orientation, to show all features of interest. FIGURE 4.15shows an example of a composite 2-D display. The seismic transect in that fi gure is composed of data from three different seismic lines, the lines being chosen to show a nearly continuous transect that intersects four ocean drilling sites. Some software packages allow interpreters to construct fence diagrams that consist of an interlocking grid of 2-D profi les (FIGURE 4.16). These views can be rotated and viewed from a variety of angles to examine structural and stratigraphic relationships.

4.4 2-D Versus 3-DIdeally, and very simplistically, 2-D seismic lines represent geologic cross sections through the earth4. However, as noted in CHAPTER 3, 2-D seismic data have a problem with sideswipe, i.e. imaging features that are not in a plane directly below the line formed by the receivers and source. As such, an interpreter can never be certain that features seen in a 2-D seismic image are truly where they appear to be.

Another signifi cant problem encountered when working with 2-D seismic data is that the way to correlate structural or stratigraphic features between lines may not be apparent. For example, look at the simple correlation problem presented in FIGURE 4.17, which shows a grid

FIGURE 4.16:Fence diagram consisting of a grid of intersecting 2-D seismic profi les

FIGURE 4.15:Example of a composite 2-D display

Figure 4.15: Example of a composite 2-D display

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A) Time structure map showing 2-D seismicgrid and two-way travel time to the top of acoustic basement in the vicinity of ODP Leg 194 (near the Great Barrier Reef, Australia). Contour interval is 20 ms. Numbered drill sites were picked based on seismically defi ned targets.

B) The seismic display at right showsa composite seismic transect created by showing portions of the 2-D grid that are highlighted in blue at left. Reproduced with modifi cations and permission from Isern et al. (2002).

Figure 4.16: Fence diagram consisting of a grid of intersecting 2-D seismic profi les

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Inset map shows the location and orientation of profi les included in the fence diagram.

Reproduced with permission from Slingerland et al. (2006).

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4 Ignoring time distortions such as those described in Chapters 2 and 3.




of four 2-D seismic lines. A series of faults, each with a component of normal displacement, is observed on those lines. As can be seen, there are many ways to correlate the faults. Clearly, knowledge of the tectonic setting can be helpful for constraining the interpretation options. However, the proper way to correlate faults between lines will always be ambiguous, with the ambiguity increasing as the distance between the 2-D seismic lines increases. Hart et al. (2001), Brown (2004) and others showed that 3-D seismic-based mapping of an area commonly indicates the existence of more faults, and more complex fault patterns, than can be mapped using 2-D seismic and/or well control. Hart et al. (1996) showed that this can be the case even for 3-D seismic surveys of 1 square mile (~2.6 km2) or less.

In contrast to the 2-D seismic correlation problem, 3-D seismic data provide continuous data throughout the survey area. Look at the image presented in FIGURE 4.18.It shows a one-square mile (~2.6 km2) area known as a section (a legal surveying area in many parts of North America). There are 16 wells (large dots) in this hypothetical section (corresponding to 40-acre well spacing, a dense development drilling case), which could be considered excellent well data control for mapping structural or stratigraphic features. Now imagine that the section is covered by 3-D seismic data having a 110 x 110 (~ 30 m x 30 m) bin size (the small dots illustrate the bin centers)5. There will be 2,304 traces within the section. In other words, the seismic survey has 144 times more data control points with which to map structural and stratigraphic features. Unless the stratigraphic or structural features cannot be detected by the seismic data, it will always be possible to map them more accurately with the 3-D seismic data than with the well control. The reader should however keep in mind the limits on lateral resolution noted in CHAPTER 3.

It is also important to note that the well control has better vertical resolution (meter-, or sub-meter scale) than the seismic data (few meters to tens of meters). As such, the idea should not be to choose between well or seismic data, but to fi nd a way of merging the relatively good vertical resolution of the well data with the much better lateral resolution of the 3-D data. This type of integration is

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5 This is a relatively coarse, but common, bin size at least for older land 3-D seismic data in the United States. A smaller bin size (e.g., at least 55 x 55, ~ 15 m x 15 m) is preferred but not always economically possible.

FIGURE 4.17:Ambiguity associated with correlating faults on grids of 2-D seismic data

Figure 4.17: Ambiguity associated with correlating faults on grids of 2-D seismic data

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Image in upper left shows a hypothetical grid of four 2-D seismic lines (dashed). The locations of faults with a component of normal displacement are indicated. An interpreter has a problem: how to correlate faults between lines. Several possible interpretation options (red) are presented. Similar types of ambiguity can be present when correlating stratigraphic features (reef margins, channels, etc.) between 2-D seismic lines. Based on an idea presented by Badley (1985).

FIGURE 4.18:Comparison of the subsurface sampling density for a 3-D seismic grid with 110 x 110 spacing and the sampling density of wells with 40-acre spacing

Figure 4.18: Comparison of the subsurface sampling density for a 3-D seismic grid with 110 x 110 spacing and the sampling density of wells with 40-acre spacing

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Grid has 110 x 110 spacing (~30 m x 30 m; a relatively coarse grid common for many 3-D surveys in the United States) and wells have 40-acre spacing (dense development drilling in the United States).

There will be 2,304 seismic traces in a square mile versus 16 well control points in that same area. In other words, 144 times more samples with which to map stratigraphic or structural features. Redrawn and used with permission from Ray (1995).

Grid(~3coamanUnihavdevUni

Thetracversin thworsamstrafeatwith(199

FIGURE 4.19:Illustration of digital seismic data and 3-D seismic cube consisting of voxel traces

Figure 4.19: Illustration of digital seismic data and 3-D seismic cube consisting of voxel traces

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A) Digital seismic data can be displayed as seismic traces(variable area wiggle or variable density displays, as illustrated here (left) and in FIGURE 4.2. With 3-D seismic data, the trace represents an area with fi xed dimensions in x and y that is also sampled at regular time (z) increments. As such, the seismic trace in a 3-D survey corresponds to a series of voxels, each of which stores a seismic amplitude value.

B) A 3-D seismic cube consists of a series ofvoxel traces, whose coordinates consist of an inline number, a crossline number and a two-way traveltime (TWT).




discussed in later chapters.

4.5 Viewing 3-D Seismic DataLet us now consider ways in which a 3-D seismic volume may be viewed. At this point it is worth noting that although different software packages have different capabilities, there are many types of views that are common among the different interpretation packages. Unfortunately the names for a single display type may be different from one software package to the next, or between different groups of users.

We begin by re-examining how the 3-D seismic data are stored in the computer. Remember that the data consist of a series of amplitude values that might be sampled at 2 or 4 ms increments, and that traditionally those data are displayed as seismic traces (FIGURE 4.2, left side FIGURE 4.19A). In a 3-D seismic volume, that seismic trace represents an area known as a bin (CHAPTER 3) which has fi xed dimensions in x and y. Combining the bin size with the sampling interval (z) allows us to defi ne voxels (right side of FIGURE 4.19A), each of which stores an amplitude value. The word voxel is an abbreviation of volume element. A voxel is the 3-D equivalent of a pixel (picture element), a term used to describe the ground area corresponding to a single element of a remote sensing dataset. Many voxel traces are combined to form the 3-D seismic cube (FIGURE 4.19B). When viewing 3-D seismic data, each voxel will be colored according to the amplitude value stored in it (FIGURE 4.2).

Vertical TransectsThe simplest way of slicing through a 3-D volume is to look at vertical transects6 that correspond to inlines or crosslines (also referred to as lines and traces, respectively, by at least one commonly used interpretation software package; FIGURE 4.20A,B; Animations 6, 7). The bins in the volume are assigned to an arbitrary coordinate system consisting of mutually orthogonal inlines and crosslines (FIGURE 4.19B). For marine data, the inline direction generally corresponds to the ships sailing direction. Onshore, the inline direction is usually the orientation of the receiver lines.

FIGURE 4.20:Common ways of viewing 3-D seismic data

Figure 4.20: Common ways of viewing 3-D seismic data

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A) In the inline direction,

B) The crossline direction,

C) Arbitrary lines (arbitrarytransects), and

D) Timeslices.

ANIMATION 6

ANIMATION 7

Animation 6Back to Chapter


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6 Interpreters should refrain from calling these transects vertical seismic profi les, because (as described in CHAPTER 5) that term is used to describe a type of borehole seismic image.




Another way of viewing vertical transects through the data is to generate arbitrary lines that can have any orientation through the data (FIGURE 4.20C). The user picks the orientation in order to best view the features of interest. For example, perhaps the grid of inlines and crosslines is oriented north-south and east-west but northeast-southwest striking faults cut through the area. Viewing the faults in either the inline or crossline orientation will distort the true fault geometry because these views cross the faults at an angle. The best way to view the faults would be to look at northwest-southeast oriented arbitrary lines that correspond to structural dip lines. An arbitrary line may be a single line, or it may be a composite line that zig-zags its way through the data volume, perhaps examining stratigraphic or structural relationships between wells. This viewing fl exibility is a major advantage of 3-D seismic data: the viewer has complete freedom to view the data from any orientation.

These vertical transects look essentially like 2-D seismic profi les. The interpreter may change scales, color bars, and other aspects of the display, as is the case when working with digital 2-D seismic data. However, two points need to be emphasized: 1) the viewer has complete freedom to pick the orientation of the lines being viewed (unlike 2-D grids), and 2) the data have had 3-D migration, and so sideswipe has been eliminated.

Time Slices, Horizon Slices and Stratal SlicesA different way of viewing the data is to take horizontal slices through the data (Figures 4.20D, 4.21;ANIMATION 8). For conventional 3-D data, with the vertical axis in two-way traveltime, these horizontal slices are known as time slices, because they represent planes of constant two-way traveltime. Note that this does not necessarily mean that the display shows a constant depth, because the wave-propagation velocity commonly varies laterally. (As a thought exercise, re-examineFIGURE 2.2B. Does 500 ms in the seismic data correspond to a constant depth?) On the other hand, if the 3-D cube has been depth converted (perhaps through pre-stack depth migration), then horizontal slices through the seismic cube are properly known as depth slices. Time slices are useful for looking at, or for, the lateral variability of structural or stratigraphic features, and examples of these displays will be shown in Chapters 6 and 7.

FIGURE 4.21:Sample timeslice from a 3-D seismic survey

ANIMATION 8

Figure 4.21: Sample timeslice from a 3-D seismic survey

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The slice, a variable density display with a greyscale color bar(see FIGURE 4.3), crosses through a 3-D seismic data volume. Interpreters need to be able to recognize structural and stratigraphic features in this type of display. Area shown is approximately 15 km across.

From Hart (2007).





When looking at a timeslice, the viewer sees areas that are covered by peaks and troughs, and so we need to understand what controls the width of an event on the display. The fi rst control is the lateral continuity of the stratigraphic feature generating the refl ection(FIGURE 4.22A). For example, does the refl ection represent a laterally continuous feature (e.g., a sheet sand) or a laterally discontinuous feature (e.g., a channel)? Next is the structural dip (FIGURE 4.22B). Flat-lying strata will cover larger areas on a timeslice than dipping strata. An analogy is a geologic map. Flat-lying strata cover larger areas of the map than steeply dipping strata. Finally, we need to consider the frequency of the refl ection event (FIGURE 4.22C). For example, if dip remains constant a broad trough (low frequency) will cover a larger area on a timeslice than a narrow (high-frequency) trough. This variable is analogous to the effects of formation thickness on a geologic map. For a constant structural dip, a thicker formation will cover a larger area than a thinner unit.

Some software packages allow the interpreter to take inclined slices through the data (FIGURE 4.23). These types of slices can be a quick look approach for cutting through the seismic cube more-or-less parallel to the stratigraphy in the presence of some structural or stratigraphic dip.

If two or more different versions of a seismic data cube are available, an interpreter may wish to view those two volumes at once. Previously we discussed the possibility of overlying wiggle traces from one version of the data onto a variable density display of another version. This technique cannot be applied when viewing timeslices in most software packages. Some interpretation software allows the interpreter to corender two different versions of the data. The software employed in FIGURE 4.24 corenders the two attributes by defi ning one attribute (in this case amplitude) using color and the other attribute (semblance) using shading (as if light was shining on the data), a technique sometimes referred to as bump mapping in graphics programming. Note how the combination of amplitude and semblance effectively highlights the faults. This corendered display shows a seismic cube that has had a portion (denoted by the yellow lines) removed so that we can see into the middle of the cube. Hart (2008b) illustrated examples of corendering semblance with an

FIGURE 4.22:Factors affecting the width of seismic refl ections on timeslices

FIGURE 4.23:Comparison of a timeslice (lower) and inclined slice (upper) through a 3-D seismic cube

FIGURE 4.24:Corendering of a amplitude and semblance (coherency) volumes can be helpful for fault interpretation

Figure 4.22: Factors aff ecting the width of seismic refl ections on timeslices

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A) The lateral extent ofthe stratigraphic features.

B) Structural dip. Thesteeper the dip, the narrower the event will be on a timeslice.

C) The frequency ofthe event. A higher frequency event (top) will be narrower on a timeslice than a broader (lower frequency) event.

Figure 4.23: Comparison of a timeslice (lower) and inclined slice (upper) through a 3-D seismic cube

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A vertical transect through the cube is shown for reference. The timeslice clearly cuts across the stratigraphy. The inclined slice is approximately parallel to the stratigraphy in the part of the data volume at left. The color bar used for the slices is diff erent from that used for the vertical transect in order to enhance their visibility. Seismic data courtesy SEI.

Figure 4.24: Corendering of a amplitude and semblance (coherency) volumes can be helpful for fault interpretation

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A) Intersecting timesliceand vertical transect through a 3-D seismic volume. Some faults are recognizable as refl ection off sets or terminations. Seismic amplitudes are shown in blue-white-red color bar,

B) The same transectshowing amplitude corendered with semblance. The combination of refl ection off sets and terminations (seen in the amplitudes) and lineations (seen in the semblance) better indicates the locations of faults. From Kirshner and Hart (2009).




attribute called sweetness in order to identify sand-fi lled channels in siliciclastic successions.

There are times that we may wish to see how amplitudes change along a horizon that we have picked in a seismic dataset. Perhaps the amplitude changes are related to changes in acoustic impedance that, in turn, are related to changes in porosity, shale content, or hydrocarbon saturation. Alternatively, perhaps the amplitude changes are related to changes in thickness that give tuning effects such as those discussed in SECTION 2.5. Whatever their origin, by looking at a map-view representation of the amplitude changes we hope to learn something about the distribution of physical properties of interest at a particular stratigraphic level.

The term horizon slice is used to describe displays that show changes in amplitude along a horizon that has been mapped in the seismic data. Alternatively, sometimes interpreters refer to them as amplitude maps of a particular horizon. Selected horizon slices are shown inFIGURE 4.25. Note that, as with all seismic displays, the color bar used to display the data can have a signifi cant impact on the interpretability of a horizon slice. A seismic interpreter needs to be able to examine horizon slices and recognize stratigraphic or structural features, hydrocarbon effects, acquisition and processing artefacts, etc.

Faults may also be used to generate slices through a 3-D seismic cube (fault slices). The slices can be moved laterally into either the hanging wall or footwall. Dee et al. (2005) showed how fault slices can be used to image fault intersections.

Sometimes an interpreter may wish to look at a slice through the data cube at a stratigraphic level that cannot be mapped. This might be the case, for example, if the objective is to look for channels at the level of a sequence boundary. The channels may be sand fi lled, but otherwise encased in fi ne-grained deposits above and below the sequence boundary. In this case, we might expect to have refl ections generated at the top and base of the sands, but no mappable refl ections might be present in the heterolithic interfl uve deposits. As such, and especially if the strata have been tectonically tilted or otherwise deformed, we need a way of slicing through the data parallel to the stratigraphy.

FIGURE 4.25:Sample horizon slices (amplitude maps)

Figure 4.25: Sample horizon slices (amplitude maps)

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C) Amplitude map of a Cretaceous horizon from western Egypt. Gaps (black) in the surface, generally running from upper left to lower right, represent fault-heave polygons (see CHAPTER 6)but otherwise there is no obvious relationship between structure and amplitude, and the relationship between amplitudes and lithology is uncertain because of a lack of well data control. Image covers an area of approximately 970 km2, and is modifi ed and reproduced with permission from Osman (2006). In these three examples and other cases, an interpreter needs to understand how changes in physical properties (lithology, porosity, pore-fi lling fl uids, etc.), thickness (CHAPTER 2), structure, acquisition, processing and other variables might aff ect the distribution of amplitudes in order to properly interpret the images.

B) Amplitude map of a Cretaceoussandstone from Alberta. McCullagh and Hart (2010) interpreted the amplitude trends in this image to be related to strandplain progradation (beach ridges), channels, interference eff ects and poor data zones below a modern valley. Seismic data cover an area of approximately 400 km2.

A) Amplitude map of a Cretaceoussandstone at Teapot Dome in Wyoming. The highest amplitudes (red, yellow) form a somewhat oval-shaped area that is elongate approximately parallel to the crest of the NNW-SSE striking anticline. As discussed in CHAPTER 8, this relationship between amplitude and structure suggests that the high amplitudes are related in some cases to the presence of hydrocarbons, although that explanation is unlikely here. Data cover an area of approximately 31 km2.




One way of approximating a slice along an unmappable depositional surface is to generate stratal slices (e.g., Hardage and Remmington, 1999). There may be a mappable seismic refl ection that is close to, and stratigraphically concordant with, the stratigraphic levels that we seek to view. In this case, the continuous horizon is picked as a reference horizon in the seismic data and then the interpretation software is used to generate a new phantom horizon the stratal slice that is parallel to, but shifted up or down from, that reference horizon (FIGURE 4.26). Brown (2004) suggested keeping the name horizon slice for these views, however a stratal slice does not necessarily correspond to a mappable seismic horizon.

A comparison of a timeslice and a stratal slice through a relatively undeformed (i.e., mostly fl at lying) portion of a basin is presented in FIGURE 4.27. The timeslice (part B of that fi gure) shows a channel feature, but the stratal slice (part C; generated by slicing down from an overlying reference horizon) more clearly shows the channel margins, and tributaries to the main channel, even in this relatively undeformed area. In more steeply dipping, folded or faulted areas, timeslices are highly unlikely to provide adequate displays of stratigraphic features.

The original approach for generating stratal slices (e.g., Brown et al., 1981; Hardage et al., 1994) was to start by picking a reference horizon (FIGURE 4.28A) and then to generate a new volume that has been fl attened on that horizon. Timeslices through that new, fl attened volume (FIGURE 4.28B) will be parallel to the reference horizon used for the fl attening, and so will be stratal slices. One problem with this approach is data proliferation the new fl attened volume will be stored on disk along with other any versions of the data including, perhaps, volumes that have been fl attened on other horizons in the dataset. These volumes use up disk space and need to be regenerated each time modifi cations are made to a horizon pick. To solve this problem, some software packages allow the user to create stratal slices by interactively scrolling up and down through the data parallel to the reference horizon (e.g., FIGURE 4.28C). This latter, newer, approach is preferable.

A restriction that can be encountered when working with

FIGURE 4.26:Principle of stratal slicing

FIGURE 4.27:Stratal slicing through a real 3-D seismic volume

FIGURE 4.28:Two different ways of stratal slicing

Figure 4.26: Principle of stratal slicing

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An interpreter may wish to slice through the seismic cube at the level of some stratigraphic features that cannot be mapped.

A) This image shows some channelizedsandbodies that would not correspond to a continuous refl ection that can be mapped seismically. A timeslice (dashed turquoise line) crosses the stratigraphy and would not show the stratigraphic features.

B) To slice through the data at that level,the interpreter maps a refl ection at the top of the underlying continuous sand (red), and then

C) generates a phantom horizon (blue)that is parallel to, but higher than the underlying horizon.

Figure 4.27: Stratal slicing through a real 3-D seismic volume

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B) Timeslice through thedata at 990 ms shows a channel with some poorly defi ned tributaries. The timeslice cuts across the stratigraphy, which is not perfectly horizontal.

A) Vertical transectshows some discontinuous troughs (red) at approximately 990 ms. Note reference horizon (green) for subsequent stratal slicing at approximately 945 ms.

C) A stratal slice 44 ms below the green horizon in part A is concordantwith the stratigraphy at the level of the channel, and shows the channel margins and distributaries more clearly. Data courtesy CNL.

Figure 4.28: Two diff erent ways of stratal slicing

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A) Seismic data showing folded stratigraphy. Horizon shown in green will be referencefor horizon stratal slicing.

B) Approach 1. The data are fi rst fl attened onpicked horizon and the new volume is saved. Timeslices (red) through this new volume will be parallel to the reference horizon, eff ectively making them stratal slices.

C) Approach 2. The user interactively slices upor down through the data parallel to the reference horizon. Approach 2 is preferred (less data proliferation) but not all software packages allow this fl exibility.




stratal slicing, as described above, is that the stratigraphic features need to be parallel to the reference horizon in order to be properly imaged. In the case of a diverging set of refl ections, neither slicing up or down (from reference horizons at the base or top of the succession respectively) will be adequate because eventually the stratal slices will begin to cut across the stratigraphy. In this case, it may be necessary to use two reference horizons, one at the top and the other at the base of the succession, and make stratal slices that are proportionately spaced between those two reference horizons (FIGURE 4.29). These types of stratal slices are commonly known as proportional slices. Wood (2007) used proportional slices to study fl uvial systems.

In some cases, especially when the stratigraphy or structure is complex, identifying the best way of slicing through a 3-D data cube can be problematic. FIGURE 4.30 shows a hypothetical example where none of the stratigraphic surfaces could be used to slice through the data parallel to the stratigraphy.

Volume VisualizationAs noted previously, when working with a 3-D seismic data volume, some software packages allow us to visualize the entire cube. What we see are three, mutually perpendicular but essentially two-dimensional, faces of the three-dimensional data cube. Perhaps we want to see into the cube to look at the 3-D form of features of interest.

Volume rendering, also referred to as volume visualization, is a technique that will help us to look into the cube7. Recall that our 3-D seismic volume consists of a series of voxels that store amplitude values (FIGURE 4.19). If we are preferentially looking for high-amplitude troughs, we might want to make all other amplitudes in our cube transparent. To do so, we adjust the opacity of the data, where opacity is the opposite of the transparency (i.e. voxels that have low opacity become invisible). As shown in FIGURE 4.31, some type of software interface allows us to adjust the range of amplitude values to make transparent, as well as the color bar used for the display. In this way we can see into the cube to examine the 3-D distribution of

FIGURE 4.29:Proportional slicing

FIGURE 4.30:A hypothetical example where none of the stratigraphic surfaces could be used to slice through the data parallel to the stratigraphy

Figure 4.29: Proportional slicing

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Two reference horizons, A and B, are mapped. Slices through the data (proportional slices) are then generated at constant ratio distances between the two reference surfaces.

Figure 4.30: A hypothetical example where none of the stratigraphic surfaces could be used to slice through the data parallel to the stratigraphy

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It may not always be possible to slice through the data parallel to the stratigraphy, especially in areas of complex stratigraphy. This hypothetical example shows three stratigraphic units (each represented by a diff erent color) and a associated stratigraphic surfaces. Evaluate the use of timeslices, horizon slices, proportional slices or some other type of slice for slicing through each of the three units parallel to the stratigraphy.

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7 Volume rendering techniques were originally borrowed from the medical industry to view CT or MRI scans. See: http://en.wikipedia.org/wiki/Volume_rendering.




features sharing common amplitude values.ANIMATION 1 is a volume rendering of some seismic data that shows high-amplitude igneous intrusions.

Kidd (1999) and Sheffi eld et al. (1999) discuss various aspects of volume rendering, including choice of color display and how much data to volume render at once. For example, instead of volume rendering an entire 3-D seismic cube, it may be preferable to examine thinner opacity slabs through the data, and interactively move those slabs up and down through the data volume. An example of some stratigraphic features seen in an opacity slab is shown in FIGURE 4.32. Moving opacity slabs through a data volume can be a highly effective reconnaissance tool when looking for features that can be defi ned using amplitudes.

VisualizationOnce faults or horizons have been interpreted, it can be a good idea to interactively visualize the interpretations. Seismic transects, mapped horizons, faults, wells and (potentially) other features might be included in the visualization. We might spin the interpretations and data around to look at them from different perspectives, we might change color bars, we might turn different features on and off. Different software packages have different capabilities.

Interactive visualization of 3-D cubes can be very helpful for at least three reasons.

It helps a seismic interpreter to do a better interpretation. Rapidly scrolling through a data cube can help an interpreter to identify subtle structural or stratigraphic features. Our eyes and brain are wired to detect motion, such as prey moving through the forest or refl ections that come and go as we scroll through a dataset. Interpreters can also see and fully exploit the data using stratal slices, inclined planes, arbitrary lines, opacity slabs, etc. These views can help an interpreter to see features that are not visible on vertical transects or even timeslices. Finally, visualization allows interpreters to more effectively perform quality control on their interpretations. For example we might

ANIMATION 1Volume-rendering of a 3-D seismic cube showing igneous intrusions. Courtesy CGG-Veritas.

(Animation fi rst appears in CHAPTER 1.)

Figure 4.31: Volume rendering

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A) A 3-D seismic cube. The histogram at lowerright shows the range of seismic amplitudes in the data. All of these values are opaque. As such, we see three 2-D surfaces when looking at the cube the top, front and side. We cannot see into the cube.

B) A volume-rendered visualization of the samecube. Inspection of the histogram shows that the interpreter has adjusted the opacity so that only the high-amplitude peaks are visible (red/yellow in this case; see red arrow). We can now examine the cube to look at the 3-D distribution of high-amplitudes. Seismic data courtesy SEI.

FIGURE 4.31:Volume rendering

FIGURE 4.32:Opacity slab through some seismic data

Animation 1 Back to Chapter

Figure 4.32: Opacity slab through some seismic data

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A thin window of a 3-D seismic cube has been volume rendered to reveal some meandering channels. Reproduced with permission from Kidd (1999).




see places where horizons cut across faults, or faults intersect, in physically impossible ways (FIGURE 4.33).

To help the interpreter to better understand relationships between different variables. For example, FIGURE 4.34 is an oblique view of a 3-D survey that shows a horizon with an amplitude extraction draped over it, and some faults. The interpreter may be looking for relationships between amplitudes, structure, stratigraphic features and hydrocarbon accumulations. It might be possible to look at 2-D maps of amplitudes and structure separately, but it is usually more instructive to combine those two pieces of information into a display that can be interactively rotated, rescaled and otherwise manipulated by the interpreter on a real-time basis.

Visualization tools allow an interpreter to quickly and effectively communicate across subsurface disciplines, for example to discuss drilling target choices with drillers, reservoir engineers, management or even other geologists and geophysicists who were not part of the interpretation team. Although 2-D maps will undoubtedly be needed at some point, visualization technologies can be more effective tools than maps for conveying complex relationships between structure, stratigraphy, amplitude or other variables (Figures 4.34, 4.35).

There may be times when we may want to interactively immerse ourselves into the data and, starting in the 1990s, some oil companies began investing in different types of immersive environments. The interpreter wears glasses that help him/her to see the data in 3-D. Dorn (1998) discussed various types of immersive environments. Although this type of technology was predicted by some to eventually replace workstation-based interpretation methods, for a variety of reasons immersive visualization technology has not replaced desktop-based methods as the norm for seismic interpretation. In fact, many companies who had invested in visualization centers have subsequently modifi ed those rooms for other purposes. It appears that,

FIGURE 4.33:Visualization of faults, a horizon, and a seismic transect that has been made semi-transparent

FIGURE 4.34:Visualization of faults, a horizon with an amplitude overlay, and some seismic data that have been made semi-transparent

FIGURE 4.35:Comparison of a time-structure map of the top of a leveed channel complex and a 3-D surface visualization showing a type of curvature draped over an illuminated rendered surface of the same horizon

Figure 4.33: Visualization of faults (red, orange, purple and blue), a horizon (green) and a seismic transect (red-white-blue) that has been made semi-transparent

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This view was generated interactively to perform quality control on an interpretation. Note how the horizon crosses the red fault in the foreground, a probable data-picking error because the horizon should be much higher up in this fault block because of the signifi cant normal displacement along the fault (based on micropaleontological studies). Seismic data courtesy SEI.

Figure 4.34: Visualization of faults (red, orange and blue), a horizon with an amplitude overlay (high amplitudes in red and green, low amplitudes in blue), and some seismic data that have been made semi-transparent

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This type of visualization can be useful for examining relationships between diff erent variables, such as amplitudes (a possible hydrocarbon indicator), folding and faulting.

Seismic data courtesy of SEI.

Figure 4.35: Comparison of a time-structure map of the top of a leveed channel complex (top) and a 3-D surface visualization (below) showing a type of curvature (a horizon attribute; see Chapter 7) draped over an illuminated rendered surface of the same horizon

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The lower image more clearly displays relief on the surface. Modifi ed from Hart and Sagan (2006).




at least at present, the most common use for immersive visualization is for making presentations, especially at meetings when it is important for a group of individuals (e.g., geologists, geophysicists, engineers) to all have the same level of understanding about the nature of the drilling target.

4.6 Seismic AttributesThe discussion so far in this chapter has focused primarily on ways of viewing the original seismic amplitude data, but there is increasing interest in the use of seismic attributes for viewing and analyzing seismic data. Seismic attributes have been defi ned in a variety of ways. For example, Brown (2004) defi ned seismic attributes as derivatives of a basic seismic measurement, and suggested that the latter consist of measurements associated with time, amplitude, frequency and attenuation. Chen and Sydney (1997) defi ned an attribute as a specifi c measurement of geometric, kinematic, dynamic or statistical features derived from seismic data. Other defi nitions exist, but essentially a seismic attribute is:

A quantitative measure. A numerical amplitude measurement is an attribute, whereas a descriptive seismic facies (e.g., chaotic refl ections; CHAPTER 7) is not.

Derived from the seismic data itself or from the interpretations. Data-derived attributes could be derived before or after stacking. Attributes derived from amplitude-variation-with-offset analyses (see CHAPTER 8) are an example of pre-stack attributes, whereas complex-trace attributes (instantaneous frequency, instantaneous phase, etc.; see below) or coherency attributes (CHAPTER 3) are examples of post-stack attributes. Attributes derived from seismic interpretations might include horizon dip, azimuth or curvature (CHAPTER 6).

Attributes are derived and analyzed for two primary purposes: 1) to help detect stratigraphic or structural features (feature detection), and 2) to make quantitative predictions of rock properties (discussed in CHAPTER 8). Applications of attribute analyses were presented by Brown (2004) and Chopra and Marfurt (2007). Chopra and Marfurt (2005, 2006) provided a historical perspective on the development and use of seismic attributes. Taner (2000) compiled a lengthy list of seismic attributes and discussed their derivation and application.

Many different types of seismic attributes have been formulated and various attempts have been made to classify them. For example, Browns (2004) classifi cation included over 70 seismic attributes. Different software packages, oil companies and geophysical service companies derive other attributes that are not included in Browns compilation. The details of how these attributes are derived, and their utility (or lack thereof) is not always discussed. As such, this section focuses on a subset of post-stack attributes associated with seismic amplitudes and with complex trace analyses.

AmplitudeSeismic amplitude is perhaps the simplest attribute to understand. As discussed in Chapters 2 and 3, the controls on seismic amplitude include:

Changes in acoustic impedance along an interface. These changes might be due to changes in lithology, changes in porosity, changes in fl uid content or some combination of these factors.

Interference from closely spaced refl ections. Recall from CHAPTER 2 that




constructive and destructive interference effects (e.g., tuning) can affect the amplitude of a seismic refl ection.

Acquisition and processing effects, such as variations in stacking fold within a dataset, wave propagation phenomena such as focusing of energy in synclines, etc.

Ideally, seismic amplitude variations represent changes in geological factors rather than acquisition and/or processing parameters, but this

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