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  • 1. Chapter 13 Interpreting 3-D Seismic Data by Geoffrey A. Dorn
  • 2. Geoffrey A. Dorn Geoff Dorn received his bachelor’s degree in astrophysics (1973) from the University of New Mexico and a Ph.D. in geophysics (engineering geoscience, 1980) from the University of California, Berkeley. He joined ARCO’s Exploration and Production Technology group in 1980, spending his first two years in seismic acquisition research. From 1982–1987 he directed ARCO’s interactive interpretation research group but left management to pursue technical research interests in horizon and volume attribute analysis and 3-D visualization. In 1993 Dorn was named an ARCO Research Advisor for his contributions in 3-D seismic interpretation research and technical service. He returned to management in 1997 to direct ARCO’s 3-D visualization research efforts. A member of the SEG Research Committee since 1990, he has helped organize several postconvention research committee workshops and was chairman of the 1993 SEG Summer Research Workshop on 3-D seismology. His interests include 3-D visualization, 3-D seismic interpretation, attribute analysis, and geophysical reservoir characterization. He is an active member of SEG, EAGE, and AAPG.
  • 3. Overview Introduction Modern 3-D seismic interpretation involves interactive workstations and information technologies to interpret large volumes of data accurately and efficiently. The 3-D interpreter must understand the concepts of geology and seismology as well as the algorithms implemented in computer-aided interpretation tools to use these systems effectively. A good source place for a broad overview of the topic is Interpretation of Three-Dimensional Seismic Data by Alistair Brown. This chapter focuses on applications and techniques essential to the efficient and effective interpretation of 3-D seismic data. In this chapter This chapter contains the following sections. Section Title Page A Basics of Interpreting 3-D Seismic Data 13–4 B Stratigraphic Interpretation Techniques of 3-D Data 13–16 C Attributes 13–20 D Visualization Techniques for 3-D Data 13–23 E References 13–27 Overview • 13-3
  • 4. Section A Basics of Interpreting 3-D Seismic Data Introduction Three-dimensional (3-D) seismic data interpretation may be approached many different ways, depending on one’s purpose. Data quality determines the level of the interpretation. For example, structural interpretation requires the least quality. Stratigraphic interpretation requires better quality data. Attribute analysis of 3-D data requires the highest quality data. The following procedure is suggested for interpreting 3-D seismic data. Step 1 Determine the goals for interpreting the 3-D data. Do you want to interpret straucture only or structure and stratigraphy, etc.? 2 Preview the 3-D data volume. 3 Pick critical horizons within the data using the appropriate autopicker. 4 Make strategic vertical, horizontal, and horizon slices. 5 In this section Action If attribute analysis is possible and necessary to the interpretation goals, decide which attributes will best show the required geological features. This section contains the following topics. Topic Page Data Preview Two- or Three-Dimensional Interpretation 13–7 Picking Horizons in 3-D Data 13–10 Surface Slicing 13–13 Interpreting 3-D Seismic Data 13-4 13–5 13–15 • Interpreting 3-D Seismic Data
  • 5. Data Preview Why preview? Why preview the 3-D volume? You have just received the final migrated volume from processing (late), and your manager is hounding you to produce maps at the reservoir or prospect level because he is looking at a deadline for a drilling commitment in the near future. Why not just dive in, do some quick picking at the reservoir level, grid and contour the picks, do a rough time-to-depth conversion, and move on? In most cases, this approach causes problems ranging from a loss in efficiency, to minor errors and omissions, to major inaccuracies in the interpretation. Data preview provides an overview of the gross structural and stratigraphic environment. Variations in data quality can be identified, giving the interpreter an idea of the relative difficulty of interpretation in different areas. It is possible to identify the initial set of seismic horizons to interpret and the manner in which those horizons should be interpreted. Preview of a discontinuity volume could prove to be extremely valuable by obtaining an overall picture of the major faulting in the survey and by providing a better initial picture of any depositional stratigraphy imaged in the data. This information allows the interpreter to very effectively plan the interpretation of the volume and proceed in the quickest, most efficient manner possible. Volume visualization Volume visualization provides an effective tool for data preview. We can view animations of opaque slices through the data volume along any orientation, and we can control the slicing interactively. We can also control the opacity of the volume. By making the data volume partially transparent, we can see the structure of strong reflections prior to doing any interpretation. It may also be possible to isolate elements of depositional systems by controlling the color and opacity mappings. Data preview example The figure on the following page is an opaque volume from a 3-D seismic survey in the southern North Sea Gas Basin. Four horizons are indicated: Top Chalk, Top Keuper, Top Zechstein, and Top Rotliegend. By visualizing the volume with the opacity set so that only the strongest peaks and troughs are opaque, we can see the overall 3-D structure of these horizons prior to interpretation (Figure 1b). Examining Figure 13–1b, we can note that the anticline at the Top Chalk has a different trend than the anticline at the Top Keuper. Some faulting is evident along the north end of the Top Keuper horizon extending roughly parallel to the trend of the anticline. The Top Zechstein and Top Keuper are approximately conformable in this volume, and the strong amplitude reflections from the west are dipping. Top Rotliegend fault blocks are also evident. By using motion (e.g., rotation around the time axis) and stereo displays, the structures, their relationships, and the positions of specific reflections become much more obvious than they are in the still images in Figure 13–1. Basics of Interpreting 3-D Seismic Data • 13-5
  • 6. Data Preview, continued Data preview example (continued) a) b) Figure 13–1. From Dorn, 1998; courtesy SEG. 13-6 • Interpreting 3-D Seismic Data
  • 7. Two- or Three-Dimensional Interpretation Introduction Three-dimensional seismic interpretation is not just dense 2-D seismic interpretation. Although it is certainly possible to interpret the 3-D seismic volume as a dense 2-D grid (in fact, some early interactive interpretation systems actually encouraged this), this is neither an effective nor an efficient approach to the interpretation. The results are generally of poorer quality and require significantly more effort than interpreting the data in a 3-D fashion. The 2-D approach Two-dimensional interpretation of a 3-D seismic volume involves limiting your views of the data, and your interpretation, to lines and cross-lines. Typically, this approach to interpretation has its roots in two things. First, the interpreter is comfortable with seismic sections. Second, the interpreter new to 3-D interpretation may simply decide that "there is too much data to possibly look at all of it." Two-dimensional interpretation of the 3-D volume results in loss of information and is the least efficient approach to interpreting the data. Modern interactive systems provide a variety of tools that allow interpreters to perform the interpretation in a more 3-D fashion. Never enough data The first rule of 3-D seismic interpretation is that there is never enough data. The problem we are trying to solve with seismic interpretation is always underdetermined. There is never enough information to uniquely define the geology of the subsurface in the area of the 3-D survey. In some instances, you may decide to disregard some of the data collected. If you do not use it all, then you are reducing resolution and control. Weaknesses of vertical sections A second rule for 3-D seismic interpretation is that the survey is always oriented at 45o to the trend of faults, channels, and other features of interest. There will always be faults or channels oriented at angles between 0o and 45o relative to the orientation of the vertical section you are interpreting. If you only look at vertical sections, you will always miss important features of the geology. Example of seeing faults The figure on the following page shows several images from a 3-D survey in the Gulf of Mexico. Figure 2a is a dip magnitude map at an interpreted horizon in the data, showing several steep dip (pink) lineaments associated with normal faults that cut the horizon. Arrows b, c, and d show the orientation and direction of three traverses cut through the volume at angles of approximately 90o, 45o, and 10o to the trace of the fault in the center of the horizon. Figures 2b, c, and d are traverses b, c, and d, respectively. The fault is clearly interpretable in the centers of the traverses that cut the fault at angles of 90o and 45o. However, it would be very difficult to interpret the fault on Figure 2d, the oblique traverse. This geometric effect produces a blind zone on vertical sections that are oriented between +/-20o of the trend of a fault. As a result, if you are only interpreting vertical seismic sections, you will fail to see faults that have trends in this zone. The same phenomenon occurs with depositional stratigraphy. The best section to image a channel is a section oriented at 90o to the trend of the channel. Basics of Interpreting 3-D Seismic Data • 13-7
  • 8. Two- or Three-Dimensional Interpretation, continued Example of seeing faults (continued) Figure 13–2. From Dorn, 1998; courtesy SEG. Start with time slices 13-8 The first step toward 3-D interpretation of a 3-D volume is to use time slices. The value of time-slice interpretation for faults is fairly obvious. Regardless of the strike of the fault, most fault surfaces intersect the time slice at an angle between 45o and 90o to the plane of the time slice • Interpreting 3-D Seismic Data
  • 9. Two- or Three-Dimensional Interpretation, continued Computer limitations Depositional systems are typically more interpretable on time slices than they are on vertical sections. The figure below is a time slice from a 3-D survey in the North Sea. A portion of a braided stream system is clearly evident. Figure 13–3. From Dorn, 1998; courtesy SEG. The following figure is a traverse cut through the data in a direction perpendicular to the channel system. Horizontal arrows indicate when the channel system occurs, and vertical arrows show the location of each of a number of individual channels cut by the traverse. It is safe to say that most if not all of these channels would have been missed if the interpretation had been limited to vertical sections. Figure 13–4. From Dorn, 1998; courtesy SEG. Basics of Interpreting 3-D Seismic Data • 13-9
  • 10. Picking Horizons in 3-D Data Introduction Computer-assisted interpretation of seismic data is one of the areas where the tools of modern interactive interpretation systems have made the most significant impact. The interpreter needs to choose the technique that will allow the best interpretation to be achieved in the most efficient manner possible. In terms of interpretive efficiency, techniques would typically be ordered in the following way, from least efficient to most efficient: • Manual picking • Surface-slicing • Interpolating • Voxel tracking • Autopicking Manual picking Manual picking is interpreting horizons on lines, cross-lines, time slices, and traverses by hand. This is the most familiar technique. It is also, by far, the least efficient horizon interpretation technique in terms of interpreter time and effort. While interpreting manually, the interpreter is looking for some degree of local continuity in the data and local similarity of character to identify the event to be picked. Picking by interpolation Interpolation is somewhat more efficient than manual interpretation. The use of interpolation, however, assumes that a horizon is locally very smooth and perhaps linear (or planar in two dimensions) between control points. If this assumption is violated between control points (e.g., there is a fault between the interpreted lines), then the results will be poor. Autopicking Autopicking (or autotracking) has been around in interactive interpretation systems since the early 1980s. The concept behind autopicking is quite simple. The interpreter places seed picks on lines and/or cross-lines in the 3-D survey. These seed points are then used as initial control for the autopicking operation. The algorithm looks for a similar feature on a neighboring trace. If it finds such a feature within specified constraints, it picks that trace and moves on to the next trace. Simple autopickers allow the user to specify a feature to be tracked, an allowable amplitude range, and a dip window in which to search. The figure to the right is a sketch of how such an autopicker works. If any of the search criteria are not met (amplitude out of range, no similar feature in the dip window, etc.), the autotracker stops tracking at that trace. More sophisticated autopickers let the user specify additional criteria to control the picking. Figure 13–5. From Dorn, 1998; courtesy SEG. 13-10 • Interpreting 3-D Seismic Data
  • 11. Picking Horizons in 3-D Data, continued Feature and cor- There are two classes of autopicker: relation trackers 1) Feature trackers 2) Correlation trackers The feature tracker searches for a similar configuration of samples within the dip window but does not perform any correlations between traces. It tries to track a configuration of samples on the seismic trace that defines a peak, trough, zero crossing, etc., from trace to trace. A correlation tracker takes a portion of the seismic trace around the seed pick and correlates it with a neighboring trace through a set of lag times constrained by the specified dip search window. If a lag time is found with an acceptable correlation quality factor, then the pick on the new trace is accepted and the picker moves on to the next trace. Clearly, the correlation autopicker is much more computationally intensive than the feature tracker. It is also typically more robust in its picking. Autopicker pathways Another aspect of autopickers that must be considered is the path that the autopicker follows through the data. Many are not true volume autopickers because they track through the data only in the line or cross-line direction. In other words, the path they follow through the data is not truly three dimensional or even two dimensional. Some autopickers make consecutive passes through the data—one pass in the line direction, the next in the cross-line direction. A few autopickers actually move through the data in a true 2-D sense, expanding around control in both the line and cross-line directions in a single pass. The more sophisticated the path the autopicker follows, the more useful it will be in infilling the horizon from the interpreted seed points. The type of control the interpreter picks in the volume prior to autopicking should in part be dependent on the type of algorithm being used and the path it follows through the data. Voxel tracking A technique called voxel tracking has become available with the advent of volume rendering and visualization. A voxel is a volume element. In a 3-D seismic volume, it is a sample. Voxel tracking is conceptually related to autopicking in the sense that an event or feature is tracked through the volume starting from seed control points picked by the interpreter. Voxel trackers, however, tend to follow a true 3-D path through the data. Starting at the seed voxels, the voxel tracker searches for connected voxels that satisfy the search criteria specified by the user. The search is typically conducted in line, cross-line, and time directions. Basics of Interpreting 3-D Seismic Data • 13-11
  • 12. Picking Horizons in 3-D Data, continued Voxel tracking assumptions The figure below is a sketch of a simple voxel tracking algorithm and its behavior under two different continuity constraints. Six-way connectivity restricts the search from one voxel to only the neighboring voxels that are connected face to face. Twenty-six-way connectivity allows the search to proceed between neighboring voxels that are connected face to face, edge to edge, or corner to corner. The connectivity constraint that is used affects the outcome of the voxel tracking. Figure 13–6. From Dorn, 1998; courtesy SEG. Like autopicking, voxel tracking assumes that the data are locally continuous, consistent, and connected or smooth. Both techniques assume a consistent phase is being interpreted from the data. Voxel tracking pros and cons Because volume-rendering techniques typically place the entire volume being visualized into RAM memory on the workstation, and since the tracking algorithm is computationally simpler than most autopickers, voxel tracking can be many orders of magnitude faster than autopicking—when it works. Most voxel tracking algorithms are more sensitive to poor signal-to-noise ratio than correlation autopickers. However, on reflections with a good signal-to-noise ratio, voxel tracking is usually the most efficient approach to picking the horizon. 13-12 • Interpreting 3-D Seismic Data
  • 13. Surface Slicing Introduction Surface slicing is a novel approach to interpreting seismic horizons. This technique involves visualizing and interpreting areally finite portions of horizons on time slice slabs of the data. The slab thickness is a weak function of the bandwidth of the data and a stronger function of the dip of the reflections. For a detailed description of the technique see Stark (1996). The figures below show the general concept of the technique. Figure 13–7 is a perspective view of a horizon structure (a) and the same structure split into 12-ms slabs and dissected (b). Suppose Figure 13–7 represented a reflection (say a peak) in a 3-D survey. This structure could be cut into time slabs and dissected. Figure 13–8 shows what these time-slice slabs might look like, where within each slab only the peaks are displayed. The dissected portions of the 3-D structure appear as annuli that fit perfectly within each other. Interpreting the horizon consists of selecting those elements that fit together, somewhat like assembling a jigsaw puzzle. (a) (b) Figure 13–7. From Stark, 1996; courtesy SEG. Figure 13–8. From Stark, 1996; courtesy SEG. Basics of Interpreting 3-D Seismic Data • 13-13
  • 14. Surface Slicing, continued Surface slicing assumptions For an interpreter who is practiced in surface slice picking and in the appropriate settings of the various parameters that control the algorithm, surface slicing can be an extremely efficient way to interpret horizons. Since entire areas on the horizon are picked with each click of the mouse button, this technique is not very sensitive to the number of traces in the 3-D survey. It assumes local continuity and connectivity in the data, and it assumes a consistent phase is being interpreted. It is less sensitive to discontinuities and poor signal-to-noise ratio than voxel tracking or autotracking because it is not entirely an automatic technique; the interpreter controls the technique and frequently can achieve better results because interpretive judgment can be applied. 13-14 • Interpreting 3-D Seismic Data
  • 15. Interpreting 3-D Seismic Data Choosing the appropriate technique Clearly, any interpretation involves some combination of the techniques listed above. The strongest events might be voxel tracked. Many of the remaining events might be surface sliced. In some areas an autopicker might be necessary. All three of these techniques typically leave some holes or unpicked traces in the horizon that might be infilled by interpolation. And all of these techniques require some interpretation by hand on seismic sections to provide initial control to the picking process. The techniques should be viewed as a set of tools that the good interpreter knows how to apply to achieve the best interpretation in the shortest period of time. Reviewing picks While these techniques are all extremely valuable, we must understand that computerassisted horizon picking is only as good as the algorithm, combined with its application by the interpreter. All of these techniques produce some cycle skips in the data (e.g., in areas of poor signal-to-noise ratio or in areas where the event being interpreted splits into a doublet). The interpreter must realize this and review the picks in the volume, on sections, and on attribute maps of the horizon that might accentuate cycle skips in the interpretation (e.g., dip magnitude maps). Cycle skips are also very obvious when 3-D visualization is used to view the horizon. Picking faults Computer-aided interpretation of fault surfaces is significantly less advanced than horizon interpretation. With horizons, a feature of the data—a reflection event—can be tracked in a controlled fashion through the volume. Fault surfaces, however, typically do not give rise to reflection events in the data. Faults are characterized by discontinuities in the horizons and by no-data zones, which are (at least conceptually) more difficult to track. Fault autopicking Fault surface autopicking algorithms have begun to appear in 3-D interpretation systems. In one fashion or another, these algorithms attempt to track discontinuities in the volume. Some require initial interpretive input—seed picks on fault surfaces that are to be tracked. Others require a preprocessing step that creates a volume to highlight discontinuities; then the discontinuities are tracked to form fault surfaces. Although these techniques are relatively new, they are developing rapidly and promise to significantly improve the efficiency of one of the most tedious aspects of 3-D structural interpretation. Nonvertical slices Seismic sections are vertical cuts through a seismic volume. Nonvertical cuts through a seismic data volume are also called slices. As illustrated in Figure 13–5, horizonal slices can be of several types: • Time slices (horizontal cuts of a time volume) • Depth slices (horizontal cuts of a depth volume) • Horizon slices (cuts in the shape of interpreted horizons) • Fault slices (cuts in the shape of interpreted fault surfaces) Depth slices are only available if the data delivered from the processor are converted to depth. Fault slices require data with mappable fault surfaces. Basics of Interpreting 3-D Seismic Data • 13-15
  • 16. Section B Stratigraphic Interpretation Techniques of 3-D Data Introduction Besides the interpretation of vertical slices for stratigraphic features, 3-D seismic data also may be interpreted in nonvertical slices. These views include time, horizon, and proportional slices. In this section This section contains the following topics. Topic Page Time and Horizon Slices 13–17 Proportional Slices and 3-D Volume Visualization 13–19 13-16 • Interpreting 3-D Seismic Data
  • 17. Time and Horizon Slices Introduction The time slice was described earlier as the first step toward 3-D interpretation of a 3-D seismic volume. A time-slice view of the data is an improvement over vertical sections for the interpretation of depositional systems because it provides the opportunity to see a portion of depositional systems in map view. This view is key to interpreting these systems because it allows a view of the morphology of the system, which facilitates its recognition. Structural effects A time slice provides at best an image of a small portion of a depositional system. Subsequent structural deformation of the depositional surface typically means that only a small portion of a depositional system is imaged on a time slice. In fact, as the structural relief increases, the anomalies on the time slice associated with the structure quickly dominate the image. Horizon slices One way to improve the imaging of the paleodepositional system is to create horizon slices through the 3-D volume. The interpreted reflection (horizon) is an approximation of a paleodepositional surface. Within the time interval where reflections are approximately conformable to the interpreted horizon in three dimensions, the shape of the horizon surface is a reasonable description of the shape of the paleodepositional surfaces. Example vertical slice Figures 13–9, 13–10, and 13–11 illustrate the value of the horizontal slice view of the data. The figure below is a portion of a vertical seismic section from a 3-D seismic survey in the North Sea. The interpreted horizon, at approximately 2 seconds, is the Top Paleocene. Approximately 120 ms below this, at about the level indicated by the arrows, the section crosses a 1-km-wide Paleocene deepwater turbidite channel. Figure 13–9. From Dorn, 1998; courtesy SEG. Stratigraphic Interpretation Techniques of 3-D Data • 13-17
  • 18. Time and Horizon Slices, continued Example time slice The figure to the right is a time slice that intersects a portion of the channel. On both the vertical section (Figure 13–9) and the time slice, the channel is difficult to interpret, even though the feature is quite large. Most of the amplitude patterns on the time slice are associated with structure, not stratigraphy. Figure 13–10. From Dorn, 1998, courtesy SEG. Example horizon slice This figure is a horizon slice 120 ms below the Top Paleocene horizon. The shape of the horizon slice is defined by the shape of the Top Paleocene horizon. This surface is shifted 120 ms down, and the seismic amplitudes are extracted where the shifted surface intersects the 3-D volume of data. The 1-km-wide channel is unmistakable on this view of the data, and both edges of the channel are readily interpretable. Figure 13–11. From Dorn, 1998, courtesy SEG. 13-18 • Interpreting 3-D Seismic Data
  • 19. Proportional Slices and 3-D Volume Visualization Introduction Additional refinement to the horizon-slice approach can be made to accommodate situations where there is growth, or differential deposition, in an interval between two interpreted horizons. For such an interval, the reflections between the two interval-bounding horizons are not parallel to either bounding horizon. To some extent, the shape of the reflections in this interval is intermediate between the shapes of the interval bounding horizons. A set of slices between the two bounding horizons, which more closely mimics the shape of the reflections in that interval, is given by S = H1 + R * (H2 – H1) where: S H1 H2 R = = = = the intermediate surface the shallower horizon the deeper horizon a fraction that varies between 0 and 1 Definition The new surface, S, is the shallower horizon time structure plus a fraction of the isochron between the two bounding horizons. Such a slice is called a proportional slice because it is proportionally between the two bounding horizons. In certain environments that exhibit significant growth in the interval, the proportional slice can provide a significantly improved image of the depositional stratigraphy compared to horizon slices in the shape of either bounding horizon. Creating proportional slices Horizon slices and proportional slices have been available for a number of years in interactive interpretation systems. Some of the implementations are awkward; creating the proportional slice, in particular, may be a multistep process. Ideally, creating these slices should be a fully interactive process, allowing the interpreter to explore intervals of interest in the 3-D seismic volume for indications of paleodepositional systems. 3-D volume visualization of depositional systems Advances in pattern recognition and volume seeding have provided tools that allow the interpreter to seed and pick the depositional system as a 3-D body. This allows the interpreter to see the depositional system as a 3-D object within the seismic volume. Visualizing this object in three dimensions provides visual integration of the stratigraphy of the depositional system with the overprint of current structural relief. Stratigraphic Interpretation Techniques of 3-D Data • 13-19
  • 20. Section C Attributes Introduction Attributes are measurements based on seismic data such as polarity, phase, frequency, or velocity. Horizon attributes were first used in the mid-1980s for interpreting fault traces on reservoir horizons. Since then there has been an explosion in the number of attributes that can be generated and displayed in 3-D interpretation systems. New attributes are added to the list regularly. Because software providers frequently use different names for the same attribute, and since some of the names are rather obscure, it can be very difficult be sure what each attribute represents. In this section This section contains the following topics. Topic Page Basics of Attributes 13–21 Using Attributes for Geological Interpretation 13–22 13-20 • Interpreting 3-D Seismic Data
  • 21. Basics of Attributes Grouping attributes At this time, no general reference includes a complete list of the attributes that can be created. One approach (Brown, 1999) to organizing the attributes is based on whether they are related to the following: • Time • Amplitude • Phase • Frequency in the seismic data An alternative approach might be to group them into these categories: • Horizon attributes (measures of the data that occur along a 3-D surface through the seismic volume) • Volume attributes (measures of the data that occur over an interval of time through the seismic volume) Attribute meanings The meaning and use of some attributes can be quite straightforward. The reflection amplitude at a horizon can, under the correct circumstances, be related to porosity or net pay in a reservoir interval. With other attributes the physical interpretation of a variation in attribute value is somewhat more obscure. For example, arc length (also call reflection heterogeneity) is a measure of the length of the wiggle trace over a specified interval (technically, it is an approximation of the line integral of the trace over the interval). This measure is affected by amplitude, frequency, and the length of the interval. Associating a variation in arc length with a physical change in the geology is possible, but it can be more challenging. Attribute interpretation Attributes are used for both qualitative and quantitative interpretation. An example of qualitative interpretation would be to use maps of dip magnitude, dip azimuth, or residual structure to interpret detailed fault trace patterns on a horizon. An example of quantitative interpretation would be an attempt to correlate attributes with reservoir properties measured in the borehole. A large number of papers illustrating both applications of horizon and volume attributes are published each year in various professional journals. Examples of applications to structural interpretation include Brede and Thomas (1986), Denham and Nelson (1986), Dalley et al. (1989), Rijks and Jauffred (1991), and Dorn et al. (1996). Stratigraphic interpretation from attribute maps is discussed in Enachescu (1993a,b), Reymond and Stampfli (1994), and Dorn (1998). Examples of reservoir characterization from attributes include Dorn et al. (1996) and Brown (1999). Attributes • 13-21
  • 22. Using Attributes for Geological Interpretation Rules of thumb Here are a few rules of thumb that the interpreter should apply when approaching the use of attributes in a 3-D seismic survey: • Consider the geology. • Use different attributes. • Use normalized attributes • Avoid using an interval attribute that involves the summation of a data measure that varies in a cyclic fashion over an interval Consider the geology Consider the geological feature you are hoping to interpret and how varying that aspect of the geology might affect the seismic data. This can help guide the initial selection of attributes and will certainly help with interpreting the resulting data. Using different attributes Don’t forget about using other attributes. It is not uncommon for there to be surprises in the data—unforeseen relationships that make physical sense once they have been discovered. Normalized attributes Be wary of using volume or interval attributes that are not normalized by the isochron of the interval over which they are calculated. This normalization can take the form of dividing the attribute by either the actual isochron or by the number of time samples in the interval. Although this normalization is not appropriate for some interval attributes (e.g., the maximum absolute amplitude in the interval), it is essential for any attribute that involves a summation over the interval. Without the normalization, the lateral attribute variation may simply be showing the variation in the isochron. Cyclic variation of an attribute Avoid using an interval attribute that involves the summation of a data measure that varies in a cyclic fashion over an interval. For example, in several systems it is possible to calculate the sum of trace amplitude over a user-specified interval. Since the trace amplitude has both positive and negative values over most intervals (unless the interval is so small as to include only a half-cycle of the data), the positive and negative values tend to cancel each other out. A second example would be the sum of instantaneous phase over an interval in the volume Know the attribute To apply these rules to attribute interpretation, it is essential for the interpreter to understand what the attribute is measuring in the data—the equation for the attribute. The only way the interpreter can know this is for the software vendor to provide well-written documentation, either on paper or online, that explains the mathematical calculation involved in generating the attribute. This combination—an interest on the part of the interpreter to understand the attributes and a willingness on the part of the vendor to provide that information—is essential to the intelligent and effective use of attributes in 3-D interpretation. 13-22 • Interpreting 3-D Seismic Data
  • 23. Section D Visualization Techniques for 3-D Data Introduction Visualization is the graphical presentation of data in an intuitive fashion to develop an understanding of data and reveal new insight. The key word in this definition is "intuitive." Three-dimensional visualization applied to 3-D seismic data is an attempt to present this data, and its interpretations, in an intuitive fashion—the same fashion in which we perceive the world around us every day. In this section This section contains the following topics. Topic Page Visualization Fundamentals 13–24 Immersive Visualization 13–25 Visualization Techniques for 3-D Data • 13-23
  • 24. Visualization Fundamentals Perceiving 3-D More than half the neurons in the human brain are associated with vision. This significant resource can be applied to the interpretation of 3-D seismic data if the data are presented in 3-D fashion. Humans perceive the 3-D world through a variety of visual cues: • Perspective • Lighting/shading • Depth of focus • Depth cueing • Transparency/obscuration • Stereopsis • Motion parallax • Peripheral vision 3-D visualization Three-dimensional visualization has been used as a part of 3-D interpretation for the last in seismic data 10 years. As each additional visual cue has been added to the 3-D displays of seismic data and seismic interpretations, there have been significant improvements in the 3-D interpretation process. For a discussion of the applications of visualization to horizon attribute analysis, see chapter 8 in Brown (1999). For a more general discussion of the role of 3-D visualization in 3-D seismic interpretation, see Dorn et al. (1995). Improvements using 3-D visualization Three-dimensional visualization improves the efficiency, accuracy, and completeness of the interpretation, integrates large amounts of data in easily understood displays, and significantly improves the communication between different specialists on an asset team and between the asset team and management. If 3-D visualization is not used in the interpretive process, the interpreter will lose productivity and the interpretation will be incomplete or inaccurate. Volume rendering Volume rendering of seismic data, and its use in data preview, is one example of an area where visualization has a major impact on the efficiency of interpretation. In surface-slice interpretation, if a 3-D visualization of the horizon being interpreted is used along with the 2-D surface-slice map displays, the interpreter can be significantly more productive and avoid errors in the interpretation. For a number of years, 3-D visualization of attributes on a lighted 3-D horizon surface has provided a means of interpreting detailed fault patterns in a reservoir interval—faults that would have been missed if 3-D visualization had not been used (Dorn and Tubman, 1996). 13-24 • Interpreting 3-D Seismic Data
  • 25. Immersive Visualization Introduction In 3-D desktop visualization, perspective, lighting/shading, transparency, stereopsis, and in some cases head-tracking (changing the data appropriately for changes in the interpreter’s head location with respect to the screen) has been applied to both surface and volume displays. With each additional visual cue used in presenting the data in 3-D, there have been improvements in efficiency. New insights have been gained about the data, and communication has improved. The next step in visualization is to engage peripheral vision—to become immersed in the data. Facilities for immersive visualization In 1997 ARCO, Texaco, and Norsk Hydro each installed large immersive visualization environments. The Texaco facilities are visionariums, large screens (8–10 ft tall) that curve horizontally through approximately 160o and can curve vertically. Data are projected on the screens using three projectors, each covering one-third of the screen. The sense of immersion is achieved primarily by engaging peripheral vision, filling most of the field of view with data. Engaging peripheral vision ARCO and Norsk Hydro have installed immersive visualization rooms, based on the CAVETM invented at the University of Illinois, which consist of four projection surfaces— three orthogonal flat vertical walls and the floor. The images on the walls are rear projected, while the image on the floor is projected from the top down. The sense of immersion is achieved by engaging peripheral vision and by stereo projection of the data. In these environments, the data surround the interpreter, actually appearing to fill the room. The figure shows interpretive work performed in the Immersive Visualization Environment at ARCO. The interpreter can walk through the data, along interpreted horizons, and between faults to locations where the wells penetrate the target horizons. Since head tracking is used to properly alter the data projections for changes in head position, the view of the data changes very intuitively as the interpreter moves through the data. Figure 13–12. From Dorn, 1998; courtesy SEG. Visualization Techniques for 3-D Data • 13-25
  • 26. Immersive Visualization, continued Immersive rooms vs. visionariums Both the immersive rooms and the visionariums have their application. The immersive room may be a better environment in which to actually interpret the data and plan the development, with a group of up to five people working together at one time. The visionarium may be a better arrangement for reviewing the prospect, drilling plan, or development plan for a larger management group. Calculating depth conversion velocity The application of these facilities and other immersive visualization devices is just beginning to be explored in the oil industry. In the not-too-distant future, these environments may fundamentally change the manner in which we do our business. One thing is certain: 3-D seismic interpretation 5–10 years from now will be very different from 3-D seismic interpretation today. 13-26 • Interpreting 3-D Seismic Data
  • 27. Section E References Brede, E.C., and S.W. Thomas, 1986, Interactive fault mapping: a case study: The Leading Edge, vol. 5, no. 9, p. 1262–1272. Brown, A.R., 1999, Interpretation of Three-Dimensional Seismic Data, 5th ed.: AAPG Memoir 42, 525 p. Dalley, R.M., E.C.A. Gevers, G.M. Stampfli, D.J. Davies, C.N. Gastaldi, P.A. Ruijtenberg, and G.J.O. Vermeer, 1989, Dip and azimuth displays for 3D seismic interpretation: First Break, vol. 7, no. 3, p. 86–95. Denham, J.I., and H.R. Nelson, Jr., 1986, Map displays from an interactive interpretation: Geophysics, vol. 51, p. 1999–2006. Dorn, G.A., 1998 Modern 3-D seismic interpretation: The Leading Edge, vol. 17, no. 9, p. 1262–1272. _____, M.J. Cole, and K.M. Tubman, 1995, Visualization in 3-D seismic interpretation: The Leading Edge, vol. 14, no. 10, p. 1045–1049. _____, K.M. Tubman, D. Cooke, and R. O’Connor, 1996, Geophysical reservoir characterization of Pickerill Field, North Sea, using 3-D seismic and well data, in P. Weimer and T. Davis, eds., Applications of 3-D Seismic Data to Exploration and Production: AAPG Studies in Geology 42, p. 107–121. Enachescu, M.E., 1993a, Amplitude interpretation of 3-D reflection data: The Leading Edge, vol. 12, no. 6, p. 678–685. _____, 1993b, Three-dimensional seismic imaging of a Jurassic paleodrainage system: 1993 SEG Summer Research Workshop on 3-D Seismology, Abstracts, p. 292–298. Reymond, B.A., and G.M. Stampfli, 1994, Sequence stratigraphic interpretation of 3D seismic data offshore Louisiana—a case study: First Break, vol. 12, no. 9, p. 453–462. Rijks, E.J.H., and J.C.E.M. Jauffred, 1991, Attribute extraction: an important application in any detailed 3-D interpretation study: The Leading Edge, vol. 10, no. 9, p. 11–19. Stark, T.J., 1996, Surface slice generation and interpretation—a review: The Leading Edge, vol. 15, p. 818–819. Acknowledgment The author and AAPG acknowledge and thank the Society of Exploration Geophysicists for permission to publish this chapter. The chapter was first published in The Leading Edge, September 1998, p. 1261–1272. References • 13-27