Chap13

Chapter 13

Interpreting 3-D Seismic Data

by

Geoffrey A. Dorn

Geoffrey A. DornGeoff Dorn received his bachelor’s degree in astrophysics (1973) from the University ofNew Mexico and a Ph.D. in geophysics (engineering geoscience, 1980) from the Universi-ty of California, Berkeley. He joined ARCO’s Exploration and Production Technologygroup in 1980, spending his first two years in seismic acquisition research. From1982–1987 he directed ARCO’s interactive interpretation research group but left manage-ment to pursue technical research interests in horizon and volume attribute analysis and3-D visualization. In 1993 Dorn was named an ARCO Research Advisor for his contribu-tions in 3-D seismic interpretation research and technical service. He returned to man-agement in 1997 to direct ARCO’s 3-D visualization research efforts. A member of theSEG Research Committee since 1990, he has helped organize several postconventionresearch committee workshops and was chairman of the 1993 SEG Summer ResearchWorkshop on 3-D seismology. His interests include 3-D visualization, 3-D seismic inter-pretation, attribute analysis, and geophysical reservoir characterization. He is an activemember of SEG, EAGE, and AAPG.

Overview • 13-3

Modern 3-D seismic interpretation involves interactive workstations and informationtechnologies to interpret large volumes of data accurately and efficiently. The 3-D inter-preter must understand the concepts of geology and seismology as well as the algorithmsimplemented in computer-aided interpretation tools to use these systems effectively. Agood source place for a broad overview of the topic is Interpretation of Three-DimensionalSeismic Data by Alistair Brown. This chapter focuses on applications and techniquesessential to the efficient and effective interpretation of 3-D seismic data.

Introduction

Overview

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

In this chapter

13-4 • Interpreting 3-D Seismic Data

Three-dimensional (3-D) seismic data interpretation may be approached many differentways, depending on one’s purpose. Data quality determines the level of the interpretation.For example, structural interpretation requires the least quality. Stratigraphic interpre-tation requires better quality data. Attribute analysis of 3-D data requires the highestquality data. The following procedure is suggested for interpreting 3-D seismic data.

Step Action

1 Determine the goals for interpreting the 3-D data. Do you want to interpretstraucture 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 If attribute analysis is possible and necessary to the interpretation goals,decide which attributes will best show the required geological features.

Introduction

Section A

Basics of Interpreting 3-D Seismic Data

This section contains the following topics.

Topic Page

Data Preview 13–5

Two- or Three-Dimensional Interpretation 13–7

Picking Horizons in 3-D Data 13–10

Surface Slicing 13–13

Interpreting 3-D Seismic Data 13–15

In this section

Basics of Interpreting 3-D Seismic Data • 13-5

Why preview the 3-D volume? You have just received the final migrated volume from pro-cessing (late), and your manager is hounding you to produce maps at the reservoir orprospect level because he is looking at a deadline for a drilling commitment in the nearfuture. Why not just dive in, do some quick picking at the reservoir level, grid and contourthe picks, do a rough time-to-depth conversion, and move on? In most cases, this approachcauses problems ranging from a loss in efficiency, to minor errors and omissions, to majorinaccuracies 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 relativedifficulty of interpretation in different areas. It is possible to identify the initial set of seis-mic 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 anoverall picture of the major faulting in the survey and by providing a better initial pictureof any depositional stratigraphy imaged in the data. This information allows the inter-preter to very effectively plan the interpretation of the volume and proceed in the quick-est, most efficient manner possible.

Why preview?

Data Preview

Volume visualization provides an effective tool for data preview. We can view animationsof opaque slices through the data volume along any orientation, and we can control theslicing interactively. We can also control the opacity of the volume. By making the datavolume partially transparent, we can see the structure of strong reflections prior to doingany interpretation. It may also be possible to isolate elements of depositional systems bycontrolling the color and opacity mappings.

Volumevisualization

The figure on the following page is an opaque volume from a 3-D seismic survey in thesouthern North Sea Gas Basin. Four horizons are indicated: Top Chalk, Top Keuper, TopZechstein, and Top Rotliegend. By visualizing the volume with the opacity set so that onlythe strongest peaks and troughs are opaque, we can see the overall 3-D structure of thesehorizons prior to interpretation (Figure 1b).

Examining Figure 13–1b, we can note that the anticline at the Top Chalk has a differenttrend than the anticline at the Top Keuper. Some faulting is evident along the north endof the Top Keuper horizon extending roughly parallel to the trend of the anticline. TheTop Zechstein and Top Keuper are approximately conformable in this volume, and thestrong amplitude reflections from the west are dipping. Top Rotliegend fault blocks arealso evident. By using motion (e.g., rotation around the time axis) and stereo displays, thestructures, their relationships, and the positions of specific reflections become much moreobvious than they are in the still images in Figure 13–1.

Data previewexample


Data previewexample(continued)

Data Preview, continued

Figure 13–1. From Dorn, 1998; courtesy SEG.

a)

b)


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 isneither an effective nor an efficient approach to the interpretation. The results are gener-ally of poorer quality and require significantly more effort than interpreting the data in a3-D fashion.

Introduction

Two- or Three-Dimensional Interpretation

Two-dimensional interpretation of a 3-D seismic volume involves limiting your views ofthe data, and your interpretation, to lines and cross-lines. Typically, this approach tointerpretation has its roots in two things. First, the interpreter is comfortable with seis-mic 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 the3-D volume results in loss of information and is the least efficient approach to interpret-ing the data. Modern interactive systems provide a variety of tools that allow interpretersto perform the interpretation in a more 3-D fashion.

The 2-Dapproach

The first rule of 3-D seismic interpretation is that there is never enough data. The problemwe are trying to solve with seismic interpretation is always underdetermined. There isnever enough information to uniquely define the geology of the subsurface in the area ofthe 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.

Never enough data

A second rule for 3-D seismic interpretation is that the survey is always oriented at 45o tothe trend of faults, channels, and other features of interest. There will always be faults orchannels oriented at angles between 0o and 45o relative to the orientation of the verticalsection you are interpreting. If you only look at vertical sections, you will always missimportant features of the geology.

Weaknesses of verticalsections

The figure on the following page shows several images from a 3-D survey in the Gulf ofMexico. Figure 2a is a dip magnitude map at an interpreted horizon in the data, showingseveral 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 thevolume at angles of approximately 90o, 45o, and 10o to the trace of the fault in the centerof the horizon. Figures 2b, c, and d are traverses b, c, and d, respectively. The fault isclearly interpretable in the centers of the traverses that cut the fault at angles of 90o and45o. However, it would be very difficult to interpret the fault on Figure 2d, the oblique tra-verse. This geometric effect produces a blind zone on vertical sections that are orientedbetween +/-20o of the trend of a fault. As a result, if you are only interpreting vertical seis-mic sections, you will fail to see faults that have trends in this zone. The same phenome-non occurs with depositional stratigraphy.

The best section to image a channel is a section oriented at 90o to the trend of the channel.

Example ofseeing faults


Example ofseeing faults(continued)

Two- or Three-Dimensional Interpretation, continued


The first step toward 3-D interpretation of a 3-D volume is to use time slices. The value oftime-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 ofthe time slice

Start with timeslices


Depositional systems are typically more interpretable on time slices than they are on ver-tical sections. The figure below is a time slice from a 3-D survey in the North Sea. A por-tion of a braided stream system is clearly evident.

Computerlimitations

Two- or Three-Dimensional Interpretation, continued


The following figure is a traverse cut through the data in a direction perpendicular to thechannel system. Horizontal arrows indicate when the channel system occurs, and verticalarrows show the location of each of a number of individual channels cut by the traverse. Itis safe to say that most if not all of these channels would have been missed if the interpre-tation had been limited to vertical sections.



Computer-assisted interpretation of seismic data is one of the areas where the tools ofmodern interactive interpretation systems have made the most significant impact. Theinterpreter needs to choose the technique that will allow the best interpretation to beachieved in the most efficient manner possible. In terms of interpretive efficiency, tech-niques would typically be ordered in the following way, from least efficient to most effi-cient: • Manual picking • Surface-slicing• Interpolating • Voxel tracking• Autopicking

Introduction

Picking Horizons in 3-D Data

Manual picking is interpreting horizons on lines, cross-lines, time slices, and traverses byhand. This is the most familiar technique. It is also, by far, the least efficient horizoninterpretation technique in terms of interpreter time and effort. While interpreting manu-ally, the interpreter is looking for some degree of local continuity in the data and localsimilarity of character to identify the event to be picked.

Manual picking

Interpolation is somewhat more efficient than manual interpretation. The use of interpola-tion, however, assumes that a horizon is locally very smooth and perhaps linear (or planarin two dimensions) between control points. If this assumption is violated between controlpoints (e.g., there is a fault between the interpreted lines), then the results will be poor.

Picking byinterpolation

Autopicking (or autotracking) has been around in interactive interpretation systems sincethe early 1980s. The concept behind autopicking is quite simple. The interpreter placesseed picks on lines and/or cross-lines in the 3-D survey. These seed points are then usedas initial control for the autopicking operation. The algorithm looks for a similar featureon a neighboring trace. If it finds such a feature within specified constraints, it picks thattrace and moves on to the next trace. Simple autopickers allow the user to specify a fea-ture to be tracked, an allowable amplitude range, and a dip window in which to search.

The figure to the right is asketch of how such anautopicker works. If any ofthe search criteria are notmet (amplitude out ofrange, no similar featurein the dip window, etc.),the autotracker stopstracking at that trace.More sophisticatedautopickers let the userspecify additional criteriato control the picking.

Autopicking


There are two classes of autopicker: 1) Feature trackers2) Correlation trackers

The feature tracker searches for a similar configuration of samples within the dip windowbut does not perform any correlations between traces. It tries to track a configuration ofsamples on the seismic trace that defines a peak, trough, zero crossing, etc., from trace totrace.

A correlation tracker takes a portion of the seismic trace around the seed pick and corre-lates it with a neighboring trace through a set of lag times constrained by the specifieddip 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 fea-ture tracker. It is also typically more robust in its picking.

Feature and cor-relation trackers

Picking Horizons in 3-D Data, continued

Another aspect of autopickers that must be considered is the path that the autopicker fol-lows through the data. Many are not true volume autopickers because they track throughthe data only in the line or cross-line direction. In other words, the path they followthrough the data is not truly three dimensional or even two dimensional. Some autopick-ers make consecutive passes through the data—one pass in the line direction, the next inthe cross-line direction. A few autopickers actually move through the data in a true 2-Dsense, 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 infill-ing the horizon from the interpreted seed points. The type of control the interpreter picksin the volume prior to autopicking should in part be dependent on the type of algorithmbeing used and the path it follows through the data.

Autopickerpathways

A technique called voxel tracking has become available with the advent of volume render-ing 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 featureis 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 theseed voxels, the voxel tracker searches for connected voxels that satisfy the search criteriaspecified by the user. The search is typically conducted in line, cross-line, and time direc-tions.

Voxel tracking



Picking Horizons in 3-D Data, continued

Because volume-rendering techniques typically place the entire volume being visualizedinto RAM memory on the workstation, and since the tracking algorithm is computational-ly simpler than most autopickers, voxel tracking can be many orders of magnitude fasterthan autopicking—when it works. Most voxel tracking algorithms are more sensitive topoor signal-to-noise ratio than correlation autopickers. However, on reflections with agood signal-to-noise ratio, voxel tracking is usually the most efficient approach to pickingthe horizon.

Voxel trackingpros and cons

The figure below is a sketch of a simple voxel tracking algorithm and its behavior undertwo different continuity constraints. Six-way connectivity restricts the search from onevoxel to only the neighboring voxels that are connected face to face. Twenty-six-way con-nectivity allows the search to proceed between neighboring voxels that are connected faceto face, edge to edge, or corner to corner. The connectivity constraint that is used affectsthe outcome of the voxel tracking.

Voxel trackingassumptions


Like autopicking, voxel tracking assumes that the data are locally continuous, consistent,and connected or smooth. Both techniques assume a consistent phase is being interpretedfrom the data.


Surface slicing is a novel approach to interpreting seismic horizons. This techniqueinvolves visualizing and interpreting areally finite portions of horizons on time slice slabsof the data. The slab thickness is a weak function of the bandwidth of the data and astronger function of the dip of the reflections. For a detailed description of the techniquesee Stark (1996).

The figures below show the general concept of the technique. Figure 13–7 is a perspectiveview 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 struc-ture could be cut into time slabs and dissected. Figure 13–8 shows what these time-sliceslabs might look like, where within each slab only the peaks are displayed. The dissectedportions of the 3-D structure appear as annuli that fit perfectly within each other. Inter-preting the horizon consists of selecting those elements that fit together, somewhat likeassembling a jigsaw puzzle.

Introduction

Surface Slicing

Figure 13–7. From Stark, 1996; courtesy SEG.

(a) (b)

Figure 13–8. From Stark, 1996; courtesy SEG.


Surface Slicing, continued

For an interpreter who is practiced in surface slice picking and in the appropriate settingsof the various parameters that control the algorithm, surface slicing can be an extremelyefficient way to interpret horizons. Since entire areas on the horizon are picked with eachclick of the mouse button, this technique is not very sensitive to the number of traces inthe 3-D survey. It assumes local continuity and connectivity in the data, and it assumes aconsistent phase is being interpreted. It is less sensitive to discontinuities and poor sig-nal-to-noise ratio than voxel tracking or autotracking because it is not entirely an auto-matic technique; the interpreter controls the technique and frequently can achieve betterresults because interpretive judgment can be applied.

Surface slicingassumptions


Clearly, any interpretation involves some combination of the techniques listed above. Thestrongest events might be voxel tracked. Many of the remaining events might be surfacesliced. In some areas an autopicker might be necessary. All three of these techniques typi-cally leave some holes or unpicked traces in the horizon that might be infilled by interpo-lation. And all of these techniques require some interpretation by hand on seismic sec-tions to provide initial control to the picking process. The techniques should be viewed asa set of tools that the good interpreter knows how to apply to achieve the best interpreta-tion in the shortest period of time.

Choosing theappropriatetechnique

Interpreting 3-D Seismic Data

While these techniques are all extremely valuable, we must understand that computer-assisted horizon picking is only as good as the algorithm, combined with its application bythe interpreter. All of these techniques produce some cycle skips in the data (e.g., in areasof poor signal-to-noise ratio or in areas where the event being interpreted splits into adoublet). The interpreter must realize this and review the picks in the volume, on sec-tions, and on attribute maps of the horizon that might accentuate cycle skips in the inter-pretation (e.g., dip magnitude maps). Cycle skips are also very obvious when 3-D visual-ization is used to view the horizon.

Reviewingpicks

Computer-aided interpretation of fault surfaces is significantly less advanced than hori-zon interpretation. With horizons, a feature of the data—a reflection event—can betracked in a controlled fashion through the volume. Fault surfaces, however, typically donot give rise to reflection events in the data. Faults are characterized by discontinuities inthe horizons and by no-data zones, which are (at least conceptually) more difficult totrack.

Picking faults

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 vol-ume. Some require initial interpretive input—seed picks on fault surfaces that are to betracked. Others require a preprocessing step that creates a volume to highlight disconti-nuities; then the discontinuities are tracked to form fault surfaces. Although these tech-niques are relatively new, they are developing rapidly and promise to significantlyimprove the efficiency of one of the most tedious aspects of 3-D structural interpretation.

Faultautopicking

Seismic sections are vertical cuts through a seismic volume. Nonvertical cuts through aseismic data volume are also called slices. As illustrated in Figure 13–5, horizonal slicescan 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 todepth. Fault slices require data with mappable fault surfaces.

Nonverticalslices


Besides the interpretation of vertical slices for stratigraphic features, 3-D seismic dataalso may be interpreted in nonvertical slices. These views include time, horizon, and pro-portional slices.

Introduction

Section B

Stratigraphic Interpretation Techniques of 3-D Data


Topic Page

Time and Horizon Slices 13–17

Proportional Slices and 3-D Volume Visualization 13–19

In this section

Stratigraphic Interpretation Techniques of 3-D Data • 13-17

The time slice was described earlier as the first step toward 3-D interpretation of a 3-Dseismic volume. A time-slice view of the data is an improvement over vertical sections forthe interpretation of depositional systems because it provides the opportunity to see aportion of depositional systems in map view. This view is key to interpreting these sys-tems because it allows a view of the morphology of the system, which facilitates its recog-nition.

Introduction

Time and Horizon Slices

A time slice provides at best an image of a small portion of a depositional system. Subse-quent structural deformation of the depositional surface typically means that only a smallportion of a depositional system is imaged on a time slice. In fact, as the structural reliefincreases, the anomalies on the time slice associated with the structure quickly dominatethe image.

Structuraleffects

One way to improve the imaging of the paleodepositional system is to create horizon slicesthrough the 3-D volume. The interpreted reflection (horizon) is an approximation of apaleodepositional surface. Within the time interval where reflections are approximatelyconformable to the interpreted horizon in three dimensions, the shape of the horizon sur-face is a reasonable description of the shape of the paleodepositional surfaces.

Horizon slices

Figures 13–9, 13–10, and 13–11 illustrate the value of the horizontal slice view of thedata. The figure below is a portion of a vertical seismic section from a 3-D seismic surveyin the North Sea. The interpreted horizon, at approximately 2 seconds, is the Top Pale-ocene. Approximately 120 ms below this, at about the level indicated by the arrows, thesection crosses a 1-km-wide Paleocene deepwater turbidite channel.

Examplevertical slice



The figure to theright is a time slicethat intersects a por-tion of the channel.On both the verticalsection (Figure 13–9)and the time slice,the channel is diffi-cult to interpret,even though the fea-ture is quite large.Most of the ampli-tude patterns on thetime slice are associ-ated with structure,not stratigraphy.

Example timeslice

Time and Horizon Slices, continued

This figure is a hori-zon slice 120 msbelow the Top Pale-ocene horizon. Theshape of the horizonslice is defined by theshape of the TopPaleocene horizon.This surface is shift-ed 120 ms down, andthe seismic ampli-tudes are extractedwhere the shiftedsurface intersects the3-D volume of data.The 1-km-wide chan-nel is unmistakableon this view of thedata, and both edgesof the channel arereadily interpretable.

Examplehorizon slice

Figure 13–10. From Dorn, 1998, courtesy SEG.

Figure 13–11. From Dorn, 1998, courtesy SEG.

Stratigraphic Interpretation Techniques of 3-D Data • 13-19

Additional refinement to the horizon-slice approach can be made to accommodate situa-tions where there is growth, or differential deposition, in an interval between two inter-preted horizons. For such an interval, the reflections between the two interval-boundinghorizons are not parallel to either bounding horizon. To some extent, the shape of thereflections in this interval is intermediate between the shapes of the interval boundinghorizons. A set of slices between the two bounding horizons, which more closely mimicsthe shape of the reflections in that interval, is given by

S = H1 + R * (H2 – H1)

where:

S = the intermediate surfaceH1 = the shallower horizonH2 = the deeper horizon R = a fraction that varies between 0 and 1

Introduction

Proportional Slices and 3-D Volume Visualization

The new surface, S, is the shallower horizon time structure plus a fraction of the isochronbetween the two bounding horizons. Such a slice is called a proportional slice becauseit is proportionally between the two bounding horizons. In certain environments thatexhibit significant growth in the interval, the proportional slice can provide a significantlyimproved image of the depositional stratigraphy compared to horizon slices in the shapeof either bounding horizon.

Definition

Horizon slices and proportional slices have been available for a number of years in inter-active interpretation systems. Some of the implementations are awkward; creating theproportional slice, in particular, may be a multistep process. Ideally, creating these slicesshould be a fully interactive process, allowing the interpreter to explore intervals of inter-est in the 3-D seismic volume for indications of paleodepositional systems.

Creatingproportionalslices

Advances in pattern recognition and volume seeding have provided tools that allow theinterpreter to seed and pick the depositional system as a 3-D body. This allows the inter-preter to see the depositional system as a 3-D object within the seismic volume. Visualiz-ing this object in three dimensions provides visual integration of the stratigraphy of thedepositional system with the overprint of current structural relief.

3-D volumevisualization ofdepositionalsystems


Attributes are measurements based on seismic data such as polarity, phase, frequency, orvelocity. Horizon attributes were first used in the mid-1980s for interpreting fault traceson reservoir horizons. Since then there has been an explosion in the number of attributesthat can be generated and displayed in 3-D interpretation systems. New attributes areadded to the list regularly. Because software providers frequently use different names forthe same attribute, and since some of the names are rather obscure, it can be very diffi-cult be sure what each attribute represents.

Introduction

Section C

Attributes


Topic Page

Basics of Attributes 13–21

Using Attributes for Geological Interpretation 13–22

In this section

Attributes • 13-21

At this time, no general reference includes a complete list of the attributes that can becreated. One approach (Brown, 1999) to organizing the attributes is based on whetherthey 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)

Groupingattributes

Basics of Attributes

The meaning and use of some attributes can be quite straightforward. The reflectionamplitude at a horizon can, under the correct circumstances, be related to porosity or netpay in a reservoir interval. With other attributes the physical interpretation of a variationin attribute value is somewhat more obscure. For example, arc length (also call reflectionheterogeneity) 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). Thismeasure is affected by amplitude, frequency, and the length of the interval. Associating avariation in arc length with a physical change in the geology is possible, but it can bemore challenging.

Attributemeanings

Attributes are used for both qualitative and quantitative interpretation. An example ofqualitative interpretation would be to use maps of dip magnitude, dip azimuth, or resid-ual structure to interpret detailed fault trace patterns on a horizon. An example of quanti-tative interpretation would be an attempt to correlate attributes with reservoir propertiesmeasured in the borehole. A large number of papers illustrating both applications of hori-zon 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 etal. (1996). Stratigraphic interpretation from attribute maps is discussed in Enachescu(1993a,b), Reymond and Stampfli (1994), and Dorn (1998). Examples of reservoir charac-terization from attributes include Dorn et al. (1996) and Brown (1999).

Attributeinterpretation


Here are a few rules of thumb that the interpreter should apply when approaching theuse 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

Rules of thumb

Using Attributes for Geological Interpretation

Consider the geological feature you are hoping to interpret and how varying that aspect ofthe geology might affect the seismic data. This can help guide the initial selection ofattributes and will certainly help with interpreting the resulting data.

Consider thegeology

Don’t forget about using other attributes. It is not uncommon for there to be surprises inthe data—unforeseen relationships that make physical sense once they have been discov-ered.

Using differentattributes

Be wary of using volume or interval attributes that are not normalized by the isochron ofthe interval over which they are calculated. This normalization can take the form of divid-ing the attribute by either the actual isochron or by the number of time samples in theinterval. 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 thatinvolves a summation over the interval. Without the normalization, the lateral attributevariation may simply be showing the variation in the isochron.

Normalizedattributes

Avoid using an interval attribute that involves the summation of a data measure thatvaries in a cyclic fashion over an interval. For example, in several systems it is possible tocalculate the sum of trace amplitude over a user-specified interval. Since the trace ampli-tude has both positive and negative values over most intervals (unless the interval is sosmall as to include only a half-cycle of the data), the positive and negative values tend tocancel each other out. A second example would be the sum of instantaneous phase over aninterval in the volume

Cyclic variationof an attribute

To apply these rules to attribute interpretation, it is essential for the interpreter to under-stand what the attribute is measuring in the data—the equation for the attribute. Theonly way the interpreter can know this is for the software vendor to provide well-writtendocumentation, either on paper or online, that explains the mathematical calculationinvolved in generating the attribute. This combination—an interest on the part of theinterpreter to understand the attributes and a willingness on the part of the vendor toprovide that information—is essential to the intelligent and effective use of attributes in3-D interpretation.

Know theattribute

Visualization Techniques for 3-D Data • 13-23

Visualization is the graphical presentation of data in an intuitive fashion to develop anunderstanding of data and reveal new insight. The key word in this definition is "intu-itive." Three-dimensional visualization applied to 3-D seismic data is an attempt to pre-sent this data, and its interpretations, in an intuitive fashion—the same fashion in whichwe perceive the world around us every day.

Introduction

Section D

Visualization Techniques for 3-D Data


Topic Page

Visualization Fundamentals 13–24

Immersive Visualization 13–25

In this section


More than half the neurons in the human brain are associated with vision. This signifi-cant resource can be applied to the interpretation of 3-D seismic data if the data are pre-sented 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

Perceiving 3-D

Visualization Fundamentals

Three-dimensional visualization improves the efficiency, accuracy, and completeness ofthe interpretation, integrates large amounts of data in easily understood displays, andsignificantly improves the communication between different specialists on an asset teamand between the asset team and management. If 3-D visualization is not used in theinterpretive process, the interpreter will lose productivity and the interpretation will beincomplete or inaccurate.

Improvementsusing 3-D visualization

Volume rendering of seismic data, and its use in data preview, is one example of an areawhere visualization has a major impact on the efficiency of interpretation. In surface-sliceinterpretation, if a 3-D visualization of the horizon being interpreted is used along withthe 2-D surface-slice map displays, the interpreter can be significantly more productiveand avoid errors in the interpretation. For a number of years, 3-D visualization of attrib-utes on a lighted 3-D horizon surface has provided a means of interpreting detailed faultpatterns in a reservoir interval—faults that would have been missed if 3-D visualizationhad not been used (Dorn and Tubman, 1996).

Volumerendering

Three-dimensional visualization has been used as a part of 3-D interpretation for the last10 years. As each additional visual cue has been added to the 3-D displays of seismic dataand seismic interpretations, there have been significant improvements in the 3-D inter-pretation process.

For a discussion of the applications of visualization to horizon attribute analysis, seechapter 8 in Brown (1999). For a more general discussion of the role of 3-D visualizationin 3-D seismic interpretation, see Dorn et al. (1995).

3-D visualizationin seismic data

Visualization Techniques for 3-D Data • 13-25

In 3-D desktop visualization, perspective, lighting/shading, transparency, stereopsis, andin some cases head-tracking (changing the data appropriately for changes in the inter-preter’s head location with respect to the screen) has been applied to both surface and vol-ume displays. With each additional visual cue used in presenting the data in 3-D, therehave been improvements in efficiency. New insights have been gained about the data, andcommunication has improved. The next step in visualization is to engage peripheralvision—to become immersed in the data.

Introduction

Immersive Visualization

In 1997 ARCO, Texaco, and Norsk Hydro each installed large immersive visualizationenvironments. The Texaco facilities are visionariums, large screens (8–10 ft tall) thatcurve horizontally through approximately 160o and can curve vertically. Data are project-ed on the screens using three projectors, each covering one-third of the screen. The senseof immersion is achieved primarily by engaging peripheral vision, filling most of the fieldof view with data.

Facilities forimmersivevisualization

ARCO and Norsk Hydro have installed immersive visualization rooms, based on theCAVETM 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 project-ed, while the image on the floor is projected from the top down. The sense of immersion isachieved by engaging peripheral vision and by stereo projection of the data. In these envi-ronments, the data surround the interpreter, actually appearing to fill the room.

Engagingperipheralvision

The figure shows inter-pretive work performedin the Immersive Visu-alization Environmentat ARCO. The inter-preter can walk throughthe data, along inter-preted horizons, andbetween faults to loca-tions where the wellspenetrate the targethorizons. Since headtracking is used to prop-erly alter the data pro-jections for changes inhead position, the viewof the data changes veryintuitively as the inter-preter moves throughthe data.



Both the immersive rooms and the visionariums have their application. The immersiveroom may be a better environment in which to actually interpret the data and plan thedevelopment, with a group of up to five people working together at one time. The visionar-ium may be a better arrangement for reviewing the prospect, drilling plan, or develop-ment plan for a larger management group.

Immersiverooms vs.visionariums

Immersive Visualization, continued

The application of these facilities and other immersive visualization devices is just begin-ning to be explored in the oil industry. In the not-too-distant future, these environmentsmay 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 seismicinterpretation today.

Calculatingdepthconversionvelocity

References • 13-27

Brede, E.C., and S.W. Thomas, 1986, Interactive fault mapping: a case study: The Lead-ing Edge, vol. 5, no. 9, p. 1262–1272.

Brown, A.R., 1999, Interpretation of Three-Dimensional Seismic Data, 5th ed.: AAPGMemoir 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: FirstBreak, vol. 7, no. 3, p. 86–95.

Denham, J.I., and H.R. Nelson, Jr., 1986, Map displays from an interactive interpreta-tion: 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 characteri-zation 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 Stud-ies in Geology 42, p. 107–121.

Enachescu, M.E., 1993a, Amplitude interpretation of 3-D reflection data: The LeadingEdge, 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 3Dseismic 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 applicationin any detailed 3-D interpretation study: The Leading Edge, vol. 10, no. 9, p. 11–19.

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Section E

References

The author and AAPG acknowledge and thank the Society of Exploration Geophysicistsfor permission to publish this chapter. The chapter was first published in The LeadingEdge, September 1998, p. 1261–1272.

Acknowledgment

Chap13

Education

Transcript of Chap13