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1 Geovolume Visualization and Interpretation: What makes a useful visualization seismic attribute? Tatum M. Sheffield* and Barton A. Payne, Chevron Energy Technology Company Summary A useful visualization seismic attribute places a wavelet characteristic that can be related to a geological event in the histogram outliers, which is then isolated using visualization techniques. There are over 100 seismic attributes available in the basic industry volume interpretation packages available to 3D seismic volume interpreters today and many have similar names but different formulas or different names with similar formulas. Attributes can often appear redundant when examined only with crossplots and correlation calculations, but become very distinct when interpreted using volume visualization techniques within the context of an understanding of the histogram. The purpose of this paper is to explain and demonstrate the components of a useful visualization seismic attribute. Choosing the correct scale is critical. For conventional seismic data, eight to ten times the RMS amplitude gives a conservative scaling with essentially no clipping. (Figure 1) If the events of interest are relatively weak, five to six times RMS may also be used. For attributes of unknown range, a histogram clip method is preferable, clipping between onehalf to one tenth a percent of the population. Introduction The key to determining the usefulness of any combination of attributes for volume interpretation is a workflow that incorporates three key steps: data preparation, histogram relation to geological events, and the methods used to combine and display the attribute volumes. Data preparation and processing quality control before attribute calculations is a critical first step. Common problems such as decimated data and processing artifacts are often overlooked. Attribute calculations combined with visualization display techniques often highlight these problems. Simple processing techniques such as frequency filters, standard and edge preserving smoothing will help sharpen key geologic events including onlaps, faults and channel edges. The relationship to geologic events is best understood by examining the data histogram, so classifying and understanding the histogram is essential. Relating this characteristic to geologic events is the point where most seismic attributes fail and interpreter experience is needed. The final step is combining and displaying the seismic attributes for a specific interpretation objective. Figure 1: 8x RMS unclipped vs. 4x RMS clipped Amplitude clipping introduces artifacts in some attributes, notably instantaneous frequency. As Figure 2 illustrates, the clipped data on the right of the central ball marker shows an artificial high frequency event created by the edges of the clipped amplitude, the left side shows the ideal (unclipped) response. 32 Bit Inst. Freq. 4x RMS Inst. Freq. Data Preparation Step 1 In order to prepare seismic data for volume interpretation attribute calculations the interpreter should ask: What am I trying to do? Stratigraphic interpretations require a closer look at clipped data, acquisition footprints and processing artifacts while structure volumes allow for more extensive use of data smoothing, frequency filters and edge preserving filters. Figure 2: 32bit vs. 4x RMS I. Frequency Some attributes also help in the detection of acquisition footprints and processing artifacts. Semblance and 849 Downloaded 09 Apr 2009 to Redistribution subject to SEG license or copyright; see Terms of Use at 849

2 Main Menu Coherency type volumes when viewed with light texture display methods show line to line acquisition footprints. These are best viewed in a z-slice for more horizontal detail. Relative Acoustic Impedance volumes work well with light texture display methods to show migration, multiple or focusing problems. These are best viewed inline or x-line for more vertical detail. Figure 3 shows a migrated seismic line with normal color display parameters. Figure 4 shows that same line with a Relative Acoustic Impedance volume overlain with peak values co-rendered with a light texture display also known as bump mapping. around each sample and assigns the average value of the selected fragment to that sample. Mean and median filters only use data parallel to dip and do not preserve amplitudes as well. Figure 5 shows a Semblance volume with no EPS applied while Figure 6 shows the same Semblance calculation done after a two passes of a 3 x 3 EPS filter is applied. Figure 5 Semblance without EPS Figure 3 Amplitude only data. Figure 6 Semblance with EPS Histogram Analysis Step 2 Figure 4 Amplitude color display with RAI light texture. Additional processing techniques include dip filters to remove noise, high cut frequency filters to improve continuity, low cut frequency filters to remove long wavelength trends generated by some attributes, standard smoothing to average data in a standard grid (3x3, 5x5 etc.). Edge Preserving Smoothing (EPS) uses a principal component (PC) KL filter on an analysis window. In principle EPS looks for the most homogeneous fragment In statistics, a histogram is a graphical display of tabulated frequencies. However, in volume interpretation the display of the histogram is not the main method to display the data, but only a path to positioning a wavelet characteristic composed of individual and combinations of amplitude, phase and frequency in the histogram outliers for display and interpretation in a 3D viewer. There are eight basic types of histograms: Normal, Skewed, Wrapped, Multiple Peaked, Spiked, Combination and Blended. Figure 7 displays the three most useful types of histograms, Normal, 850 Downloaded 09 Apr 2009 to Redistribution subject to SEG license or copyright; see Terms of Use at 850

3 Skewed and Blended. The Normal histogram is centered on zero with positive and negative extremes on either side. Used primarily with attributes composed of more than one combination of wavelet characteristics such as amplitude with phase or calculated attribute that has positive and negative changes. Figure 8 displays the histogram for the Envelope Time Derivative which is the rate of change of the envelope over time. Yellow represents a sharp negative change, blue a sharp positive change. This can indicate sequence boundaries or fluid changes as shown in Figure 9. Skewed histograms are composed of positive numbers designed to isolate significant processes in either tail. This is used with attributes that display either a single wavelet characteristic (i.e. frequency, amplitude) or a single calculated value such as semblance. Generally the smaller tail highlights the characteristic of interest. Figure 6 displays the Semblance attribute where extreme changes between wavelets indicate channel edges or faults. The Blended histograms are composed of any value range and combine different data sets to shared sections of the histogram. Figure 10 shows the blending of the Amplitude and Frequency volumes histogram. Blended histograms require specific color maps that use both color and intensity. Figure 11 shows the Instantaneous Frequency in colors with amplitude based on a gradational intensity. display; the Envelope Modulated Phase display introduces phase information into the Instantaneous Amplitude display. Amplitude Weighted Apparent Polarity is calculated as the polarity multiplied by the envelope. This allows the interpreter to examine the Envelope information and also determine if it is single or multiple cycle energy. Determinations can also be made if the energy has a trough over peak or peak over trough relationship. Both Envelope Weighted Apparent Polarity (EWAP) and Envelope Modulated Phase (EMP) display the same data, but their histograms are different. EWAP maintains a negative to positive value range while EMP goes from zero to positive numbers similar to Instantaneous Amplitude. Different data organization in the histograms allows the interpreter to choose different methods to display and isolate the key wavelet character. Display and isolation methods include, phasing, surface bounding, horizon and volume slicing, volume rendering, co-rendering (Fig. 13), blending (Fig. 11), light texture (Fig. 4), Voxel GeoAnomaly (Fig. 14) etc. Conclusions This thought process turns into a workflow and results in an interpretation. This is a three-step process. Step 1 begins with data preparation and asking the questions: What wavelet character am I trying to recognize and is my data capable of indicating it? Step 2 involves determining what attribute best isolates the key wavelet character and isolates on a specific section of the histogram outliers. Step 3 is the final step that involves choosing the best volume visualization technique to display and isolate the histogram outliers for mapping purposes. If the seismic attribute fails any one of these three steps then it is not useful for volume interpretation. Figure 7 Basic Histogram Distributions Volume Interpretation Display Techniques Step 3 Combining and displaying the volume interpretation attributes in a method that allows critical information to be isolated and mapped is the final and most important step. Building the correct colorbar is the most important part of that display. Figure 12 shows the histograms from three different attributes: Amplitude Weighted Apparent Polarity introduces the phase information into the Apparent Polarity Figure 8 Envelope Time Derivative Histogram 851 Downloaded 09 Apr 2009 to Redistribution subject to SEG license or copyright; see Terms of Use at 851

4 Figure 9 Envelope Time Derivative Color Display Figure 12 Histogram Comparison Figure 10 Amplitude and Frequency Color Map Figure 13 Co-Rendering Amplitude and Semblance Figure 11 Amplitude and Frequency Blended Volume Figure 14 GeoAnomaly Display 852 Downloaded 09 Apr 2009 to Redistribution subject to SEG license or copyright; see Terms of Use at 852

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2008 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Chopra, S., and K. J. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization: SEG. 853 Downloaded 09 Apr 2009 to Redistribution subject to SEG license or copyright; see Terms of Use at 853