Seismic attributes - book
Raw seismic data represent the input to the seismic processing system (which features filtering, deconvolution, migration, and other processes). The output is the final seismic image. It is assumed that the processing has been done correctly so that the final seismic image represents the true subsurface rock structure and no longer represents wave motion, as do the raw seismic records.
The interpretive processing system is made up of image-enhancement software similar in nature to software used to enhance digital photographs. The final seismic image is the input to the interpretive processing system. The output is the interpreted (or enhanced) image. If done correctly, the enhancement will better portray the subsurface geology.
The intricacies of seismic wave motion and the conversion of that wave motion into an image via seismic processing generally are peripheral to interpretive processing except in cases such as the following: Seismic reflections come from horizons, so the final seismic images (e.g., the migrated records) show those horizons. However, seismic reflections generally do not come from faults, so the final seismic images generally do not show faults as such. In interpretive processing, the position of a fault often can be identified, either directly from diffractions or indirectly from breaks (or discontinuities) in the horizons on the seismic image. One purpose of interpretive processing is to bring out faults and other features directly.
In a general sense, a seismic attribute is a specific measurement that has been based on seismic data. This definition includes any quantity that can be computed from a single seismic trace or a set of seismic traces. Wadsworth et al. (1953) computed the first crosscorrelation between two seismic traces. From that crosscorrelation, various attributes were used — such as the maximum value, minimum value, and dip value. Such attributes based on crosscorrelation of traces give measures of similarity or dissimilarity between the two traces that were crosscorrelated.
In the early 1950s, the emphasis in seismic exploration was on detecting reflections that could not be seen visually because of high noise levels. The different offsets of detectors from the source caused a reflection to hit one of a pair of traces before the other. As a result, at a reflected event, a discontinuity resulted between the two traces. Thus, points of dissimilarity between two traces spotlighted reflections. By this reasoning, a dissimilarity attribute was required for detecting reflections. Such an attribute would be based on the crosscorrelations between two traces. Those crosscorrelations were computed as the first step.
The next step was to use those crosscorrelations to predict one trace from the other. At places where two traces are similar to each other, the prediction error is small. At places where two traces are dissimilar, the prediction error is large. The dissimilarity attribute chosen for this task originally was the mean-square prediction error, which is a positive quantity. Thus, low points on the dissimilarity attribute curve represent points of weak discontinuity between two traces, and high points on the dissimilarity attribute curve represent points of strong discontinuity between two traces. This was an early use of seismic attributes. Ideally, this attribute would stick out like the proverbial sore thumb at the location of reflections.
What is an attribute array? A seismic attribute is the product of a mathematical or statistical operation that is applied to an array of data. A new array results, which is called the attribute array. Use of different attributes yields displays that emphasize different features, as the case may warrant. For example, one attribute might reveal subsurface anomalies more clearly. Another might serve as a direct hydrocarbon indicator. Today, attribute analysis forms a mainstay of interpretative processing.
The input to an interpretive processing system is the final image provided by seismic processing. It is hoped that processing has been done correctly and the image represents the subsurface rock structure and stratigraphy rather than merely the seismic wavefield obtained from the raw seismic records. The intricacies of seismic wave motion and its conversion into an image by seismic processing now are relegated to the background. Instead, the interpreter seeks the best possible choice of a set of attributes that will enhance various features of the geology of interest.
How are attributes used in seismic stratigraphy? Seismic stratigraphy involves the study of seismic data, using them to describe geologic depositional environments through an orderly approach to interpreting seismic reflections. Fundamental to this approach is an understanding of the effects of lithology and bed spacing on reflection parameters.
Attributes such as amplitude, frequency, and coherency are valuable tools in the interpretation of depositional environments. The attribute of reflection amplitude contains information concerning velocity and density contrasts at individual interfaces as well as information on the extent of interbedding. The attribute of frequency is related to such geologic factors as the spacing of reflectors or lateral changes in interval velocity. An attribute that represents coherency of reflections is associated closely with continuity of bedding. For example, continuous reflections indicate widespread, layered deposits.
The interactive computer systems in use today allow the handling of many displays of seismic data at one time. Thus, an interpreter can look at the same data volume in various forms. Each such form is considered to be a seismic attribute. These attributes are useful in estimating the petrophysical properties associated with the seismic signal. Anything connected to petrophysics is of immense value in geophysical and geologic interpretation. Attributes can reveal features that might be missed on conventional seismic sections. For example, an attribute plot might show lateral changes along the bedding, such as changes associated with stratigraphy or with hydrocarbon accumulations.
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Also in this chapter
- Interpretive processing
- Instantaneous attributes
- Seismic sequence attribute map (SSAM)
- Coherence cube (C3)
- SSAM and C3
- Appendix L: Design of Hilbert transforms
- Appendix M: Exercises
- Wadsworth, G. P., E. A. Robinson, J. G. Bryan, and P. M. Hurley, 1953, Detection of reflections on seismic records by linear operators: Geophysics, 18, 539-586.
- Whaley, J., 2017, Oil in the Heart of South America, https://www.geoexpro.com/articles/2017/10/oil-in-the-heart-of-south-america], accessed November 15, 2021.
- Wiens, F., 1995, Phanerozoic Tectonics and Sedimentation of The Chaco Basin, Paraguay. Its Hydrocarbon Potential: Geoconsultores, 2-27, accessed November 15, 2021; https://www.researchgate.net/publication/281348744_Phanerozoic_tectonics_and_sedimentation_in_the_Chaco_Basin_of_Paraguay_with_comments_on_hydrocarbon_potential
- Alfredo, Carlos, and Clebsch Kuhn. “The Geological Evolution of the Paraguayan Chaco.” TTU DSpace Home. Texas Tech University, August 1, 1991. https://ttu-ir.tdl.org/handle/2346/9214?show=full.