- Copyright © 2002 Society of Exploration Geophysicists
In this study, we present an application of textural analysis to 3D seismic volumes. Specifically, we combine image textural analysis with a neural network classification to quantitatively map seismic facies in three-dimensional data. Key advantages of this approach are:
it produces a detailed 3D facies classification volume (whereas manual seismic facies classifications are typically 2D maps),
it enables rapid and quantitative anlaysis of the increasingly large seismic volumes available to the interpreter, and
it eliminates many time-consuming tasks, thereby freeing the interpreter to focus on determining seismic facies and integrating them into a geologic framework.
Finally, we extend our textural analysis-based seismic facies classification technique to interpretation of AVO attribute volumes, such as “A + B” (AVO intercept + gradient), to reduce the inherent nonuniqueness of seismic facies to geologic and lithologic facies, and simplify the facies analysis of complex, mixed-impedance reservoirs.
Seismic facies analysis
Seismic facies analysis is a powerful qualitative technique used in stratigraphic analysis from seismic data and in hydrocarbon exploration. Seismic facies are groups of seismic reflections whose parameters (such as amplitude, continuity, reflection geometry, and frequency) differ from those of adjacent groups. Seismic facies analysis involves two key steps—(1) seismic facies classification (i.e., seismic facies are defined, and lateral/vertical extents delineated) and (2) interpretation (i.e., analysis of vertical/lateral associations, map patterns, and calibration to wells) to produce a geologic and depositional interpretation. This interpretation step is required because there is a nonunique relationship between seismic data, seismic facies, and depositional environment or rock property relationships (Figure 1).