AVO Crossplot Research Articles

Unbiased AVO crossplotting?

AVO crossplotting of intercept and gradient attributes has been a mainstay of AVO interpretation and analysis for more than two decades. In binary sand-shale settings, the viability of a lead or prospect can be further evaluated by identifying a background trend of wet sands and shales, and observing how removed the prospect's intercept-gradient pairs are from the background trend using small temporal or stratigraphic-consistent windows. This technique has primarily been performed visually, and, as such, contains inherent interpretation bias. While overall the visual methodology of the technique has been successful, methods to reduce such errors are valued as prospect economics become more challenging. To mitigate this interpretation bias, an unsupervised neural network methodology is presented. The unsupervised vector quantizer (neural network) assists in subgrouping (classifying) those seismic pairs that comprise the AVO background trend and those pairs that may subgroup as outliers — possibly indicative of hydrocarbon-saturated reservoirs. With this methodology, a clearer determination of what is AVO background trend and what is AVO outlier is presented using 2D seismic data in a frontier exploration province. Use of unsupervised neural networks for AVO crossplot assessment is accompanied with additional benefits that further assist in understanding geologies or seismic facies within the AVO crossplot subset.

Seismic azimuthal anisotropy analysis for estimating reservoir properties at Stybarrow Field, NW Shelf, Australia

Reservoir rocks are often subjected to anomalous vertical and/or horizontal stress conditions and may also contain complex physical attributes such as fracture sets. These rock properties are not easy to detect and map directly away from the borehole, but are sometimes indirectly evident in seismic data as azimuthal anisotropy. Thus, an analysis of anisotropic rock physics seismic attributes can be important for estimation of stress orientations and magnitudes, useful for reservoir evaluation, reducing drilling risks, and enhancing reservoir injection and production methods. The Stybarrow field, located offshore NW Australia, is an example where seismic data exhibit strong azimuthal anisotropy, likely due to the effects of large horizontal tectonic stresses applied to the Carnarvon sedimentary basin. We find evidence for azimuthal anisotropy in AVO reflection amplitude difference maps and cross plots from two repeated 3D seismic surveys acquired at different azimuths, as well as in dipole shear logs and borehole breakout data. We model azimuthal AVO responses using Ruger?s horizontal transverse isotropy (HTI) AVO equation, using the anisotropy parameters derived from dipole shear logs, and compare the results with AVO gathers from the two 3D seismic surveys with different acquisition azimuths. We use a least squares method to find the coordinate rotation and scaling factor that optimally matches the real seismic data to the modelled data predicted by Ruger?s theory.

3D AVO crossplotting—An effective visualization technique

Crossplots, visual representations of the relationship between two variables, are used to (a) visually identify outliers that may bias a correlation, (b) gain a visual sense for the strength of the correlation between variables, (c) detect if the relationship between variables is linear or nonlinear, (d) identify trends which may indicate multiple populations within the same data set, and (e) detect significant departures from a background trend—in other words to detect anomalies. Crossplotting is widely used in AVO analysis, because it facilitates the simultaneous and meaningful evaluation of two attributes. Generally, common lithology units and fluid types cluster together in AVO crossplot space, allowing identification of background lithology trends and anomalous off-trend aggregations that could be associated with hydrocarbons. Initially, AVO crossplotting typically used the intercept and gradient. However, in 1997, Goodway et al. used crossplots of elastic parameters (Lambda-Rho and Mu-Rho) to improve petrophysical discrimination of rock properties. Other attributes have also been used as AVO anomaly indicators (Castagna and Smith, 1994). Crossplotting appropriate pairs of attributes so that common lithologies and fluid types generally cluster together allows for straightforward interpretation. The off-trend aggregations can then be more elaborately evaluated as potential hydrocarbon indicators. This is the essence of successful AVO crossplot analysis and interpretation, all of which is based on the premise that data that are anomalous statistically are interesting geologically. This article describes attempts to extend crossplotting to three dimensions and assess any advantages that result. Ross and Sparlin (2000) made this extension by using the intercept, gradient, and inlines for a 3D seismic volume as the three dimensions on a 3D crossplot. Our procedure begins by first visualizing different combinations of the measured well-log parameters (P-velocity, S-velocity, density, porosity, and gamma ray) in two and three dimensions. Next, observed patterns are visualized and compared in the …

The AVO hodogram: Using polarization to identify anomalies

Traditional methods for designing AVO indicators are based on the deviation of gas sands from the wet-sand/shale trend in an AVO crossplot such as intercept and gradient (A-B), near and far angle (N-F), P and S reflectivity (RP−RS), etc. These crossplots are typically constructed directly from log data such that each point represents an interface. A drawback of this approach is that it neglects the seismic wavelet. When a wavelet is convolved with the reflection coefficients, each point on the AVO crossplot becomes a series of points, which typically spread across all four quadrants of the crossplot.

AVO Analysis of Long Offset Seismic Data Using 3-D Graphical Analysis

P-wave AVO intercept (A) and gradient (B) crossplotting is widely used for detecting anomalous elastic properties that may be indicative of hydrocarbons, but long offset seismic data are starting to become widely available. It is well known that the two-term AVO equation is only an approximation for angles of P-wave incidence less than 30°. However, long-offset seismic data with P-wave angle of incidence much larger than 30o are sometimes used to extract A and B values using a 2-term AVO equation. And then the extracted A and B values are used for AVO crossplotting and hydrocarbon detection. We show how inaccurate A and B values may result if the two-term AVO equation is used for prestack seismic data with large incident angles. A linear Vp–Vs and a Gardner-like Vp–ρ (rho) relationship explain the linear relationship between A and B for typical brine-saturated sandstones and shales, and this line passes through the origin. However, in the Gulf of Mexico (Hilterman, 1990) and Gulf of Thailand (Zhu, 2000), the sand velocity is sometimes larger than the shale velocity, while sand density is smaller than the shale density. As a result, the A–B trend of some brine-saturated sandstones and shales may not pass through the origin or may not be linear in the A–B plane. Nevertheless, a linear Vp–Vs relationship holds and the average Vp across the shale and sand interface does not vary significantly. AVO intercept (A), gradient (B) and curvature (C), which can be extracted from long offset prestack seismic data, are expressible in an equation representing a plane the 3-D A–B–C space. Deviations from this plane suggest abnormal velocities and indicate lithology and fluid variations.

The anatomy of AVO crossplots

AVO crossplots are a simple and elegant way of representing AVO data. Offset variations in amplitude for reflecting interfaces are represented as single points on a crossplot of intercept and gradient. The advantage of this type of plot is that a great deal of information can be presented and trends can be observed in the data that would be impossible to see with a standard offset (or angle) versus amplitude plot. The crossplot is an ideal way of examining differences in AVO responses that may be related to lithologic or fluid-type variations. Commonly used techniques for revealing these differences include color-coding samples from the crossplot and using this as an overlay to a seismic display or creating weighted (or “equivalent angle”) stacks (i.e., linear combinations of intercept (R0) and gradient (G). The early literature approached AVO crossplots from the point of view of rock properties. Acentral concept that emerged from this work was the “fluid line,” a hypothetical trend based on a consideration of brine-filled rock properties together with simplifications of the reflectivity equations (Figure 1). If the intercept is plotted on the x axis and the gradient on the y axis, then for consolidated sand/shale rocks the top and base reflections form a trend from the upper left to the lower right quadrant of the crossplot that passes through the origin. When it was realized that data points for equivalent hydrocarbon-filled rocks plot to the left of this line, it became clear that normalizing the data against the fluid line might provide an optimum AVO indicator. Figure 1. AVO classes and the AVO crossplot. The similarity of the fluid-line trend to trends on time-window AVO crossplots generated from seismic was compelling, and many assumed these are the same. In fact, both models and real data examples show that in general this …

Yet another perspective on AVO crossplotting

Two recent articles in The Leading Edge have focused on the use of crossplotting to aid AVO interpretation: “Principles of AVO crossplotting” by Castagna and Swan (April 1997) and “Another perspective on AVO crossplotting” by Foster, Keys, and Reilly (September 1997). These articles highlight the usefulness of AVO attribute crossplotting to delineate anomalies that may be caused by hydrocarbons or unusual geology. The method relies on the common observation that points on an AVO crossplot associated with nonhydrocarbon‐bearing rocks cluster along approximately linear trends, and points associated with hydrocarbon‐bearing rocks fall on different (linear) trends.

Lithologic Color-Coded Sections by AVO Crossplots: ABSTRACT

Lithologic Color-Coded Sections by AVO Crossplots: ABSTRACT