Amplitude variation with offset (AVO) interpretation may be facilitated by crossplotting the AVO intercept (A) and gradient (B). Under a variety of reasonable petrophysical assumptions, brine‐saturated sandstones and shales follow a well‐defined “background” trend in the A-B plane. Generally, A and B are negatively correlated for “background” rocks, but they may be positively correlated at very high [Formula: see text] ratios, such as may occur in very soft shallow sediments. Thus, even fully brine‐saturated shallow events with large reflection coefficients may exhibit large increases in AVO. Deviations from the background trend may be indicative of hydrocarbons or lithologies with anomalous elastic properties. However, in contrast to the common assumptions that gas‐sand amplitude increases with offset, or that the reflection coefficient becomes more negative with increasing offset, gas sands may exhibit a variety of AVO behaviors. A classification of gas sands based on location in the A-B plane, rather than on normal‐incidence reflection coefficient, is proposed. According to this classification, bright‐spot gas sands fall in quadrant III and have negative AVO intercept and gradient. These sands exhibit the amplitude increase versus offset which has commonly been used as a gas indicator. High‐impedance gas sands fall in quadrant IV and have positive AVO intercept and negative gradient. Consequently, these sands initially exhibit decreasing AVO and may reverse polarity. These behaviors have been previously reported and are addressed adequately by existing classification schemes. However, quadrant II gas sands have negative intercept and positive gradient. Certain “classical” bright spots fall in quadrant II and exhibit decreasing AVO. Examples show that this may occur when the gas‐sand shear‐wave velocity is lower than that of the overlying formation. Common AVO analysis methods such as partial stacks and product (A × B) indicators are complicated by this nonuniform gas‐sand behavior and require prior knowledge of the expected gas‐sand AVO response. However, Smith and Gidlow’s (1987) fluid factor, and related indicators, will theoretically work for gas sands in any quadrant of the A-B plane.

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Recently Spratt (1987) showed how amplitude‐versus‐offset analysis (AVO) can be sensitive to small residual velocity errors. However, even when the velocity is determined perfectly, serious AVO distortions remain due to normal‐moveout stretch, differential tuning as a function of offset, spherical divergence, and source and receiver directivity patterns. I have found that all of these errors can be expanded in a Taylor series about the zero‐offset event time, assuming it is much larger than the wavelet width. The first term of this series represents the residual velocity error term found by Spratt, while the second term encompasses the remaining effects mentioned. In practice, either term can be larger than the underlying amplitude variations being estimated. For example, Ricker wavelet stretch leads to a peak AVO error which is 61 percent of the peak zero‐offset reflectivity, even though the velocity field is uniform and correct. This result is independent of the wavelet frequency, and the range of incidence angles used in the analysis. Positive gradients in moveout velocity amplify this error, while narrowband filtering of the data prior to AVO analysis greatly widens its temporal extent. Aligning a particular event with static shifts instead of normal‐moveout correction can eliminate stretch, but not differential tuning error, in a finely layered target zone whose wavelets overlap.

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The phenomenon of geopressure is essentially stratigraphic in nature. In most cases, its occurrence correlates strikingly well with some mappable geologic characteristics, such as lithology changes, sediment deformation, and faulting. High‐precision velocity estimates can be made from the apparent amplitude variations with offset (AVO) that result from moveout errors, even if the seismic data itself lacks any intrinsic AVO. These velocity estimates provide us with an opportunity to estimate cross‐sections and 3‐D volumes of the gradient of pore pressure with depth from surface seismic data. These cross‐sections and volumes may be obtained through the estimation of seismic interval velocities as a function of depth, subtraction of the shale compaction trend, and the calibration of trend deviations in terms of pore‐pressure gradients. When viewed in combination with stacked seismic sections, the pore‐pressure gradient sections provide the interpreter added information about the hydrogeology of the sediment. In this paper, we show examples of pressure gradients caused by a lithology change, sealing faults, and fluid migration flows. Pressure gradient cross‐sections are also extremely useful for the design of mud densities and casing prior to spudding a well.