In the surface‐consistent hypothesis, a seismic trace is the convolution of a source operator, a receiver operator, a reflectivity operator (representing the subsurface structure) and an offset‐related operator. In the log/Fourier domain, convolutions become sums and the log of signal amplitude at a given frequency is the sum of source, receiver, structural, and offset‐related terms. Recovering the amplitude of the reflectivity for a given frequency is then a linear problem (very similar to a surface‐consistent static correction problem). However, this linear system is underconstrained. Thus, among the infinite number of possible solutions, a particular one must be selected. Studies with real data support the choice of a spatially band‐limited solution. The surface‐consistent operators can then be calculated very efficiently using an inverse Hessian method. Applications to real seismic data show improvement compared with previous techniques. Surface‐consistent deconvolution is robust and fast in the log/Fourier domain. It allows the use of long operators, improves statics estimation, and removes the amplitude variations due to source or receiver coupling.

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

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Standard prestack multiple elimination techniques, such as predictive deconvolution or Radon transforms, fail in the presence of complex structures. A technique popularized by the Delphi consortium offers an attractive alternative in 2-D because it is theoretically independent of subsurface structure and can therefore attack all

AVO crossplotting has been widely used in the past few years as a way of deriving improved hydrocarbon indicators from seismic data. By crossplotting the standard AVO attributes of intercept and gradient, it is possible to obtain an optimum combination of the two (the fluid factor), which is insensitive to the AVO effect of brine-saturated shales and sands. Any remaining AVO anomaly can then be attributed to hydrocarbons or other lithologic factors. In addition, the physical location of an AVO anomaly on the crossplot gives an indication as to the geological setting of the potential reservoir.However, like the stack, intercept and gradient are sensitive to noise. While the intercept standard-deviation is slightly higher than the stack, the gradient standard-deviation is much larger and dramatically increases with travel-time. This partly explains the scale difference between the two attributes that is always observed with real data. Furthermore, in the presence of noise, intercept and gradient become statistically correlated. This correlation biases fluid factor calculations so that this attribute is reduced to a mere far-offset stack.Since stack and gradient do not correlate statistically, their crossplot can be used to validate or dismiss a trend observed in an intercept versus gradient crossplot. If the trend still exists, albeit slightly rotated, in the stack versus gradient crossplot, it is a lithologic effect; if not, it is a statistical artifact. Applications to real data have shown that, in general, what could be interpreted as a background shale trend is in fact noise-related. Consequently, the calculated fluid factor corresponds to a far-offset stack, which is actually a legitimate hydrocarbon indicator, but not as good as the theoretical fluid factor. Improved hydrocarbon indicators can nonetheless be obtained using the statistical independence of stack and gradient.

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