Ten methods for the computation of attenuation have been investigated, namely: amplitude decay, analytical signal, wavelet modelling, phase modelling, frequency modelling, rise-time, pulse amplitude, matching technique, spectral modelling and spectral ratio. In particular, we have studied the reliability of each of these methods in estimating correct values of Q using three synthetic VSP seismograms for plane P-waves with different noise contents.

The investigations proved that no single method is generally superior. Rather, some methods are more suitable than others in specific situations depending on recording, noise or geology. The analytical signal method has been demonstrated to be superior if true amplitude recordings are available. Otherwise spectral modelling or, in the ‘ noise-free’ case the spectral ratio method, is optimal.

Finally, two field VSPs in sediments are investigated. Only in the case of the highest quality VSP can significant information be deduced from the computed attenuation.

The determination of the seismic quality factor q from vsp data: a comparison of different computational methods1

Time-lapse data are increasingly used to study production-induced changes in the seismic response of a reservoir as part of a reservoir management program. However, residual differences in the repeated time-lapse data that are independent of changes in the subsurface geology impact the effectiveness of the method. These differences depend on many factors such as signature control, streamer positioning, and recording fidelity differences between the two surveys. Such factors may be regarded as contributing to the time-lapse noise and any effort designed to improve the time-lapse signal-to-noise ratio must address the quantifiable repeatability of the seismic survey.

Although there are counter-examples (for example, Johnston et al., 2000), minimization of the acquisition footprint and repeatability of the geometry to equalize residual footprints in both surveys are considered important. This has been a key objective in the development of point receiver acquisition systems.

In this study, which develops the analysis from Kragh and Christie (2001), we examine the use of two repeatability metrics in assessing the similarity of two sets of repeat 2D lines acquired with a marine point receiver system. In one repeat set, no streamer positioning control was in use; in the other repeat set, positioning differences were minimized using the streamer positioning control.

There does not appear to be a standard measure of repeatability, defined as a metric, to quantify the likeness of two traces. One commonly used metric is the normalized rms difference of the two traces, at and bt within a given window t1-t2: the rms of the difference divided by the average rms of the inputs, and expressed as a percentage: \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[NRMS\ =\ \frac{200\ {\times}\ RMS(a\_{t}\ {-}\ b\_{t})}{RMS(a\_{t})\ {+}\ RMS(b\_{t})}\] \end{document} where the rms operator is defined as: \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[RMS(x\_{t})\ =\ \sqrt{\frac{{\Sigma}^{t\_{2}}\_{t\_{1}}(x_{t})^{2}}{N}}\] \end{document} and N is the number of samples in the interval t1-t2.

The values of nrms are not intuitive and …

Seismic repeatability, normalized rms, and predictability

Thin-skinned versus thick-skinned structural models for Apulian carbonate reservoirs: constraints from the Val d'Agri Fields, S Apennines, Italy

Seismic amplitudes yield key information on lithology and fluid fill, enabling interpretation of reservoir quality and likelihood of hydrocarbon presence. The modern seismic interpreter must be able to deploy a range of sophisticated geophysical techniques, such as seismic inversion, AVO (amplitude variation with offset), and rock physics modelling, as well as integrating information from other geophysical techniques and well data. This accessible, authoritative book provides a complete framework for seismic amplitude interpretation and analysis in a practical manner that allows easy application - independent of any commercial software products. Deriving from the authors' extensive industry expertise and experience of delivering practical courses on the subject, it guides the interpreter through each step, introducing techniques with practical observations and helping to evaluate interpretation confidence. Seismic Amplitude is an invaluable day-to-day tool for graduate students and industry professionals in geology, geophysics, petrophysics, reservoir engineering, and all subsurface disciplines making regular use of seismic data.

/pdf/seismic-amplitude-an-interpreter-s-handbook-1p79u8biew.pdf

Seismic Amplitude: An Interpreter's Handbook

A new prestack inversion algorithm has been developed to simultaneously estimate acoustic and shear impedances from P‐wave reflection seismic data. The algorithm uses a global optimization procedure in the form of simulated annealing. The goal of optimization is to find a global minimum of the objective function, which includes the misfit between synthetic and observed prestack seismic data. During the iterative inversion process, the acoustic and shear impedance models are randomly perturbed, and the synthetic seismic data are calculated and compared with the observed seismic data. To increase stability, constraints have been built into the inversion algorithm, using the low‐frequency impedance and background Vs/Vp models. The inversion method has been successfully applied to synthetic and field data examples to produce acoustic and shear impedances comparable to log data of similar bandwidth. The estimated acoustic and shear impedances can be combined to derive other elastic parameters, which may be use...

Simultaneous inversion of prestack seismic data for rock properties using simulated annealing

A crucial step in the use of synthetic seismograms is the estimation of the filtering needed to convert the synthetic reflection spike sequence into a clearly recognizable approximation of a given seismic trace. In the past the filtering has been effected by a single wavelet, usually found by trial and error, and evaluated by eye. Matching can be made more precise than this by using spectral estimation procedures to determine the contribution of primaries and other reflection components to the seismic trace. The wavelet or wavelets that give the least squares best fit to the trace can be found, the errors of fit estimated, and statistics developed for testing whether a valid match can be made. If the composition of the seismogram is assumed to be known (e.g. that it consists solely of primaries and internal multiples) the frequency response of the best fit wavelet is simply the ratio of the cross spectrum between the synthetic spike sequence and the seismic trace to the power spectrum of the synthetic spike sequence, and the statistics of the match are related to the ordinary coherence function. Usually the composition cannot be assumed to be known (e.g. multiples of unknown relative amplitude may be present), and the synthetic sequence has to be split into components that contribute in different ways to the seismic trace. The matching problem is then to determine what filters should be applied to these components, regarded as inputs to a multichannel filter, in order to best fit the seismic trace, regarded as a noisy output. Partial coherence analysis is intended for just this problem. It provides fundamental statistics for the match, and it cannot be properly applied without interpreting these statistics. A useful and concise statistic is the ratio of the power in the total filtered synthetic trace to the power in the errors of fit. This measures the overall goodness-of-fit of the least squares match. It corresponds to a coherent (signal) to incoherent (noise) power ratio. Two limits can be set on it: an upper one equal to the signal-to-noise ratio estimated from the seismic data themselves, and a lower one defined from the distribution of the goodness-of-fit ratios yielded by matching with random noise of the same bandwidth and duration as the seismic trace segment. A match can be considered completely successful if its goodness-of-fit reaches the upper limit; it is rejected if the goodness-of-fit falls below the lower one.

R.E. White

Papers

Partial coherence matching of synthetic seismograms with seismic traces