Parameters tested are left to right 0. Errors in the time-depth curve will propagate into your extracted wavelet. Then go back and extract a better constant-phase wavelet no more Ricker through autocorrelation and try to modify the constant phase while simultaneously tweaking the time-depth curve along the well. The predictive deconvolution operator basically attempts to produce a trace with zero autocorrelation between the gap and operator length b. Design a cross-equalization filter that takes the input from step 1 as the source and step 2 as the target. Panel 1 provides just wavelet compression and no multiple suppression. Once you are happy with the position of your synthetic with respect to the seismic data, then you can apply the method of Walden and White to get the best fit filter that gets you from the reflectivity at the well to the seismic data along the well path. The spike deconvolution affords maximum resolution, is often too noisy, but should always be tested, even if only as a reference section. Typically a long gap e. Please vote for quality content.

• is a filtering process which removes a wavelet from the recorded. Since this is rarely true in practice the autocorrelation of the seismic trace is used performs a zero-phase conversion called Phase Deconvolution in PROMAX. HGS now called Western) called DESIG used to statistically extract a wavelet.

Video: Statistical deconvolution seismic exchange Lesson 17 - Seismic Processing

ABSTRACT Wiener 'spiking' deconvolution of seismic traces in the absence of a known source wavelet relies upon the use of digital filters, which are optimum in.
Click here to enlarge the figure. The autocorrelation functions associated with the deconvolution parameters should also be displayed. Extraneous noise and amplitude variations which may bias the autocorrelation should therefore be omitted from the computation.

Some contractors refer to the total operator length as the length of the gap plus the operator length, others refer ambiguously to this as the operator length. This should be expected since we know higher frequencies are progressively removed from the wavelet as it travels though the earth by the processes of attenuation and absorption.

 COMPARE AND CONTRAST TWO SPORTS ESSAY TITLES The results of this procedure can be quite unpredictable and should be avoided if possible. Hot Network Questions. For noisy data an increased percentage of white noise can be used to improve the deconvolution results.The objective of deconvolution would be to suppress the multiple reflections. Design a cross-equalization filter that takes the input from step 1 as the source and step 2 as the target. Generally in the North Sea data is too noisy for spike deconvolution to be effective even when followed by bandpass filtering. This is accomplished by choosing a design window for the autocorrelation function.
Keywords: mixed-phase deconvolution, seismic data processing, wavelet estimation.

. inverse ﬁlter is calculated using the statistical method of least. squares Table 3 – Mutation process showing the random exchange of. nel blind deconvolution of seismic signals, which exploits lateral.

continuity of Earth layers. denotes white Gaussian noise, which is statistically independent. Seismic data with reliable amplitude and phase can significantly impact drilling Surface-consistent deconvolution decomposes the seismic wavelet into five . The caveat is that this conversion of phase is not unique because there is no Surface-consistent deconvolution reduces those uncertainties by statistically.
The DBS often performs best after multiple suppression e. What you can do is to first approximate the real wavelet with a simple zero-phase phase wavelet extracted from the autocorrelation of the seismic data or you can use a Ricker wavelet of a reasonable dominant frequency if you want and make a synthetic seismogram at the well.

Waveshaping deconvolution is designed to convert one wavelet into another.

This is accomplished by choosing a design window for the autocorrelation function. This is because the job can largely be done by the DAS at a later stage in the processing where it is cheaper and easier to redo if the wrong parameters are chosen. Longer operator lengths e.

 Jackson park ministries directions maps Examples include signature deconvolution where a mixed phase source signature is converted to it's minimum phase equivalent and zero-phase conversion. Longer operator lengths e.For deeper targets a 24ms or 32ms gap is probably the commonest used in the North Sea. The adjacent figure compares left to right gaps of 4ms spike12ms, 16ms, 24ms and raw. For noisy data an increased percentage of white noise can be used to improve the deconvolution results. The spike deconvolution affords maximum resolution, is often too noisy, but should always be tested, even if only as a reference section.
the case of spiking deconvolution (statistical deconvolution), whereas it is computed directly from the known source wavelet One way to extract the seismic wavelet, provided it is minimum phase, is to compute the spiking. My preferred way of doing this (I've been working in seismic data Wiener deconvolution) that make a statistical estimate of what the wavelet.

Keywords: mixed-phase deconvolution, seismic data processing, wavelet. filter is calculated using the statistical method of least e3 x3 x2 x1 .

selection, mation exchange methods more complex. crossover and mutation.
As we know the mixed phase data would not give the "real TWTT" through the medium.

It is also sensible to note that if the data is required to tie other vintages then it is wise to check the parameters applied to these vintages since a mis-match could result in change of character of a target event which could conceivably lead to mis-interpretation.

For noisy data an increased percentage of white noise can be used to improve the deconvolution results. The method tries to estimate and then remove the predictable parts of a seismic trace usually multiples.

Previous experience in the data area is also useful since usually the trials are designed around some initial guess at the final parameters.

The deconvolution gap probably has the most effect on the final appearance of the deconvolved data. For example, if you know that your data contains an impulsive minimum phase source wavelet i.