Time-lapse processing and imaging require careful attention to many details in order to remove the effects of non-repeatable sources of noise and variations in
signal from the true 4D effects that lie within the data.
The purpose of 4D processing is to attenuate the non-repeatable effects on the data due to changes in near-surface conditions upon the signal and the noise. For example, statics differences due to
In general, TGS uses a careful approach at each processing step using 4D statistics to monitor whether each processing step is helping or hindering the 4D result.
Calculates 4D statistics namely NRMS, Predictability, Signal to Distortion Ratio and RMS ratio. NRMS, Predictability and RMS ratio rely on instantaneous values and can provide a measurement of similarity between vintages. They can, however, be sensitive to time shifts between the two vintages and do not necessarily capture wavelet shape differences. The Signal to Distortion Ratio is sensitive to wavelet shape differences between vintages. RMS ratio is a measure of the overall amplitude difference between vintages.
Identifies trace pairs that are close to each other in a 4D bin defined by
Correcting for phase, amplitude and statics differences between vintages
Amplitude, statics and phase differences exist between vintages in both marine and land time-lapse surveys due to many factors such differences in acquisition design and near-surface conditions. TGS has a full suite of processing tools to handle the challenges that different processing projects and datasets may present. These include many of the industry-leading modules used during everyday AVO-compliant processing of land and marine data. These tools include deterministic approaches to removing variations in the source signatures and receiver responses from the data, plus residual phase, amplitude and statics effects using mostly statistical approaches. TGS also has 4D match filtering capabilities that can be used to minimize any residual 4D differences. During 4D processing, all modules are used in conjunction with the 4D statistics and 4D binning tools.
The regularization of seismic datasets is usually used in 4D processing because it is an important method of reducing the effect of differences in survey design and irregularities. It is able to map any monitor geometry onto a baseline survey, or it can be used to interpolate all vintages onto a more complete regular grid. The use of five dimensions in the interpolate guarantees accuracy and can dramatically improve the 4D results.
This example illustrates the ability of 4D processing to reduce the nonrepeatable effects and reveal the true 4D effects between data vintages.
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