Image-Domain 4D-Binning for Seismic Similarity
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Solution Overview
Problem
Traditional 4D-binning methods fail to accurately compensate for differences in acquisition geometries between baseline and monitor seismic surveys, leading to the generation of 4D noise due to inadequate similarity measures in datasets with varying information content and wavefield sampling.
Innovation Solution
The proposed method involves calculating decimating weights in the image-domain to maximize similarity between baseline and monitor seismic data, using a data-domain decimation strategy linked to image-domain similarity measures, which allows for accurate comparison and matching of datasets with different acquisition geometries without relying on surface or data-domain trace attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data-domain 4D-binning methods are used to select traces from baseline and monitor surveys, then the processing is simpler and faster, but the similarity assessment is inaccurate when acquisition geometries differ, leading to 4D noise
Solution Approach 1:
The patent transitions the similarity assessment from the data domain to the image domain. Instead of comparing seismic traces directly in the data domain, the method migrates both baseline and monitor datasets to the image domain, where similarity is assessed on migrated images. This dimensional change enables accurate comparison of subsurface representations regardless of acquisition geometry differences, resolving the contradiction between accuracy and complexity.
2Reliability
If decimation is performed in the data domain using surface attributes, then the process is computationally efficient, but it fails to account for differences in information content and wavefield sampling
Solution Approach 1:
The patent shifts the decimation process from the data domain to the image domain. By assessing similarity on migrated images rather than raw seismic traces, the method ensures that decimation decisions are based on actual subsurface information content and wavefield sampling characteristics. This resolves the contradiction by enabling reliable compensation while maintaining processing efficiency through the use of standard migration algorithms.
3Measurement precision
If baseline and monitor datasets with different acquisition geometries are processed using traditional methods, then processing time is reduced, but 4D noise is generated due to inadequate similarity measures
Solution Approach 1:
The patent resolves the contradiction by performing similarity assessment in the image domain after migration. This allows for accurate comparison of datasets with different acquisition geometries (e.g., towed-streamer vs. ocean-bottom node) because the migrated images represent the same subsurface location regardless of surface geometry. The method maintains reasonable processing time by using efficient migration algorithms and image-domain operations.
Data Source
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AI summary
A method for increasing similarity between a base seismic survey and a monitor seismic survey of a same surveyed subsurface during a 4-dimensional (4D) project. The method includes receiving first seismic data associated with the base seismic survey; receiving second seismic data associated with the monitor seismic survey, wherein the monitor seismic survey is performed later in time than the base seismic survey; migrating the first and second seismic data to an image-domain; and calculating, with a processor, a set of decimating weights based on the migrated first and second seismic data in the image-domain, to maximize a similarity between the first seismic data and the second seismic data.