Seismic Data Similarity via Dip-Angle Domain Migration
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Solution Overview
Problem
Conventional 4D processing methods struggle to accurately compare and compensate for differences in acquisition geometries between seismic datasets from different survey techniques, leading to the generation of 4D noise and inaccurate representation of subsurface changes related to fluid production.
Innovation Solution
The method involves migrating seismic data to a dip-angle image domain, where decimating weights are calculated to maximize similarity between seismic datasets from different survey vintages, allowing for the reduction of noise and improved representation of subsurface changes by recombining weighted partial images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 4D processing methods are used to compare seismic datasets from different survey techniques, then processing speed is maintained, but measurement precision deteriorates due to inability to accurately compensate for acquisition geometry differences
Solution Approach 1:
The patent transforms the similarity measurement from conventional data domain attributes to image domain attributes after migration. This dimensional change allows for more accurate comparison of seismic datasets by evaluating similarity in the migrated image space rather than in the raw data space, thereby resolving the contradiction between measurement precision and processing complexity.
Solution Approach 2:
The patent introduces an image domain as an intermediary space between the raw seismic data and the final comparison. By migrating datasets to the image domain and performing similarity assessment in this intermediate representation, the method achieves more accurate geometry compensation while maintaining a structured processing framework.
2Manufacturing precision
If seismic datasets with different acquisition geometries are processed together, then data coverage is improved, but manufacturing precision deteriorates due to generation of 4D noise
Solution Approach 1:
The patent applies local quality by calculating decimating weights for individual seismic traces or trace groups based on their specific acquisition geometry characteristics. This allows each trace to be weighted according to its local similarity to reference traces, thereby maintaining subsurface image accuracy while accommodating variations in acquisition geometry across the dataset.
Solution Approach 2:
The patent changes the parameter space by migrating from the data domain to the image domain, where similarity can be assessed in terms of migrated amplitude and position rather than raw trace attributes. This parameter transformation enables accurate comparison of datasets with different acquisition geometries by focusing on the geologically relevant image domain characteristics.
3Measurement precision
If decimating weights are calculated in the image domain, then measurement precision is improved, but loss of time increases due to additional migration processing
Solution Approach 1:
The patent applies preliminary action by performing the migration to the image domain once for each dataset before the similarity assessment. This preliminary transformation enables subsequent efficient weight calculations without requiring repeated migrations, thereby achieving high measurement precision while minimizing the time penalty of the additional processing step.
Data Source
AI summary
A method for increasing similarity between a first seismic dataset and a second seismic dataset from at least one seismic survey of a subsurface. The first and second seismic datasets are migrated to a dip angle image domain. The migrated first and second seismic datasets are used in the dip angle image domain to calculate a set of decimating weights to be applied to the first seismic dataset and the second seismic dataset to maximize a similarity between the first seismic dataset and the second seismic dataset. The decimated weights are applied to the first and second seismic datasets, and an image of the subsurface is generated using the first seismic dataset and the second seismic dataset following application of the decimated weights.