Least-Squares Migration Using Common Reflectivity for 4D Seismic Imaging
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Solution Overview
Problem
Existing seismic data migration methods, particularly in 4D imaging, face challenges such as low resolution, acquisition geometry repeatability issues, and high computational costs, which can destroy the 4D signal and lead to inefficient processing of subsurface images.
Innovation Solution
A least-square migration (LSM) method that uses non-stationary filters based on common reflectivity to generate 4D images by calculating baseline and monitor filters, applying them to raw migrated data, and iteratively updating the migration data to improve image resolution and reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional migration algorithms are used, then processing speed is faster, but image resolution is poor and illumination is uneven
Solution Approach 1:
The patent implements an iterative least-squares migration process that dynamically adjusts the imaging model through multiple iterations. Each iteration refines the reflectivity model and updates the migration result, transforming a static single-pass migration into a dynamic iterative optimization process that progressively improves image resolution while managing computational load through efficient update strategies.
2Manufacturing precision
If iterative least-squares migration is used, then image resolution improves, but computational cost increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the migration operator and its adjoint, preparing the imaging geometry and velocity model before the actual iterative inversion. This preliminary setup includes computing ray paths, travel times, and illumination patterns that are reused across iterations, reducing the computational burden during the iterative refinement process.
Solution Approach 2:
The patent applies local quality by using different filters for baseline and monitor datasets in 4D imaging. This allows each dataset to be optimized independently according to its specific acquisition characteristics, improving the local quality of each image while maintaining overall computational efficiency through targeted rather than uniform processing.
3Manufacturing precision
If different filters are applied to baseline and monitor datasets, then each dataset is optimized, but 4D signal repeatability is compromised
Solution Approach 1:
The patent changes parameters by using dataset-specific filters that are optimized for each acquisition's characteristics while maintaining a common reflectivity model. This allows parameter optimization for each dataset (improving resolution) while keeping the underlying geological model consistent (maintaining repeatability), resolving the contradiction through hierarchical parameter management.
Data Source
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Figure 3A~3B
AI summary
A least-square migration, LSM, based method for generating a 4D image of a subsurface, including receiving (500) seismic data d related to the subsurface (210), the seismic data d including a baseline dataset dB and a monitor dataset dM, calculating (502) a baseline filter and a monitor filter based on a same common reflectivity r of the subsurface (210) and corresponding remigrated baseline data mB1 and remigrated monitor data mM1 so that the base filter applied to the remigrated baseline data mB1 equals the monitor filter applied to the remigrated monitor data mM1, applying (504) the baseline filter to raw migrated baseline data mB0 and applying the monitor filter to raw migrated monitor data mM0 to generate LSM baseline data mB and LSM monitor data mM, and generating (506) the 4D image of the subsurface.