Surface Aperture Common Image Gathers Seismic Processing
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Solution Overview
Problem
Current seismic data processing methods, particularly wave-equation pre-stack depth migration techniques, face challenges in computing aperture indexed Common Image Gathers (CIGs), which are essential for accurate subsurface imaging, due to limitations in wavefield extrapolation methods and reliance on asymptotic ray-based assumptions.
Innovation Solution
A method is proposed to directly determine aperture indexed CIGs using a double migration approach, involving a first standard Reverse-Time Migration (RTM) followed by a mid-point attribute RTM migration, with a division process to estimate aperture values, allowing for the computation of surface aperture indexed CIGs applicable to various depth migration processes, including RTM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional stacking is used to process seismic data, then the processing workflow is simple and fast, but the image quality deteriorates in areas with uneven illumination and diverse data quality
Solution Approach 1:
The patent applies local quality by implementing azimuth-dependent weighting factors that adjust the contribution of different azimuth ranges based on local data quality characteristics. This allows the stacking process to adapt to varying illumination conditions and data quality across different spatial locations, rather than applying uniform weighting throughout the entire dataset.
Solution Approach 2:
The patent introduces dynamic adaptation by automatically determining optimal azimuth ranges and weighting factors based on the actual distribution and quality of input data. The system dynamically adjusts stacking parameters rather than relying on fixed conventional settings, enabling adaptation to diverse geological conditions and data acquisition geometries.
2Reliability
If wide azimuth acquisition is used to improve subsurface illumination, then the signal to noise ratio improves, but data quality becomes uneven across different azimuths and locations
Solution Approach 1:
The patent addresses data quality inconsistency by implementing location-specific and azimuth-specific weighting factors. Each data contribution is evaluated and weighted according to its local quality characteristics, allowing the system to optimize the stacking process for each specific spatial location and azimuth range rather than treating all data uniformly.
Solution Approach 2:
The patent changes the stacking parameters dynamically by determining optimal azimuth ranges and weighting factors based on the actual data distribution and quality metrics. This parameter adaptation allows the system to maintain consistent image quality across wide azimuth acquisitions by adjusting the contribution of different azimuth ranges according to local conditions.
3Measurement precision
If velocity model inaccuracies are present, then the travel-time computations contain errors, but advanced velocity estimation techniques require more complex processing
Solution Approach 1:
The patent implements feedback by using the migrated data themselves to evaluate and determine optimal stacking parameters. The system analyzes the migrated results to identify azimuth ranges with consistent imaging quality and uses this feedback to adjust weighting factors, creating a self-correcting process that compensates for velocity model inaccuracies without requiring external velocity refinement.
Solution Approach 2:
The patent applies self-service by enabling the stacking process to automatically adapt to its own input data characteristics. The system determines optimal azimuth ranges and weighting factors based on the actual migrated data quality, allowing the process to self-optimize without requiring external velocity model refinement or complex preprocessing steps.
4Manufacturing precision
If reference trace quality is improved for local correlation stacking, then the stacking quality improves, but the method remains semi-automatic and dependent on reference selection
Solution Approach 1:
The patent implements self-service by automatically determining optimal azimuth ranges and weighting factors directly from the migrated data without requiring manual reference trace selection. The system analyzes the data distribution and quality characteristics to self-determine the stacking parameters, eliminating the semi-automatic nature of previous methods while maintaining high stacking quality.
Solution Approach 2:
The patent applies preliminary action by pre-determining optimal azimuth ranges and weighting factors based on the migrated data characteristics before performing the final stacking operation. This preliminary analysis of data quality and distribution enables the system to automatically configure the stacking process without requiring manual intervention during execution.
Data Source
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AI summary
The method processes input (DS,G[t]) including, for each of a plurality of shots at respective source locations, seismic traces recorded at a plurality of receiver locations. Common-mid-point-modulated data (D'S,G[t]) are also computed by multiplying the seismic data in each seismic trace by a horizontal mid-point. A depth migration process is applied (i) to the seismic data to obtain a first set of migrated data (MS[x,y,z]), and (ii) to the mid-point-modulated data to obtain a second set of migrated data (M'S[x,y,z]). For each shot, aperture values (â s [x, y, z]) are estimated and associated with respective subsurface positions, by a division process applied to the first and second sets of migrated data. A migrated value (Rx,y[z,a]) for a depth z and an aperture a in a surface aperture common image gather (CIG) at a horizontal position x, y is a migrated value of the first set of migrated data associated with a subsurface position x, y, z for a shot such that the estimated aperture value associated with that subsurface position is the aperture a.