Seismic Partition Image Weighting for Dip Field Correlation
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Solution Overview
Problem
In subsurface imaging, particularly in subsalt areas with complex velocity models, existing methods face challenges in accurately illuminating target dipping directions due to noise from partition images that do not align well with the geological structure, leading to suboptimal image enhancement and drilling precision.
Innovation Solution
The method involves generating partition images from seismic data, determining dip fields and their correlation with a target dip field, assigning weights based on correlation to produce weighted images, and iteratively refining the geological model to enhance image quality and drilling accuracy by amplifying relevant seismic signals and suppressing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all partition images are stacked equally to produce the raw image, then the complete seismic data is utilized, but noise from mismatched dipping directions degrades image quality
Solution Approach 1:
The patent applies local quality by assigning different weights to different partition images based on their dip field correlation with the target dip field. Partition images with higher correlation receive higher weights, while those with lower correlation receive lower weights. This selective weighting ensures that each partition image contributes appropriately to the final stacked image, reducing noise from mismatched directions while preserving useful seismic signals.
Solution Approach 2:
The patent changes the parameter of weight assignment for partition images based on dip field correlation. By calculating the correlation between each partition image's dip field and the target dip field, the system dynamically adjusts the weighting parameter to optimize the stacking process. This parameter change enables the system to adaptively enhance image quality by suppressing noise from partition images with poor correlation.
2Measurement precision
If partition images with low correlation to target dip field are included, then more seismic data is processed, but the illumination of target dipping direction is obscured
Solution Approach 1:
The patent applies local quality by differentiating the contribution of each partition image based on its dip field correlation with the target. Instead of uniform treatment, each partition image is evaluated locally according to its correlation metric, and weights are assigned accordingly. This ensures that partition images with low correlation contribute minimally, preventing them from obscuring the target dipping direction illumination.
Solution Approach 2:
The patent introduces asymmetry in the weighting scheme where partition images are not treated equally. Those with high correlation to the target dip field receive asymmetrically higher weights compared to those with low correlation. This asymmetric weighting strategy prioritizes the illumination accuracy of the target dipping direction while still incorporating relevant seismic data.
3Reliability
If weights are assigned based on dip field correlation, then image enhancement is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by calculating the dip fields of partition images and determining their correlation with the target dip field before the stacking process. This pre-computation of correlation metrics allows the system to establish weights in advance, avoiding the need for complex real-time calculations during stacking. The preliminary determination of weights simplifies the overall computational process while maintaining image enhancement quality.
Solution Approach 2:
The patent uses feedback by comparing the dip fields of partition images against the target dip field to determine correlation degrees. This feedback mechanism guides the weight assignment process, allowing the system to iteratively optimize the weighting scheme based on the measured correlation. The feedback loop ensures that the computational complexity is justified by the improved image enhancement quality.
Data Source
Figure 1A~1D
Figure 2
Figure 3A
AI summary
A method for generating an image of a subterranean formation includes receiving seismic data that was collected from seismic waves that propagated in the subterranean formation. Partition images are generated using the seismic data. A geological model of the subterranean formation is generated. Dip fields in the partition images are determined. A target dip field in the geological model is determined. A degree of correlation between the respective dip fields and the target dip field is determined. Weights are assigned to the partition images based upon the degrees of correlation to produce weighted partition images. The image of the subterranean formation is generated by stacking the weighted partition images.