Azimuthal Amplitude Gradient Estimation via Windowed Statistical Correlation
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Solution Overview
Problem
Conventional methods for estimating azimuthal amplitude gradient in seismic data are limited by noise, data misalignments, and ambiguity between gradient polarity and symmetry azimuth, leading to unstable and inaccurate fracture orientation and magnitude determinations.
Innovation Solution
A windowed statistical method is employed to process 3-D seismic data by forming common image location gathers, performing linear regression, calculating joint correlations of seismic traces, and determining rotation angles to resolve symmetry azimuths, thereby stabilizing estimates and resolving ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If point-wise estimation method is used for azimuthal amplitude gradient, then calculation simplicity is maintained, but estimation stability and accuracy deteriorate due to noise and data misalignments
Solution Approach 1:
The patent combines multiple seismic traces within a sliding time window to perform joint correlation analysis, merging information from multiple azimuths and offsets to stabilize the gradient estimation. This pooling of data reduces the impact of noise and misalignments that plague point-wise methods.
Solution Approach 2:
The patent uses iterative optimization to refine the gradient estimates by comparing predicted and actual seismic amplitudes, adjusting parameters to minimize misfit. This feedback mechanism continuously improves estimation accuracy while accounting for noise and data quality variations.
2Productivity
If point-wise estimation method is used for azimuthal amplitude gradient, then computational speed is maintained, but measurement precision deteriorates due to ambiguity between gradient polarity and symmetry azimuth
Solution Approach 1:
The patent combines gradient estimates from multiple azimuths and offsets within a unified correlation framework, merging information that resolves the polarity ambiguity. By analyzing the joint statistical properties of traces across different geometries, the method distinguishes between true symmetry azimuths and artifacts.
Solution Approach 2:
The patent transitions from analyzing single azimuth profiles to examining the multi-dimensional joint correlation structure of traces across azimuth, offset, and time. This additional dimensional context provides the constraints needed to resolve the inherent polarity ambiguity in conventional methods.
3Productivity
If conventional linear regression is applied to each gather separately, then processing efficiency is maintained, but reliability deteriorates due to noise and data misalignments at different azimuths
Solution Approach 1:
The patent merges the processing of multiple gathers by performing joint correlation analysis on traces from different azimuths and offsets simultaneously within a sliding window. This unified approach maintains computational efficiency while leveraging the complementary information across geometries to improve reliability.
Solution Approach 2:
The patent employs a sliding time window that dynamically adapts to local data characteristics, adjusting the analysis volume to optimize the balance between computational efficiency and estimation reliability. This dynamic approach allows efficient processing while maintaining high reliability in varying data quality conditions.
Data Source
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AI summary
Method of estimating azimuthal amplitude gradient is disclosed. This method uses a correlation of seismic attributes within a sliding volume of data to obtain azimuthal gradient.