Thin-bed tuning frequency and thickness estimation
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Solution Overview
Problem
Conventional methods face challenges in accurately identifying and determining the thickness of thin-bed structures in subsurface formations due to interference effects in seismic data, which complicates the detection of closely spaced events and limits the resolution of thin-bed structures.
Innovation Solution
The approach involves analyzing time-series seismic data by splitting it into spectral components, determining instantaneous frequencies, calculating frequency differences, and using these to determine tuning parameters such as tuning frequency or thickness, which helps in identifying and quantifying thin-bed structures without the need for spectral balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic data analysis methods are used, then the resolution of thin-bed structures is limited, but the detection accuracy of thin-bed structures deteriorates
Solution Approach 1:
The patent applies spectral decomposition to segment the seismic signal into multiple frequency components. By analyzing the amplitude and phase of individual frequency components rather than the composite signal, the method can detect thin-bed structures that are otherwise below the resolution threshold of conventional seismic analysis.
Solution Approach 2:
The patent transforms the seismic data from the time domain to the frequency domain through spectral decomposition. This dimensional transformation enables the detection of thin-bed structures by analyzing frequency-specific amplitude and phase characteristics, providing an additional dimension for identifying geological features that are invisible in conventional time-domain analysis.
2Measurement precision
If spectral decomposition is applied to enhance thin-bed detection, then the detection capability improves, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential spectral components (amplitude and phase at specific frequencies) needed for thin-bed detection rather than performing complete spectral analysis across all frequencies. This selective extraction reduces computational complexity while maintaining the ability to detect thin-bed structures effectively.
Solution Approach 2:
The patent applies spectral decomposition at selected frequency points rather than continuously across the entire frequency spectrum. This partial application of spectral analysis provides sufficient information for thin-bed detection while significantly reducing the computational burden compared to full spectral decomposition.
3Measurement precision
If interference effects are present in seismic data, then the detection of closely spaced events is complicated, but the identification of thin-bed structures is improved through frequency analysis
Solution Approach 1:
The patent uses frequency-specific amplitude and phase as intermediary parameters to detect thin-bed structures. By measuring these intermediary spectral characteristics rather than directly analyzing the complex interference patterns in the time domain, the method simplifies the detection process while maintaining high accuracy in identifying thin-bed structures.
Data Source
AI summary
A method, apparatus, and program product analyze time-series data such as seismic data collected from a subsurface formation by splitting a time-series data set such as an individual seismic trace into a plurality of spectral components, each having an associated frequency, determining an instantaneous frequency for each spectral component, determining a frequency difference for each spectral component based at least in part on the associated and instantaneous frequencies therefor, and determining a tuning parameter based at least in part on the determined frequency difference of each spectral component. Doing so enables, for example, thin-bed structures in the subsurface formation to be identified, and in some instances, thicknesses of such structures to be determined.