Dispersion Slowness Processing via Histogram Statistics
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Solution Overview
Problem
Estimating formation slowness for dispersive waves propagating in earth formations is challenging due to the frequency-dependent velocity, which complicates the determination of subsurface geologic structures and hydrocarbon deposit identification.
Innovation Solution
The method involves acquiring dispersive array acoustic data, determining slowness-frequency coherence, and using an analytic function to characterize the histogram by matching statistics, thereby defining the cut-off frequency and estimating formation slowness for compressional, shear, and Stoneley waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If slowness-time coherence (STC) processing is used to analyze dispersive waves, then the data can be processed in the slowness-time plane, but it becomes difficult to determine formation velocity from the STC plot
Solution Approach 1:
The patent transforms the analysis from the slowness-time plane to the slowness-frequency plane. By computing slowness-frequency coherence (SFC) instead of slowness-time coherence, the method enables velocity determination through histogram analysis of slowness distribution at different frequencies, resolving the difficulty of extracting velocity information from STC plots.
2Productivity
If conventional STC processing is applied to dispersive waves, then processing can be performed, but the frequency-dependent velocity characteristic complicates formation slowness estimation
Solution Approach 1:
The patent segments the dispersive wave analysis by computing slowness-frequency coherence at multiple discrete frequencies. This allows the frequency-dependent velocity characteristic to be captured through a series of frequency-specific histograms, enabling accurate slowness estimation for each frequency component while maintaining processing efficiency.
Solution Approach 2:
The method changes the analysis parameter from time-domain coherence to frequency-domain coherence. By transforming the approach to work in the frequency domain and utilizing histograms of slowness distribution, the patent accurately captures the dispersive nature of waves while providing precise formation slowness estimates.
3Loss of information
If histogram analysis of slowness distribution is performed, then velocity information can be extracted, but the presence of dispersive characteristics requires sophisticated processing methods
Solution Approach 1:
The patent introduces slowness-frequency coherence (SFC) as an intermediary that bridges the raw acoustic data and the final velocity determination. The SFC computation at multiple frequencies, followed by histogram analysis, serves as a systematic intermediary process that extracts velocity information while managing the complexity of dispersive wave characteristics.
Data Source
AI summary
Dispersive array acoustic data are acquired. A histogram is determined from the semblance-frequency coherence of the data. The low frequency limit of the data is estimated by matching the statistics of the histogram to the statistics of a modeling function.