Multi-mode dispersion analysis for formation slowness estimation
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Solution Overview
Problem
Existing methods for estimating formation slowness using acoustic wave processing often fail in noisy environments or small boreholes, as low-frequency asymptotes are missed due to strong noise or underdeveloped first flexural waves, leading to inaccurate shear wave slowness estimation.
Innovation Solution
A multi-mode dispersion analysis is performed using sonic logging tools to generate semblance maps, extract slowness dispersions, and determine formation type, with techniques like Prony or Matrix-pencil methods, and slowness density logs are used to identify wave types and initial body wave slowness, while adjusting models to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single-mode dispersion analysis is used to estimate formation slowness, then the method is simple to implement, but it fails in noisy environments or small boreholes where low-frequency asymptotes are missed
Solution Approach 1:
The patent combines multiple wave modes (first flexural mode, second flexural mode, and higher-order modes) into a unified dispersion analysis framework. By merging information from multiple modes with different threshold frequencies and dispersion characteristics, the method overcomes the limitations of single-mode analysis in noisy environments and small boreholes, achieving both reliability and practical implementability.
Solution Approach 2:
The patent transitions from single-mode analysis to multi-mode analysis by adding the dimension of mode diversity. This dimensional expansion allows the method to capture a broader spectrum of dispersion information, including high-frequency asymptotes that are inaccessible to traditional single-mode methods, thereby improving estimation accuracy without excessive complexity.
2Measurement precision
If first flexural waves are used for shear wave slowness estimation, then the lowest order provides good resolution, but the dispersion curve is affected by strong low-frequency noise in hard formations
Solution Approach 1:
The patent converts the harmful effect of low-frequency noise into a beneficial situation by utilizing higher-order flexural modes whose dispersion curves are less sensitive to low-frequency noise. The higher-order modes, while having higher threshold frequencies, provide clean dispersion data in the high-frequency range where low-frequency noise does not interfere, thus transforming the noise problem into an opportunity to use complementary wave modes.
Solution Approach 2:
The patent changes the operational parameters by selecting different wave modes with different frequency thresholds and dispersion characteristics. By adjusting which mode is analyzed based on the specific formation conditions and noise characteristics, the method optimizes the signal-to-noise ratio and maintains measurement precision across varying geological conditions.
3Measurement precision
If higher-order waves are analyzed, then high-frequency asymptotes can be captured, but the threshold cutoff frequency increases making detection more difficult
Solution Approach 1:
The patent creates a universal dispersion analysis framework that can handle multiple wave modes with different threshold frequencies and dispersion characteristics. This multi-functional approach allows the same processing methodology to extract high-frequency asymptotes from various modes, making the detection of higher-order waves feasible despite their higher thresholds by providing a unified analytical tool.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively estimates formation slowness by reducing mismatches between modelings and measurements, providing accurate classification of wave types and body wave slowness even in challenging logging conditions, such as hard formations or deviated wells.
Implementation Method 1
Waveform data of a plurality of waves traversing through a downhole formation are measured by a sonic logging tool
Implementation Method 2
A multi-mode dispersion analysis is performed using sonic logging tools to generate semblance maps, extract slowness dispersions
Data Source
AI summary
Methods to estimate formation slowness from multi-borehole modes and multi-mode dispersion estimation systems are presented. The method includes obtaining waveform data of a plurality of waves traversing through a downhole formation, wherein each wave of the plurality of waves has a different threshold cutoff frequency, and performing a multimode dispersion analysis of the waveform data to generate a semblance map of the wave comprising the plurality of waves. The method also includes obtaining a slowness dispersion of a wave of the plurality of waves, and determining a formation type of the wave based on one or more properties of the plurality of waves. The method further includes determining an initial body wave slowness estimate of the wave, generating a modeling of the wave, and reducing a mismatch between the modeling of the wave and the slowness dispersion of the wave to improve the modeling of the wave.