Formation Slowness Estimation via Low-Frequency Asymptote Mapping
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Solution Overview
Problem
Acoustic or sonic logging in wellbore environments faces challenges in reliably extracting and validating signal dispersions, particularly at low frequencies, due to noise, interference, and scattering from borehole radius changes, which complicates the extraction and validation of signal dispersions.
Innovation Solution
The use of 2D frequency semblance or coherence calculations to generate a quality control log display that visually validates signal or dispersion response results, including innovative mapping operations to identify low-frequency asymptotes and present them as a visual map with high color contrast, applicable to various wave propagating modes such as dipole shear, quadrupole, and leaky-P waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic logging tools are used to measure signal responses at low frequencies, then formation slowness information can be obtained, but noise and interference from borehole radius changes and non-suppressed waves complicate the extraction and validation processes
Solution Approach 1:
The patent extracts and isolates the low-frequency asymptote signal from the complex acoustic response data. By specifically targeting and separating the low-frequency component that approaches the shear wave formation slowness, the method extracts the desired formation information while leaving behind the noise and interference components related to borehole radius changes and non-suppressed waves.
Solution Approach 2:
The patent applies local quality by focusing analysis on specific frequency ranges (low-frequency asymptote region) rather than treating all frequencies uniformly. The coherence map and confidence interval calculations are applied locally to identify regions where the low-frequency asymptote signal dominates, allowing precise formation slowness measurement in those specific frequency domains while ignoring noisy regions.
2Reliability
If 2D frequency semblance or coherence calculations are performed to validate signal dispersions, then extraction reliability improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary action by calculating the 2D frequency semblance or coherence map before extracting formation slowness. This pre-processing step creates a visual validation framework that identifies reliable signal regions and filters out noisy data beforehand, making the subsequent slowness extraction more reliable and straightforward despite the initial computational investment.
Solution Approach 2:
The coherence map serves as an intermediary between the raw acoustic response data and the final formation slowness measurement. This intermediate visual representation allows operators to validate signal quality, identify low-frequency asymptotes, and confirm measurement reliability before committing to final interpretation, thereby improving overall extraction reliability.
3Measurement precision
If low-frequency asymptotes are used to estimate formation slowness, then measurement capability is enhanced, but difficulty in detecting and validating signal dispersions at low frequencies increases
Solution Approach 1:
The patent transitions from 1D frequency analysis to 2D frequency-coherence analysis by generating coherence maps. This dimensional change adds a coherence dimension that makes low-frequency asymptotes visually distinguishable and easier to detect. The 2D representation reveals patterns and structures in the low-frequency data that are not apparent in traditional 1D spectra, thereby reducing detection difficulty.
Solution Approach 2:
The patent uses color-coded visual representations in the coherence maps to indicate different coherence levels and signal qualities. High coherence regions (indicating reliable low-frequency asymptotes) are visually distinct from low coherence regions (indicating noisy or unreliable data). This visual color-coding system makes it significantly easier to detect and validate low-frequency signal dispersions compared to traditional numerical analysis alone.
Data Source
AI summary
Techniques for estimating and visually presenting formation slowness are disclosed herein. The techniques include receiving acoustic signal responses from adjacent formations at a plurality of depths in a borehole environment, mapping a distribution of the acoustic signal responses at each depth according to slowness and a frequency values, determining at least one confidence interval to define a coherence threshold for the distribution of the acoustic signal responses at each depth, generating a variable density log for each depth based on the distribution of acoustic signals responses that satisfy the confidence interval for one or more frequency ranges, determining a formation slowness value for each depth based on the variable density log for the each depth, and presenting a semblance map that includes a slowness axis, a depth axis, the formation slowness value for each depth, and at least a portion of the distribution of acoustic signal responses at each depth.