Sonic Logging Slowness-Frequency Projection for Real-Time QC
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Solution Overview
Problem
Conventional Slowness-Frequency Analysis (SFA) techniques for sonic logging are computationally expensive and require significant computing power and bandwidth, making real-time quality control (QC) challenging, especially in situations with limited resources.
Innovation Solution
The method involves computing slowness values at a limited number of discrete frequencies, interpolating these values to generate slowness-frequency dispersions, and displaying them as QC indicators, reducing computational and bandwidth requirements by transforming sonic data from the time-space domain to the frequency-wave number domain using techniques like 2D-FFT or Radon transform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SFA techniques are used to extract dispersions over the entire frequency range, then measurement precision is improved, but computing power requirements increase significantly
Solution Approach 1:
The patent segments the frequency range analysis into discrete frequency points rather than continuous analysis. Slowness values are computed at specific discrete frequencies (e.g., 2-5 discrete points) rather than across the entire frequency spectrum, reducing computational operations while maintaining essential dispersion characteristics for QC purposes.
Solution Approach 2:
The patent applies partial action by computing slowness values at a limited number of discrete frequencies rather than performing complete dispersion extraction across the full frequency range. This partial computation (2-5 discrete points) provides sufficient QC information without the full computational burden of conventional SFA.
2Reliability
If conventional SFA techniques are used for complete dispersion extraction, then reliability of QC is improved, but bandwidth requirements for data transmission increase
Solution Approach 1:
The patent extracts only the essential slowness values at discrete frequencies from the complete waveform data, rather than transmitting and processing entire dispersion curves. This extraction of key parameters (slowness at 2-5 discrete frequencies) maintains QC reliability while dramatically reducing data transmission volume.
Solution Approach 2:
The patent creates a simplified representation (copy) of the dispersion information by computing slowness values at discrete frequency points rather than transmitting complete waveform or full dispersion data. This copied information preserves essential QC characteristics with minimal data volume.
3Power
If slowness values are computed at limited discrete frequencies, then computing power is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent changes the parameter of frequency analysis from continuous spectrum to discrete frequency points. By selecting specific discrete frequencies (2-5 points across the frequency range), the system achieves adequate slowness measurement accuracy for QC while minimizing computational power requirements.
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 enables reliable, real-time QC with reduced computing power and bandwidth usage, facilitating effective data transmission and processing in resource-constrained environments.
Implementation Method 1
transforming the acquired sonic data from a time-space domain to a frequency-wave number domain
Implementation Method 2
transforming the acquired sonic data from a time-space domain to a frequency-wave number domain
Data Source
AI summary
An example method for displaying sonic logging data associated with a formation surrounding a borehole can include acquiring sonic data at a plurality of depths using an acoustic array located in the borehole and transforming the acquired sonic data from a time-space domain to a frequency-wave number domain at a limited number of discrete frequencies. The method can also include estimating slowness values at the limited number of discrete frequencies from the transformed sonic data, interpolating the estimated slowness values to obtain a projection of one or more slowness-frequency dispersions of the acquired sonic data and displaying the projection of the slowness-frequency dispersions. The projection of the slowness-frequency dispersions can include a plurality of color bands corresponding to each of the limited number of discrete frequencies.