Energy Band Acoustic Processing for Real-Time Lithology Identification
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Solution Overview
Problem
Existing acoustic signal processing methods for drilling operations suffer from noise, uncertainty, and error, which hinder effective lithology identification and geosteering in real-time drilling applications.
Innovation Solution
A method involving transforming acoustic signals into frequency spectra using a frequency transformer, partitioning these into energy bands based on an energy band partitioning function, and determining representative frequencies and amplitudes to create an energy band series, which reduces noise and uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic signals are processed using traditional methods, then the processing can be performed with simple algorithms, but the results contain noise, uncertainty, and error that hinder accurate lithology identification
Solution Approach 1:
The frequency spectrum is segmented into multiple energy bands based on energy distribution characteristics. This segmentation allows the signal to be analyzed in distinct frequency regions, reducing the impact of noise and uncertainty in any single band while improving overall lithology identification accuracy through comprehensive multi-band analysis
Solution Approach 2:
Energy band series are introduced as intermediary representations between the raw acoustic signal and the final lithology identification. These energy band series serve as mediators that filter out noise and uncertainty while preserving the essential geological information needed for accurate formation characterization
2Loss of information
If the frequency spectrum is analyzed in detail with many frequency data points, then more information is available for analysis, but the complexity of processing increases
Solution Approach 1:
The complex frequency spectrum is segmented into a manageable number of energy bands, each representing a specific frequency range. This segmentation reduces processing complexity by grouping frequency data points into meaningful energy bands while retaining the essential information needed for lithology identification through representative amplitude and frequency calculations for each band
Data Source
AI summary
A method that includes obtaining a first acoustic signal and transforming the first acoustic signal into a first frequency spectrum using a frequency transformer, where the first frequency spectrum comprises a first plurality of frequency data points. The method further includes partitioning the first plurality of frequency data points into a first plurality of energy bands using an energy band partitioning function. The method further includes determining a representative frequency for each of the first plurality of energy bands and determining a representative amplitude for each of the first plurality of energy bands, where the representative amplitude is determined based on an energy conservation principle. The method further includes determining a first energy band series, where the first energy band series comprises the representative amplitude and the representative frequency for each of the first plurality of energy bands and determining, using the first energy band series, an acoustic signal characteristic.