Adaptive Acoustic Filtering for Formation Slowness Estimation
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Solution Overview
Problem
Current acoustic waveform processing methods, such as Slowness-Time Coherence (STC) processing, often overestimate formation slowness due to dispersive nature of acoustic waves, especially at higher frequencies, requiring accurate borehole and fluid property data which can be variable and difficult to obtain.
Innovation Solution
The method employs adaptive filtering based on polynomial coefficients derived from modal analysis, using minimum and peak excitation frequencies to establish a frequency filter band that accurately estimates formation slowness, thereby correcting for dispersive behavior and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Slowness-Time Coherence (STC) processing is used to estimate formation slowness, then processing can be performed with available data, but the formation slowness is overestimated due to dispersive nature of acoustic waves at higher frequencies
Solution Approach 1:
The patent applies dynamics by making the frequency filter band adaptive rather than fixed. The filter band is dynamically adjusted based on the estimated formation slowness and borehole properties, allowing the processing system to adapt to different formation conditions and frequency dispersions, thereby resolving the contradiction between obtaining results and maintaining accuracy
Solution Approach 2:
The patent changes the parameters of the frequency filter band based on estimated formation slowness and borehole properties. By adjusting the frequency range used in STC processing according to the specific formation conditions, the method corrects for dispersive effects and improves measurement accuracy while maintaining processing capability
2Device complexity
If a fixed frequency filter band is used in STC processing, then processing is simplified, but accuracy deteriorates due to frequency-dependent dispersion effects
Solution Approach 1:
The patent transforms the static frequency filter band into a dynamic one that adapts to formation conditions. The filter band is adjusted based on estimated slowness and borehole properties, providing a balance between processing complexity and measurement accuracy by automating the adaptation process
Solution Approach 2:
The patent implements feedback by using the estimated formation slowness to adjust the frequency filter band for subsequent processing. This closed-loop approach allows the system to automatically correct for dispersion effects based on the actual formation conditions, improving accuracy without significantly increasing complexity
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 provides more precise estimates of formation slowness by using adaptive filtering to adjust frequency ranges, reducing overestimation and improving the accuracy of formation property calculations.
Implementation Method 1
An acoustic signal is transmitted by the acoustic source, and received at the receivers of the tool... The acoustic signal from the acoustic source travels through the formation adjacent to the borehole to the receiver array
Implementation Method 2
Current acoustic waveform processing methods, such as Slowness-Time Coherence (STC) processing, often overestimate formation slowness due to dispersive nature of acoustic waves, especially at higher frequencies
Data Source
AI summary
A method of processing acoustic waveform data is disclosed. An acoustic logging tool acquires acoustic waveform data. An adaptive frequency filter is determined. The acoustic waveform data is filtered using the adaptive frequency filter. A formation slowness is determined from the filtered acoustic waveform data. The lower bound of the adaptive frequency filter may be determined using a polynomial function of a minimum excitement frequency parameter, a slowness parameter, and a third parameter. The upper bound of the adaptive frequency filter may be determined using a polynomial function of a peak excitement frequency parameter, the slowness parameter, and the third parameter.