Phase-Based Dispersion Analysis for Acoustic Logging Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current acoustic logging methods face challenges in accurately analyzing and extracting relevant information from acoustic data due to limitations in signal processing, leading to poor resolution and susceptibility to noise, as well as issues with aliasing and the need for a priori knowledge of the number of modes.
Innovation Solution
A phase-based dispersion analysis (PBDA) method that derives phase data from acoustic signals, unwraps phase information, and calculates slowness by fitting a line to phase difference versus receiver spacing, providing a non-parametric and data-driven approach that generates high-resolution dispersion curves without relying on model-based assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used for acoustic logging data, then the processing can be performed with existing tools, but the resolution is poor and the results are susceptible to noise and aliasing
Solution Approach 1:
The patent applies parameter changes by transforming the acoustic signal from time domain to frequency domain through Fourier transformation, and then to phase domain. This parameter transformation enables the extraction of phase information at different frequencies, which when plotted against receiver spacing and fitted with a line, yields the slowness parameter. This approach resolves the contradiction by achieving high resolution and noise immunity through mathematical transformation of the signal parameters.
2Ease of operation
If model-based assumptions are used in dispersion analysis, then the analysis can be performed with fewer data requirements, but the results require a priori knowledge of the number of modes and are subject to aliasing
Solution Approach 1:
The patent extracts phase information from the Fourier transformed acoustic signal at multiple frequencies. By taking out the phase component specifically and plotting it against receiver spacing, the method isolates the useful information from the complex signal. The linear fitting of phase difference versus receiver spacing extracts the slowness parameter directly, eliminating the need for a priori knowledge of mode numbers and preventing aliasing issues that plague model-based approaches.
3Measurement precision
If complex signal processing algorithms are used to improve resolution, then the measurement precision improves, but the computational cost increases
Solution Approach 1:
The patent replaces complex iterative signal processing algorithms with a straightforward mathematical approach: Fourier transformation followed by phase extraction and linear fitting. This substitution of computational mechanics achieves high precision slowness calculation without requiring heavy computational resources, as the method relies on basic linear algebra operations rather than complex optimization algorithms.
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
The PBDA method improves resolution and reduces aliasing, providing accurate and unambiguous slowness calculations with lower computational costs, enhancing the ability to determine formation velocities and attributes.
Implementation Method 1
an acoustic source to energize at plural frequencies an earth formation penetrated by a borehole
Implementation Method 2
operating receivers defining plural, longitudinally spaced receiver stations to receive acoustic energy altered by the earth formation
Data Source
AI summary
Disclosed herein is method of computing formation attributes from acoustic measurements in a borehole. The acoustic measurements can be made by operating an acoustic source at multiple frequencies to excite the formation and operating receivers at multiple, longitudinally spaced receiver stations to receive acoustic energy from the formation. The method can include: deriving phase data from the spectrum of received acoustic signals; unwrapping phase information of the phase spectrum data; determining two or more values of difference of phase between acoustic signals at each of a range of frequencies each based on a single generated signal received at two or more pairs of adjacent said receiver stations; generating a value of slope of phase difference values; and in any case of slope ambiguity, unwrapping phase difference information and deriving a dominant slope, at each frequency, from which slowness of the acoustic signal in the formation can be derived.