Wellbore Acoustic Signal Deconvolution for Defect Detection
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Solution Overview
Problem
Unwanted noise from wellbore environments distorts acoustic data, leading to inaccurate determinations and potential wellbore failures.
Innovation Solution
Computer modeling techniques simulate wellbore strata and structures to eliminate noise distortions, allowing for accurate identification of sound sources and defects by deconvoluting measured spectral data using Green's function and acoustic modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If acoustic sensors are used to collect data from wellbore environment, then data collection capability is improved, but measurement precision deteriorates due to unwanted noise
Solution Approach 1:
The patent extracts and removes unwanted noise components from the acoustic signal through spectral analysis and filtering techniques. The system identifies noise frequencies in the spectrum and selectively removes them while preserving the useful acoustic information, thereby improving measurement precision without sacrificing data collection capability
Solution Approach 2:
The patent introduces an intermediary processing system that includes spectral analysis, noise identification, and signal reconstruction components. This intermediary process acts as a mediator between the raw noisy acoustic data and the final clean signal, enabling separation of useful information from noise through mathematical transformations and filtering operations
2Measurement precision
If noise removal processing is applied to acoustic data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex hardware-based noise filtering systems with software-based signal processing techniques. By using spectral analysis, Fourier transforms, and digital signal processing algorithms, the system achieves effective noise removal through computational methods rather than complex mechanical or electronic filtering hardware
Solution Approach 2:
The patent transforms the acoustic signal from time domain to frequency domain through spectral analysis, changing the representation parameters of the signal. This parameter transformation enables easier identification and separation of noise components from useful signals, simplifying the overall processing approach while improving precision
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
Enhances the accuracy of wellbore data analysis, ensuring safer and more effective operation by identifying and addressing potential defects.
Implementation Method 1
A computer model is operated to solve a wave equation and identify frequency dependent distortion introduced by wellbore strata and structures on sound from a sound source
Implementation Method 2
The Green's function is used to deconvolute sets of measured acoustic spectra such that the spectral content of a sound source may be identified
Data Source
AI summary
When a wellbore is manufactured or is operated, a wellbore defect can result in the wellbore failing. While hydrophones may be used to collect data indicative of a wellbore defect, reflections, oscillations, or harmonics of sounds indicative of the defect may result in inaccurate determinations being made. This is because such reflections, oscillations, or harmonics may mask the sounds that are indicative of the wellbore defect. As such, systems and methods of the present disclosure are directed to computer modeling techniques that simulate the effects of wellbore strata and structures such that these effects can be eliminated from datasets. By making more accurate determinations, safety of a wellbore may be enhanced. Methods of the present disclosure may be used to identify when a wellbore is safe to operate.