Q Filter Estimation for Acoustic Wellbore Cement Evaluation
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Solution Overview
Problem
Existing wellbore sensing systems face challenges in accurately evaluating data from wellbores due to constraints in the wellbore environment, which limits the accuracy of sensor data and subsequent evaluations.
Innovation Solution
The implementation of advanced data processing techniques, including the use of analytical Q filter time-frequency attenuation equations and up-sampling methods, to improve the accuracy of wellbore evaluations by enhancing the analysis of sensed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor data evaluation methods are used in wellbore environments, then the evaluation process is simple and fast, but the accuracy of the evaluation results is limited due to environmental constraints
Solution Approach 1:
The patent introduces an intermediary Q filter estimation process that acts as a mediator between the raw sensor data and the final evaluation. This intermediary step models the frequency-dependent attenuation effects in the wellbore environment, allowing the system to compensate for environmental constraints and improve measurement precision without requiring fundamental changes to the sensor hardware
Solution Approach 2:
The patent applies parameter changes by transforming the evaluation from a simple time-domain analysis to a frequency-domain analysis using Q filter estimation. By estimating the Q factor and applying frequency-dependent attenuation corrections, the system changes the parameters of data processing to achieve higher accuracy in evaluating wellbore conditions
2Measurement precision
If advanced data processing techniques like Q filter estimation are applied, then the accuracy of wellbore evaluations is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements partial action by applying Q filter estimation selectively to the most critical frequency ranges and time windows in the sensor data. Rather than processing the entire dataset with full complexity, the method focuses computational resources on the portions of data that provide the most valuable information for wellbore evaluation, thereby reducing overall processing time while maintaining accuracy
Solution Approach 2:
The patent applies preliminary action by performing Q factor estimation and attenuation correction before the final evaluation step. By pre-processing the data to compensate for expected environmental effects, the system reduces the computational burden of subsequent analysis steps and enables faster generation of accurate evaluation results
3Reliability
If Q filter estimation with up-sampling is used to analyze acoustic energy absorption, then the assessment accuracy of cement bonding quality is enhanced, but the device complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex hardware-based solutions with software-based signal processing techniques. Instead of using multiple physical sensors or complex measurement apparatus to achieve high reliability in cement bonding assessment, the system uses computational methods including Q filter estimation and up-sampling to extract accurate information from standard sensor data, thereby reducing device complexity while maintaining or improving reliability
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
These techniques enable more accurate assessments of wellbore conditions, such as cement bonding quality, by improving the analysis of acoustic energy absorption patterns, thereby enhancing the reliability of wellbore evaluations.
Implementation Method 1
improving the analysis of acoustic energy absorption patterns
Data Source
AI summary
Described herein are systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively referred to as “systems and techniques”) for improving an accuracy of determinations made using data sensed in a wellbore. Such systems and techniques may use a combination of sampled data and simulated data to generate updated datasets that may be used to make evaluations about how well a casing is cemented to subterranean formations where a wellbore is located.