NMR Tool Calibration Using Nonlinear Echo-Train Fitting
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Solution Overview
Problem
Conventional NMR tool calibration methods are inaccurate due to the assumption of multiplicative noise, which affects the determination of exponential decay parameters, and are computationally expensive, requiring extensive data stacking that is not feasible in short calibration windows, especially in varying temperature environments.
Innovation Solution
A method involving a non-linear fit of echo trains with statistical techniques to generate independent test sets, using initial linear fit values as a baseline, and selecting the most accurate set based on error thresholds to calibrate the NMR tool efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional linear fit methods are used to determine exponential decay parameters, then the calibration process is computationally simple, but the measurement precision deteriorates due to the incorrect assumption of multiplicative noise
Solution Approach 1:
The patent changes the noise model parameter from multiplicative to additive noise assumption, and transforms the exponential decay parameter estimation from linear fit to non-linear least squares fitting. This parameter change resolves the contradiction by achieving both computational simplicity and high calibration accuracy through the modified approach.
Solution Approach 2:
The patent replaces the conventional linear fitting mechanism with a non-linear least squares fitting mechanism. This substitution eliminates the need for incorrect multiplicative noise assumptions while maintaining computational efficiency, thereby resolving the contradiction between simplicity and accuracy.
2Measurement precision
If extensive data stacking is performed to improve calibration accuracy, then the measurement precision improves, but the loss of time increases making it infeasible in short calibration windows
Solution Approach 1:
The patent applies partial action by using a modified non-linear least squares fitting approach that achieves sufficient calibration accuracy without requiring extensive data stacking. This partial approach to data collection resolves the contradiction by obtaining adequate precision within short calibration windows.
Solution Approach 2:
The patent changes the fitting methodology parameter from conventional linear fit to non-linear least squares fit with additive noise assumption. This parameter change enables accurate calibration with fewer data samples, thereby reducing calibration time while maintaining or improving accuracy.
3Ease of operation
If conventional calibration methods are used in varying temperature environments, then the ease of operation is maintained, but the reliability deteriorates due to temperature sensitivity
Solution Approach 1:
The patent incorporates temperature compensation feedback into the non-linear least squares fitting process. The method uses measured temperature data to adjust the calibration parameters, creating a feedback loop that maintains reliability across varying temperature conditions while preserving ease of operation.
Solution Approach 2:
The patent changes the calibration model to include temperature as a variable parameter in the non-linear least squares fitting process. This allows the calibration to adapt to temperature variations, resolving the contradiction between operational simplicity and reliability in varying thermal environments.
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 enhances calibration accuracy with fewer data samples, reducing time and expense while maintaining laboratory efficiency, by leveraging statistical techniques to improve signal-to-noise ratio and reduce noise influence.
Implementation Method 1
nuclear magnetic resonance (NMR) logging... magnetic interactions with subsurface material
Implementation Method 2
a magnet assembly that produces a static magnetic field
Implementation Method 3
a coil assembly that generates radio frequency (RF) control signals and detects magnetic resonance phenomena
Data Source
AI summary
A method for calibrating an NMR tool includes receiving N echo trains from an NMR tool; performing a linear fit of the echo trains to determine an initial parameter of an exponential decay curve; generating a plurality of test sets, each identifying a subset of the echo trains as test samples and identifying at least one echo train as a control sample. The selected test samples in each test set is independent of the selected test samples in other test sets. The method also includes performing, for each test set, a non-linear fit of the test samples based on the initial parameter to determine a test value for the parameter of the test set; determining an error value for each test set; selecting a test set having an error value less than an error threshold; and calibrating the NMR tool based on the test value of the selected test set.