Calibration Transfer for Downhole Spectrometers
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Solution Overview
Problem
Existing methods for calibration transfer in fluid analysis, particularly in subterranean well drilling, face challenges in accurately transferring calibrations between different instruments due to sensitivity to small variations in wavelengths and absorbance, making it difficult to determine primary fluid components in reservoir formation testing.
Innovation Solution
The implementation of a robust calibration transfer method using a database, neural networks, and pattern recognition systems to select appropriate samples and tune parameters for instrument standardization, along with the development of a global oil library and experimental design for downhole filter spectrometers, enables effective calibration transfer across instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration is performed on one instrument at a standard calibration lab, then measurement precision is improved, but the calibration cannot transfer to a second instrument without adjustment
Solution Approach 1:
The patent introduces a database as an intermediary that stores reference spectral data and calibration information from multiple instruments. This database serves as a mediator between different instruments, allowing calibration parameters to be transferred and adapted across instrument platforms without requiring direct recalibration at each instrument, thus resolving the contradiction between calibration precision and transferability
Solution Approach 2:
The patent employs parameter transformation methods that adjust calibration parameters between different instrument characteristics. By changing and transforming calibration parameters to account for variations in wavelengths and absorbance between instruments, the system enables robust calibration transfer while maintaining measurement precision across different instrument platforms
2Productivity
If multivariate calibration is used to determine primary fluid components, then analysis capability is improved, but sensitivity to small variations in wavelengths and absorbance increases
Solution Approach 1:
The patent implements feedback mechanisms through iterative calibration processes and validation routines that monitor and adjust for variations in wavelength and absorbance. By continuously feedback-correcting calibration parameters based on observed variations, the system maintains reliable fluid component analysis while using multivariate calibration methods
Solution Approach 2:
The patent applies beforehand cushioning by pre-adjusting calibration parameters and incorporating tolerance ranges that compensate for expected variations in wavelength and absorbance before they affect measurement accuracy. This proactive approach cushions against the sensitivity to small variations inherent in multivariate calibration, ensuring reliable fluid component determination
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 allows for accurate and robust calibration transfer, enabling real-time determination of fluid components in reservoir formation testing, improving the precision and accuracy of fluid analysis across different instruments and environmental conditions.
Implementation Method 1
A filter spectrometer may be built into a downhole tool to generate a sample spectrum
Data Source
AI summary
A method of calibration transfer for a testing instrument includes: collecting a first sample; generating a standard response of a first instrument based, at least in part, on the first sample; and performing instrument standardization of a second instrument based, at least in part, on the standard response of the first instrument. Data corresponding to a second sample is then obtained using the second instrument and a component of the second sample is identified based, at least in part, on a calibration model.