Cross-Sensor Linearization for Optical Sensor Calibration
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Solution Overview
Problem
Current optical sensor calibration methods face challenges in reducing the number of reference fluids while maintaining accuracy, especially for non-linear mapping algorithms, and are costly and unsafe when increasing the number of fluids, with a lack of cross-sensor data linearization methods applicable across different optical element designs and configurations.
Innovation Solution
The implementation of a cross-sensor linearization method that uses a combination of measured and simulated optical sensor responses from a reduced set of reference fluids, applying a transformation model to standardize optical sensor responses across various designs and configurations, allowing for robust reverse transformation and fluid characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of reference fluids is reduced in calibration procedure, then calibration cost and safety are improved, but measurement precision deteriorates due to over-fitting and inadequate generalization
Solution Approach 1:
The patent introduces a linearization layer as an intermediary component between the optical sensor responses and the non-linear mapping algorithm. This linearization layer transforms the non-linear cross-correlation data from reduced reference fluid calibration into a linear relationship, enabling accurate generalization to downhole application fluids without requiring extensive reference fluid sets. The linearization layer acts as a mediator that reconciles the reduced calibration data with the requirements for accurate fluid characterization.
Solution Approach 2:
The patent transforms the calibration data by applying linearization operations that change the parameter space of the sensor responses. By converting non-linear cross-correlation relationships into linear ones through mathematical transformation, the system enables accurate fluid characterization with fewer reference fluids. This parameter transformation allows the reduced calibration dataset to adequately represent the broader range of downhole fluids.
2Measurement precision
If the number of reference fluids is increased for each optical sensor, then measurement precision is improved, but cost and safety deteriorate
Solution Approach 1:
The patent extracts and isolates the non-linear cross-correlation characteristics from the calibration data and applies linearization specifically to this component. By separating the linearization step from the full calibration process and applying it selectively to the cross-correlation data, the system achieves accurate fluid characterization without requiring all sensors to be calibrated with extensive reference fluid sets. This extraction approach reduces calibration burden while maintaining precision.
3Adaptability or versatility
If non-linear mapping algorithm is used, then adaptability to different fluid compositions is improved, but manufacturing precision deteriorates due to difficulty in generalization from reduced reference fluids
Solution Approach 1:
The patent replaces the direct non-linear mapping approach with a hybrid system that combines linear transformation (linearization layer) with subsequent non-linear analysis. This substitution allows the system to maintain the adaptability of non-linear algorithms for different fluid compositions while eliminating the generalization problems through the intermediate linear transformation step. The linearization layer preprocesses the data to enable reliable standardization across different sensor configurations.
Data Source
AI summary
A method includes obtaining a plurality of master sensor responses with a master sensor in a set of training fluids and obtaining node sensor responses in the set of training fluids. A linear correlation between a compensated master data set and a node data set is then found for a set of training fluids and generating node sensor responses in a tool parameter space from the compensated master data set on a set of application fluids. A reverse transformation is obtained based on the node sensor responses in a complete set of calibration fluids. The reverse transformation converts each node sensor response from a tool parameter space to the synthetic parameter space and uses transformed data as inputs of various fluid predictive models to obtain fluid characteristics. The method includes modifying operation parameters of a drilling or a well testing and sampling system according to the fluid characteristics.