Dual-Sensor Optical Data Processing via Master Sensor Standardization
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Solution Overview
Problem
Conventional multi-sensor downhole tools require frequent calibration and maintenance, leading to high costs and inefficiencies due to sensor-dependent fluid models and inconsistencies in data interpretation across multiple sensors.
Innovation Solution
Implementing a master sensor standardization method that maps dual-sensor data into a single framework using non-linear cross-sensor standardization algorithms, allowing for data transformation between tool and synthetic parameter spaces, thereby reducing uncertainty and simplifying data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple optical sensors are used in downhole tools, then the capability for comprehensive fluid analysis is improved, but the complexity of calibration and maintenance increases
Solution Approach 1:
The patent merges multiple sensor data streams into a unified framework by establishing a master sensor that serves as a reference. Cross-sensor standardization algorithms transform readings from multiple operational sensors into the master sensor's parameter space, allowing all sensors to be calibrated through a single master sensor calibration process, thereby reducing overall calibration complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The master sensor serves multiple functions: it acts as a reference for cross-sensor standardization, provides a unified calibration framework for all operational sensors, and enables consistent fluid characterization across different sensor types. This universal approach eliminates the need for separate calibration procedures for each sensor while preserving the ability to analyze diverse fluid properties
2Measurement precision
If separate fluid models are maintained for each sensor, then sensor-specific optimization is improved, but the cost and time for calibration and maintenance increase
Solution Approach 1:
The patent combines multiple sensor-specific fluid models into a single unified fluid model framework. By transforming all sensor readings to the master sensor's parameter space through cross-sensor standardization, the system maintains sensor-specific optimization benefits while using a single calibrated model, significantly reducing calibration and maintenance time
Solution Approach 2:
Instead of maintaining separate fluid models for each sensor, the patent creates a master sensor fluid model that serves as a template. Operational sensor readings are transformed to match the master sensor's parameter space, effectively copying the master model's calibration across all operational sensors without requiring separate model development for each
3Adaptability or versatility
If different optical parameters are used as fluid model inputs for different sensors, then each sensor's specific characteristics are captured, but data interpretation complexity increases
Solution Approach 1:
The patent applies parameter transformation by converting optical parameters from different sensor types into a unified parameter space defined by the master sensor. Cross-sensor standardization algorithms transform readings from various optical parameters into consistent master sensor parameters, allowing a single fluid model to process data from all sensors without requiring separate interpretation procedures for each sensor type
4Measurement precision
If predictions from multiple sensors are used, then comprehensive fluid characterization is improved, but the difficulty of ensuring prediction consistency increases
Solution Approach 1:
The master sensor acts as an intermediary that mediates between multiple operational sensors and the fluid model. By transforming all operational sensor readings to the master sensor's parameter space through cross-sensor standardization, the master sensor serves as a common reference frame that ensures consistent predictions across all sensors while maintaining comprehensive fluid characterization capability
Data Source
AI summary
A method may include transforming optical responses for a fluid sample to a parameter space of a downhole tool. The optical responses are obtained using a first operational sensor and a second operational sensor of the downhole tool. Fluid models are applied in the parameter space of the downhole tool to the transformed optical responses to obtain density predictions of the fluid sample. The density predictions of the first operational sensor are matched to the density predictions of the second operational sensor based on optical parameters of the fluid models to obtain matched density predictions. A difference between the matched density predictions and measurements obtained from a densitometer is calculated, and a contamination index is estimated based on the difference.