Cross-Sensor Standardization for Optical Reservoir Analysis
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Solution Overview
Problem
Conventional methods for calibrating multivariate models for optical tools in reservoir fluid analysis are costly and inefficient, requiring large sample sets and frequent updates, especially as the number of sensors increases, due to the need for strict sensor-based fluid property prediction model calibration.
Innovation Solution
A cross-sensor standardization approach is implemented, where a representative sensor is selected based on primary optical elements, and a transformation model maps optical responses from other sensors to the representative sensor's responses, reducing the number of fluid property prediction models needed by calibrating instrument standardization and fluid property prediction models for the representative sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strict sensor-based fluid property prediction model calibration is performed for each sensor, then measurement precision is improved, but device complexity and calibration costs increase significantly
Solution Approach 1:
The patent applies universality by developing a single fluid property prediction model that can be applied across multiple sensors. Instead of creating separate calibration models for each sensor, the invention uses one standardized model that works universally for all sensors in the group, reducing the number of models from N (where N is the number of sensors) to just 1, while maintaining prediction accuracy through standardized sensor responses.
Solution Approach 2:
The patent changes the parameter space by transforming sensor responses into a standardized parameter space. By applying standardization transformations to sensor data before inputting to the prediction model, the system adjusts parameters (sensor responses) to a common reference frame, allowing a single model to handle multiple sensors without requiring separate calibrations for each sensor's unique response characteristics.
2Productivity
If the number of sensors is increased, then productivity and data coverage are improved, but calibration costs and time requirements increase
Solution Approach 1:
The patent enables a single fluid property prediction model to serve multiple sensors simultaneously. By standardizing sensor responses and using one universal model instead of multiple sensor-specific models, the calibration process time is dramatically reduced when adding new sensors, as they can be integrated into the existing standardized framework without requiring separate calibration procedures.
3Reliability
If sensor-based calibration is performed for each sensor, then reliability of individual sensor predictions is improved, but the overall system becomes less manageable and more costly
Solution Approach 1:
The patent improves ease of operation by reducing model management from handling N separate models to managing 1 standardized model. The universal approach allows consistent application of the same prediction model across all sensors, simplifying maintenance, updates, and quality control while maintaining reliability through standardized processing pipelines.
Solution Approach 2:
The patent applies parameter changes through standardization transformations that convert diverse sensor responses into a unified parameter space. This transformation ensures that the input parameters to the prediction model are consistent and reliable across all sensors, maintaining prediction reliability while simplifying model management through parameter unification.
Data Source
AI summary
The disclosed embodiments include a method, apparatus, and computer program product for generating a cross-sensor standardization model. For example, one disclosed embodiment includes a system that includes at least one processor; at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations comprising selecting a representative sensor from a group of sensors comprising at least one of same primary optical elements and similar synthetic optical responses and calibrating a cross-sensor standardization model based on a matched data pair for each sensor in the group of sensors and for the representative sensor. In one embodiment, the at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations further comprises generating the matched data pair, wherein the matched data pair comprises calibration input data and calibration output data.