Adaptive Optical Sensor Calibration for Downhole Fluid Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional optical sensor calibration methods for downhole fluid analysis in oil and gas exploration are limited by their reliance on synthetic data, which can lead to unreliable predictions when faced with real-world variations and unexpected sensor signal changes, particularly due to unrealistic calibration data and failure to generalize on new data.
Innovation Solution
An adaptive calibration method using neural networks that integrates both synthetic and actual sensor inputs, enabling robust real-time fluid prediction by combining conventional synthetic sensor data with measured actual sensor data through a novel normalization scheme, allowing for in-situ signal processing workflow switching and improved data prediction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optical sensor calibration methods using synthetic data are used, then the calibration process is simple and fast, but the prediction reliability deteriorates when facing real-world variations and unexpected sensor signal changes
Solution Approach 1:
The patent combines synthetic sensor data with actual measured sensor data to create a hybrid calibration dataset. This merging allows the system to retain the speed advantages of synthetic data while incorporating the realism and variability of actual field measurements, thereby improving prediction reliability without sacrificing calibration efficiency
Solution Approach 2:
The patent transforms actual sensor responses into synthetic parameter space using data transformation models, then combines these transformed actual responses with purely synthetic data. This parameter transformation allows integration of real-world variability into the calibration process while maintaining computational efficiency
2Productivity
If calibration is performed using only synthetic sensor data, then the calibration process is efficient, but the model fails to generalize on new data and unexpected sensor signal changes
Solution Approach 1:
The patent merges synthetic calibration data with transformed actual sensor responses to create a more diverse and representative calibration dataset. This combination enables the model to learn from both simulated scenarios and real-world variations, improving its ability to generalize to new data while maintaining calibration efficiency
Solution Approach 2:
The patent performs preliminary transformation of actual sensor responses into synthetic parameter space before combining them with synthetic data. This preliminary processing ensures that real-world data is properly formatted and integrated, enabling better generalization without compromising calibration efficiency
3Loss of time
If cross-space data transformation models are trained on a small number of reference fluids, then the training process is fast, but the transformation quality deteriorates and fails to tolerate unexpected raw sensor signal change over time
Solution Approach 1:
The patent combines transformed actual sensor responses with synthetic calibration data to create an enriched training dataset. This merging provides the transformation model with both real-world variability and synthetic completeness, improving transformation quality and robustness to sensor signal changes while keeping training time manageable
Solution Approach 2:
The patent creates a composite calibration dataset that integrates transformed actual sensor responses and synthetic data. This composite approach leverages the strengths of both data sources—realism from actual measurements and completeness from synthetic data—to build more robust transformation models
Data Source
AI summary
A method comprises determining an adaptive fluid predictive model calibrated with a plurality of types of sensor data, wherein the plurality of types of sensor responses comprise a first type of sensor response associated with a synthetic parameter space and a second type of sensor response associated with a tool parameter space. The method comprises applying the adaptive fluid predictive model to one or more fluid samples from field measurements obtained from a tool deployed in a wellbore formed in a subterranean formation and determining a value of a fluid answer product prediction with the applied adaptive fluid predictive model. The method comprises facilitating a wellbore operation with the tool based on the value of the fluid answer product prediction.