Production Model Calibration Using Reinforcement Learning Feedback
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Solution Overview
Problem
Existing production models in oilfields require continuous calibration to match changing field conditions, which is time-consuming and prone to human error, necessitating improved methods for efficient and automated model parameter updating.
Innovation Solution
A reinforcement learning system with a surrogate model is used to track model deviations and suggest calibration parameters in real-time, utilizing a surrogate model to accelerate training and automate the model calibration process, reducing human intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used to update model parameters, then model accuracy can be maintained, but the process is time-consuming and prone to human error
Solution Approach 1:
The system enables self-service calibration by automatically detecting model deviations and triggering reinforcement learning models to identify optimal calibration parameters without human intervention. The production model continuously monitors its own performance and self-corrects through the automated calibration pipeline.
Solution Approach 2:
The patent replaces manual mechanical calibration processes with automated computational systems. The reinforcement learning model substitutes human operators, using algorithms to automatically adjust calibration parameters based on real-time model deviation detection.
2Reliability
If continuous model calibration is performed manually, then model reliability is maintained, but human error and operational complexity increase
Solution Approach 1:
The system implements continuous feedback loops where model predictions are compared against actual production data, deviations are detected, and calibration parameters are automatically adjusted. This closed-loop feedback mechanism maintains model reliability while eliminating manual intervention.
Solution Approach 2:
The calibration system serves itself by automatically detecting when calibration is needed and executing the calibration process without human operators. The system monitors its own performance metrics and triggers recalibration autonomously when performance degradation is detected.
3Productivity
If automated calibration systems are implemented, then time and human error are reduced, but system complexity increases
Solution Approach 1:
The surrogate model acts as an intermediary between the complex production model and the reinforcement learning system. It simplifies the training process by providing a computationally efficient approximation that accelerates calibration while maintaining accuracy, thereby reducing overall system complexity.
Solution Approach 2:
The system dynamically changes calibration parameters based on detected model deviations. The reinforcement learning model automatically adjusts parameter values to optimize model performance, transforming a static calibration process into a dynamic, adaptive system that responds to changing conditions.
4Loss of time
If reinforcement learning models are used for calibration, then automation and speed are improved, but computational resources and system complexity increase
Solution Approach 1:
The calibration system is segmented into distinct functional components: deviation detection module, surrogate model for accelerated training, reinforcement learning model for parameter optimization, and calibration application module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Data Source
AI summary
The present disclosure relates to systems and methods for automated model calibration. The systems and methods continuously track a model status of a production model deployed in a production environment and suggests calibration parameters in real time for the production model in response to changes in the production environment. The systems and methods use model outputs and field observations to calibrate the production model.