Autonomous Hydrocarbon Control Using RL and Constraint Prediction
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Solution Overview
Problem
Conventional process control techniques are inadequate for dynamic environments like oil and gas extraction sites, requiring significant human intervention for reprogramming, reconfiguration, and model rebuilding to adapt to changing conditions, limiting scalability and reliability.
Innovation Solution
A self-driving, self-optimizing control system using reinforcement learning and model-based approaches, with edge devices and cloud computing, to autonomously manage and optimize hydrocarbon site operations by predicting future states and adjusting control decisions based on constraints, minimizing human dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional process control techniques are used, then system simplicity is maintained, but adaptability to changing conditions deteriorates
Solution Approach 1:
The control system transitions from static conventional control to dynamic adaptive control through reinforcement learning models that continuously learn and adjust control policies based on changing environmental conditions, enabling the system to adapt its behavior dynamically without requiring manual reconfiguration
Solution Approach 2:
The reinforcement learning model enables the control system to self-optimize and self-adjust by automatically learning optimal control strategies from operational data, eliminating the need for human intervention in reprogramming or model rebuilding while maintaining high adaptability
2Extent of automation
If conventional control systems are used, then ease of operation is maintained, but extent of automation deteriorates
Solution Approach 1:
The reinforcement learning model implements self-service automation by autonomously learning optimal control policies and making control decisions without human intervention, while the system maintains ease of operation through automated model training and deployment processes that reduce operational complexity
Solution Approach 2:
The system incorporates continuous feedback loops where operational data is fed back to the reinforcement learning model for ongoing optimization, enabling autonomous adaptation to changing conditions while reducing the need for manual monitoring and adjustment
3Reliability
If conventional control systems are used, then device complexity is reduced, but reliability in dynamic environments deteriorates
Solution Approach 1:
The control system employs dynamic reinforcement learning models that continuously adapt to changing operational conditions, significantly improving reliability in dynamic hydrocarbon extraction environments compared to static conventional control systems
Solution Approach 2:
The control architecture is segmented into modular components including edge devices for local data processing, cloud-based training infrastructure, and distributed control agents, which manages complexity through organized modularity while enabling robust autonomous operation
4Productivity
If conventional control systems are used, then ease of manufacture is maintained, but productivity through optimization deteriorates
Solution Approach 1:
The reinforcement learning model continuously self-optimizes control policies based on real-time operational data, automatically improving productivity and operational efficiency without requiring manual intervention for model rebuilding or system reconfiguration
Solution Approach 2:
The system performs preliminary actions by pre-training reinforcement learning models with historical data and simulating future scenarios, enabling proactive optimization of control strategies before actual operational changes occur, thereby improving productivity
Data Source
AI summary
A method executable by one or more processors includes obtaining a measured value of a first variable at a current time step, estimating, with a first model, an estimated value of a second variable at the current time step based on the measured value of the first variable, generating, by a reinforcement learning model, a control decision for a subsequent time step based on the measured value of the first variable and the estimated value of the second variable, predicting, with a second model, a predicted value of the first variable for the subsequent time step based on the measured value of the first variable at the current time step, adjusting the control decision for the subsequent time step based on a constraint and the future value of the first variable, and controlling an actuator based on the control decision.