Digital Agents for Production Simulator Automation
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Solution Overview
Problem
Production simulators in gas and oil fields require substantial training and expertise to operate effectively, with minimal automation between the simulator and digital twin assets, leading to inconsistent simulation outcomes based on the operator's skill level.
Innovation Solution
Digital agents act as an interface between the production simulator and the operator, providing guided instructions, suggesting relevant assets, and flagging errors, enabling untrained users to operate the simulator by leveraging a library of assets and models, and minimizing errors through automated parameter updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If production simulators use specialized interfaces with many inputs and controls, then simulation functionality and capability are improved, but operator training requirements and operational complexity increase substantially
Solution Approach 1:
A digital agent is introduced as an intermediary between the operator and the production simulator interface. The digital agent translates simple user requests into complex simulator commands, managing the many inputs and controls automatically. This mediator handles the complexity of the specialized interface while presenting a simplified interaction model to the operator, resolving the contradiction between simulation capability and ease of operation.
Solution Approach 2:
The digital agent enables the production simulator to serve itself by automatically configuring and executing simulation scenarios based on high-level user instructions. The system self-manages the complex interface interactions, asset selections, and parameter settings without requiring the operator to manually navigate through numerous controls, thereby reducing training requirements while maintaining full simulation functionality.
2Measurement precision
If production simulators rely on manual operation by experienced technicians, then simulation accuracy and asset utilization are improved, but consistency and reproducibility deteriorate due to varying operator skill levels
Solution Approach 1:
The digital agent enables the simulation system to operate autonomously without relying on human operator expertise. The agent automatically identifies and configures relevant simulator assets, selects appropriate simulation scenarios, and executes simulations consistently according to predefined criteria. This self-service capability ensures that simulation accuracy is maintained while consistency is improved, as the same decision-making logic is applied uniformly across all simulations regardless of which user initiates them.
Solution Approach 2:
The digital agent incorporates feedback mechanisms that monitor simulation progress and adjust operations in real-time based on system state and performance metrics. This closed-loop control ensures that simulations are executed with optimal parameters and that any deviations from expected outcomes are corrected automatically, thereby maintaining both accuracy and consistency across multiple simulation runs with different users.
3Adaptability or versatility
If production simulators have minimal automation between interface and digital twin assets, then system flexibility and customization are improved, but operational efficiency and time consumption deteriorate
Solution Approach 1:
The digital agent performs preliminary actions by pre-configuring simulation assets and pre-processing input data before the actual simulation execution. The agent automatically identifies relevant digital twin assets, pre-loads necessary models and parameters, and prepares the simulation environment in advance. This preliminary preparation reduces the time required during actual simulation execution while maintaining the flexibility to handle different simulation scenarios and asset configurations.
Solution Approach 2:
The digital agent replaces manual mechanical operations with automated digital processes. Instead of operators manually navigating through the interface and configuring assets, the agent uses automated algorithms to perform these tasks. This substitution maintains system flexibility through programmable logic while dramatically improving productivity by eliminating manual configuration time and enabling parallel processing of simulation setup and execution.
Data Source
AI summary
Methods and systems are configured for executing a production simulation by receiving, through a client device, an instruction for executing a portion of a production simulation; responsive to receiving the instruction, accessing one or more digital agents, the one or more digital agents configured and trained with training data representing actions for operating one or more production simulations, the training data associating one or more simulation assets with respective data signatures each representing a scenario for executing a given simulation asset within the production simulation, wherein at least one of the one or more simulation assets includes the portion of the production simulation; accessing, by the one or more digital agents, the simulation asset including the portion of the production simulation of the instruction; executing the portion of the production simulation to generate output data; and providing the output data to the client device.