Hybrid AI Control System for Resource Extraction Operations
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Solution Overview
Problem
Existing resource extraction operations face challenges in efficiently adjusting production processes in real-time due to limitations in data collection and analysis, leading to inefficiencies and potential downtime.
Innovation Solution
The implementation of a hybrid artificial intelligence (AI) system that integrates machine learning models and workflow systems to collect and analyze data from various assets within the resource extraction site, enabling autonomous adjustments to operational parameters and improving production efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data collection and analysis methods are used in resource extraction operations, then system complexity remains manageable, but real-time production adjustment capability is insufficient leading to inefficiencies and downtime
Solution Approach 1:
The system segments data collection and analysis functions across multiple distributed devices including sensors, edge computing devices, and central control systems. Each device handles specific data processing tasks locally before transmitting results, enabling real-time production adjustments without requiring a monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary layer of edge computing devices and communication networks that bridge the gap between traditional data collection methods and real-time production control. These intermediaries process and filter data locally, reducing the complexity burden on the central system while enabling timely production adjustments.
2Productivity
If manual monitoring and adjustment of production operations is performed, then system simplicity is maintained, but operational efficiency is reduced and downtime increases
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor production parameters in real-time, the system analyzes this data using machine learning models, and automatically adjusts operational parameters based on the analysis. This automated feedback mechanism significantly improves operational efficiency while managing automation complexity through modular architecture.
Solution Approach 2:
The production system performs self-adjustment through automated control mechanisms that respond to real-time data without requiring constant manual intervention. The system autonomously optimizes production parameters, schedules maintenance, and adjusts operations based on detected conditions, thereby improving operational efficiency while reducing the need for manual monitoring.
3Measurement precision
If comprehensive data collection from all production devices is implemented, then production optimization capability is improved, but data processing time and system complexity increase
Solution Approach 1:
The system performs preliminary data processing and filtering at the edge devices before data reaches the central analysis system. Sensors and local processors pre-process raw data, extracting key features and filtering out redundant information in advance, which reduces the time required for comprehensive data analysis while maintaining high monitoring accuracy.
Solution Approach 2:
The patent implements a hierarchical data collection strategy where not all devices transmit all data simultaneously. Instead, the system collects data from critical devices with higher frequency and from less critical devices with lower frequency, balancing monitoring accuracy with processing time requirements through selective and prioritized data acquisition.
Data Source
AI summary
A method may include receiving a plurality of datasets from devices in a resource extraction site. The method may also involve identifying workflow systems associated with one or more operations of the devices. The method may also involve determining updated operational parameters for the devices based on the workflow systems and the plurality of datasets and generating one or more commands for implementing the one or more updated operational parameters for the one or more devices. The method may then include sending the commands to the devices, wherein the devices are configured to adjust the operations based on the updated operational parameters.