Advisor Model Routing for Timely Oilfield Equipment Analysis
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Solution Overview
Problem
Existing industrial devices, such as RTUs, are limited in their data processing capabilities and analysis efficiency, particularly in oil-and-gas facilities, leading to suboptimal operation and limited data storage and timely analysis.
Innovation Solution
Implementing a system with edge devices and a computing platform that includes machine learning models to fine-tune advisor models, sort user queries, and generate actionable responses for improving the operation of oil-and-gas facilities, utilizing data from various equipment and enhancing user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing industrial devices like RTUs are used for data processing, then device simplicity is maintained, but data processing capabilities and analysis efficiency are limited
Solution Approach 1:
The system divides data processing functions into two segments: edge devices (including RTUs) handle local data collection and preliminary processing, while a separate cloud-based computing platform performs advanced analysis and generates actionable responses. This segmentation allows each component to be optimized independently, improving overall productivity without excessively increasing complexity at the edge device level.
Solution Approach 2:
The patent introduces a cloud-based computing platform as an intermediary between edge devices and users. This intermediary handles complex data processing and analysis tasks, enabling edge devices to maintain simplicity while the system as a whole achieves high productivity through the intermediary's advanced capabilities.
2Measurement precision
If more data is collected from oil-and-gas equipment, then analysis accuracy improves, but data storage and timely analysis become more difficult
Solution Approach 1:
The system performs preliminary data processing and filtering at the edge device level before data is transmitted to the cloud platform. This preliminary action reduces the volume of data that needs to be stored and analyzed centrally, enabling timely analysis of large datasets without overwhelming storage or processing resources.
Solution Approach 2:
The patent implements continuous data streaming and processing pipelines that maintain constant analysis of incoming data from oil-and-gas equipment. This continuous action ensures that insights are generated in real-time rather than through batch processing, maintaining both high analysis accuracy and timeliness.
3Measurement precision
If multiple advisor models are used to handle different query types, then query resolution accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the advisor functionality into multiple specialized models, each trained to handle specific types of queries or tasks. This segmentation allows each model to achieve high accuracy in its domain while the overall system manages complexity through modular architecture and automated model selection based on query type.
Data Source
AI summary
A system includes an edge device and a computing platform. The computing platform includes one or more processors and one or more non-transitory computer-readable media storing program instructions that cause the one or more processors to perform operations including fine-tuning, a plurality of advisor models to perform different types of advisor operations for an oil-and-gas facility using data relating to oil-and-gas equipment, sorting, a user query to a first advisor model by selecting, by at least one machine learning model, the first advisor model from the plurality of advisor models based on a content of the user query, generating, by the first advisor model, an actionable response to the user query, and causing the oil-and-gas facility to operate in accordance with the actionable response.