Well Facility Data Calibration for ML Training
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Solution Overview
Problem
Well systems face challenges in analyzing and calibrating data from various equipment due to different formats, locations, and time references, leading to noise in machine learning operations and reduced accuracy.
Innovation Solution
A system that uses event records to identify events and calibrate data by mapping equipment operations into a supervisory control and data acquisition (SCADA) system, aligning measurements into time series data, and combining online and offline data for machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple equipment sources are collected for machine learning training, then the quantity and diversity of training data increases, but data consistency and noise increase due to different formats, locations, and time references
Solution Approach 1:
The system segments data processing into distinct stages: data collection from multiple equipment sources, calibration processing, and machine learning training. By dividing the complex data integration task into manageable segments, the system can apply specific calibration techniques to each data type while maintaining overall data consistency.
Solution Approach 2:
The patent introduces a calibration system as an intermediary layer between raw equipment data and machine learning models. This calibration component standardizes data from various sources by adjusting for format differences, location variations, and time reference discrepancies, thereby maintaining data consistency while incorporating diverse training data.
2Measurement precision
If data calibration and alignment processes are implemented, then data consistency and machine learning accuracy improve, but system complexity and processing time increase
Solution Approach 1:
The calibration system is designed as a universal platform that handles multiple data types and equipment sources through a single integrated process. Rather than creating separate calibration routines for each equipment type, the system implements a multi-functional calibration framework that standardizes diverse data sources, thereby reducing overall system complexity.
Solution Approach 2:
The system performs calibration as a preliminary action before machine learning training begins. By pre-calibrating and aligning all data during an initial processing stage, the system eliminates the need for complex real-time calibration during training, thereby reducing processing complexity and improving training efficiency.
3Adaptability or versatility
If offline equipment data is integrated with online equipment data, then the comprehensiveness of training data improves, but data synchronization and calibration difficulty increase
Solution Approach 1:
The system creates standardized copies of offline equipment data that match the format and structure of online equipment data. By copying and transforming offline data into a unified format during the calibration stage, the system achieves comprehensive data integration without the complexity of real-time synchronization, as all data is processed and aligned before training begins.
Data Source
AI summary
Disclosed are systems, apparatuses, methods, and computer readable medium for run data based on operation of a facility. A method includes: analyzing tickets identifying modification of a plurality of equipment installed and configured at the facility, wherein a ticket identifies at least one of an installation of a first equipment, removal of the first equipment, or testing of the first equipment; generating a plurality of runs based on the tickets, wherein each run of the plurality of runs identifies an equipment configuration, a start time, and an end time; and mapping measurement data from the equipment based on the equipment configuration of the run into time series data.