Remote Field Equipment Characterization Using Latent Sensor Models
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Solution Overview
Problem
Field equipment deployed in remote locations faces challenges in characterization and management due to its distance from urban infrastructure, impacting operations such as drilling, fracturing, and fluid processing.
Innovation Solution
A system utilizing a time series foundation model to generate latent space representations of sensor data for characterizing the operation of field equipment, enabling improved management and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If field equipment is deployed in remote locations, then operational reach and coverage are improved, but characterization and management difficulty increase
Solution Approach 1:
The patent introduces latent space representations as an intermediary between raw sensor data and equipment characterization. These representations serve as a mediator that transforms complex, high-dimensional sensor data into a compressed, informative space that captures essential equipment states and operational characteristics, making remote equipment analysis feasible despite distance from urban infrastructure
Solution Approach 2:
The patent replaces traditional physical inspection and manual characterization methods with data-driven machine learning models. By substituting mechanical/physical assessment with computational analysis of sensor data through latent space transformations, the system achieves accurate equipment characterization without requiring physical proximity or manual intervention
2Measurement precision
If traditional sensor data analysis is used, then system complexity is reduced, but measurement precision and characterization accuracy deteriorate
Solution Approach 1:
The patent transforms the parameter space of sensor data by mapping high-dimensional raw measurements into a lower-dimensional latent space. This parameter transformation preserves critical information while reducing dimensionality, enabling accurate characterization without processing the full complexity of original sensor data streams
Solution Approach 2:
The patent extracts essential features and patterns from complex sensor data by projecting them into latent space representations. This extraction process separates critical diagnostic information from noise and redundancy, achieving high measurement precision by focusing computational resources on the most informative data dimensions
Data Source
AI summary
A method can include receiving sensor data from one or more sensors in a field system that includes field equipment; generating latent space representations of the sensor data utilizing a time series foundation model; and, based at least in part on a portion of the latent space representations, characterizing operation of one or more pieces of the field equipment.