Edge ML Analytics for Remote Well Operation Anomaly Detection
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Solution Overview
Problem
Oil and gas wells often operate unattended in remote areas, leading to costly and time-consuming maintenance due to unforeseen operational issues, which can be dangerous for field personnel and result in productivity loss.
Innovation Solution
Implementing machine learning (ML) based analytics on an edge device at the well site to detect abnormal operations, issue alerts, and take predefined actions, such as adjusting motor speed or shutting off power, to prevent damage and anticipate failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If field personnel physically inspect equipment at remote well sites, then operational issues can be detected and repaired, but this results in costly and time-consuming travel and exposes personnel to safety risks
Solution Approach 1:
The system enables self-service monitoring where the well site equipment automatically detects and reports its own operational status through sensors and edge computing devices, eliminating the need for field personnel to travel for routine inspections. The edge device performs local data analysis and generates alerts when anomalies are detected, allowing the system to monitor itself continuously.
Solution Approach 2:
The patent replaces the mechanical system of physical inspection by field personnel with an electronic monitoring system using sensors, edge computing devices, and communication networks. The edge device processes sensor data locally and transmits alerts remotely, substituting human physical presence with automated electronic detection and communication systems.
2Reliability
If field personnel travel to remote well sites for maintenance, then equipment issues can be addressed, but this increases operational costs and reduces productivity
Solution Approach 1:
The system performs preliminary detection and alerting of potential equipment failures before they occur. The edge device continuously analyzes sensor data and generates early warnings, allowing maintenance to be scheduled proactively rather than requiring immediate field personnel deployment. This preliminary action prevents productivity loss by enabling planned maintenance during non-critical periods.
Solution Approach 2:
The monitoring system establishes continuous feedback loops where sensor data is collected, analyzed by the edge device, and used to generate alerts that trigger appropriate maintenance actions. This closed-loop feedback system ensures equipment issues are detected and addressed systematically, maintaining productivity by preventing unexpected failures while optimizing maintenance scheduling.
3Measurement precision
If real-time monitoring is implemented at well sites, then abnormal operations can be detected immediately, but this requires deployment of edge computing devices and increases system complexity
Solution Approach 1:
The monitoring system is segmented into distributed edge computing devices deployed at individual well sites rather than a centralized system. Each edge device independently processes sensor data locally, enabling precise real-time detection at the source while distributing computational complexity across multiple independent units rather than concentrating it in one complex centralized system.
Data Source
AI summary
Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.