Edge Analytics for Autonomous Well Pump Failure Prediction
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Solution Overview
Problem
Oil and gas wells often operate unattended in remote areas, leading to costly and time-consuming maintenance when issues arise, resulting in lost productivity, profitability, and safety risks for field personnel due to the need for physical inspections.
Innovation Solution
Deployment of machine learning-based analytics on edge devices at well sites for real-time monitoring and control, enabling the detection of unusual events and automatic response to prevent damage, reduce downtime, and minimize health and safety risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If field personnel physically inspect equipment at remote well sites, then equipment problems can be detected and repaired, but productivity is lost and safety risks increase due to travel time and exposure
Solution Approach 1:
The monitoring system enables self-service by automatically detecting equipment anomalies and generating maintenance alerts without requiring field personnel presence. The system monitors pump performance, detects failures, and notifies operators, allowing the equipment to essentially monitor itself and trigger maintenance needs autonomously.
Solution Approach 2:
Physical inspection by field personnel is replaced with electronic monitoring systems including sensors, data acquisition devices, and communication systems. The mechanical act of traveling to and physically examining equipment is substituted with remote electronic detection and analysis of equipment status.
2Reliability
If field personnel travel to remote well sites for physical inspection, then equipment issues can be addressed, but costs increase due to travel time and resource allocation
Solution Approach 1:
The system performs self-monitoring and self-diagnosis, eliminating the need for costly field trips. Equipment automatically tracks its own performance parameters and triggers maintenance alerts only when needed, converting reactive maintenance into condition-based maintenance that reduces unnecessary travel and associated costs.
Solution Approach 2:
The system performs preliminary detection and assessment of equipment issues before they become critical failures. By monitoring trends and predicting potential problems, the system enables planned maintenance during optimal times, avoiding emergency repairs that would require immediate field personnel deployment and incur higher costs.
3Productivity
If real-time monitoring and automatic response systems are deployed, then downtime and costs are decreased, but device complexity increases
Solution Approach 1:
The monitoring system is segmented into modular functional components: sensors for data collection, data acquisition devices for processing, communication modules for transmission, and control systems for actuation. This segmentation allows independent optimization of each component and simplifies deployment and maintenance while achieving comprehensive monitoring capabilities.
Data Source
AI summary
Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods use machine learning (ML) based analytics on an edge device directly at the well site to detect possible occurrence of abnormal events and automatically respond to such events. The event detection may be based on trends identified in the data acquired from the well site operations in real time. The trends may be identified by correlation and by fitting line segments to the data and analyzing the slopes of the line segments. Upon detecting an unusual event, the edge device can issue alerts regarding the event and take predefined steps to reduce potential damage resulting from such event. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.