Anomaly Detection in Well Equipment Operation Using Machine Learning Methods
September 2025
To improve equipment reliability and reduce the number of unplanned failures, the team developed an intelligent monitoring algorithm designed for early detection of anomalies in the operation of well pumps.
The system is based on the analysis of seven key performance indicators received in real time from telemetry sensors installed on the equipment. The algorithm automatically detects deviations both in individual parameters and in their combined behavior.
The model integrates multiple approaches: Isolation Forest for outlier detection, statistical methods, and time series forecasting with Prophet. This allows for accurate assessment of current equipment conditions and forecasting of potential future issues.
When a potential malfunction is detected, the system immediately sends a notification to the operator via the nv.EBS software, enabling prompt response.
The main goal of the algorithm is to prevent serious failures through early detection of abnormal operating modes. This significantly reduces equipment breakdowns, lowers recovery costs, and improves the overall reliability of the production process.
