Wellsite Event Detection With Adaptive ML Retraining
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Solution Overview
Problem
Existing wellsite equipment monitoring systems face challenges in accurately detecting events related to equipment operation due to suboptimal training data sets, leading to increased costs and potential equipment failures, as each well's environment is unique and changes over time.
Innovation Solution
An electrical submersible pump system equipped with sensors and a machine learning model that analyzes measurements to identify events, with a monitoring system that generates a modified training data set based on local sensor data to retrain the model, improving event detection accuracy without disrupting ongoing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained with initial training data set to identify events, then event detection capability is established, but detection accuracy deteriorates over time due to changing well environment and suboptimal training data
Solution Approach 1:
The system implements dynamic retraining of the machine learning model by continuously collecting new sensor measurements and identified events, generating modified training data sets, and retraining the model to adapt to changing well environments. This transforms the static model into a dynamic system that evolves with operational conditions.
Solution Approach 2:
The monitoring system receives measurements and identified events from the event detection system, analyzes them to determine if training modification is needed, generates modified training data sets, and applies them to retrain the model. This closed-loop feedback mechanism ensures continuous improvement of detection accuracy.
2Measurement precision
If machine learning model is retrained with modified training data set, then event detection accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
The monitoring system automatically performs the retraining process by collecting measurements and events, determining training modification needs, generating modified training data sets, and applying them to retrain the model without requiring external intervention. This self-service approach manages complexity internally while maintaining simplicity for end users.
Solution Approach 2:
The system generates and applies modified training data sets in advance to retrain the model before accuracy degradation becomes critical. By proactively updating the model with preliminary actions, the system prevents performance deterioration rather than reacting to failures.
3Adaptability or versatility
If continuous monitoring and retraining is performed, then model adapts to specific well environment, but processing time and operational disruption increase
Solution Approach 1:
The system performs retraining operations periodically based on accumulated measurements and events rather than continuously. The monitoring system collects data over time, determines when training modification is needed, and applies updates at optimal intervals, balancing adaptation with operational efficiency.
Solution Approach 2:
The event detection system continues to operate and identify events continuously while the monitoring system processes and applies training updates in the background. This ensures uninterrupted event detection functionality while maintaining model adaptability through continuous useful action.
Data Source
AI summary
A method for detecting events includes receiving measurements from a plurality of sensors, and executing a machine learning model trained to identify events based on the measurements. The machine learning model identifies the events based on the measurements. The method also includes determining based on the measurements and the identified events that training applied to the machine learning model is to be modified. A modified training data set is generated based on the measurements and an initial training data set used to train the machine learning model to identify the events. The modified training data set is applied to retrain the machine learning model.