Stuck Pipe Detection Using Moving Average and DBSCAN Clustering
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Solution Overview
Problem
Detecting when drill pipe becomes stuck in a borehole is challenging, leading to potential damage if drilling operations continue, as existing methods lack effective real-time monitoring and detection capabilities.
Innovation Solution
A system utilizing a hookload sensor and bit depth sensor to compute moving averages and apply DBSCAN clustering to identify tight spots and fully-stuck events, preventing further damage by alerting operators before excessive force is applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time monitoring and detection capabilities are implemented, then stuck pipe detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the detection task into multiple components: hookload sensor for force measurement, bit depth sensor for position measurement, moving average calculator for noise reduction, and DBSCAN clustering algorithm for pattern recognition. Each component handles a specific aspect of the detection problem, improving overall accuracy while keeping individual components relatively simple.
Solution Approach 2:
The patent introduces intermediate processing steps between raw sensor data and final detection results. Moving averages serve as an intermediary to filter noise from sensor readings, and DBSCAN clustering acts as an intermediary to identify patterns in the processed data before declaring a stuck pipe event. This multi-layer intermediary approach improves detection reliability.
2Reliability
If continuous monitoring is performed to detect stuck pipe early, then equipment damage is prevented, but energy consumption increases
Solution Approach 1:
The system performs monitoring periodically rather than continuously, calculating moving averages at regular intervals and comparing them against thresholds. This periodic approach maintains equipment protection capabilities while significantly reducing energy consumption compared to continuous monitoring at high frequency.
Solution Approach 2:
The system uses existing operational data (hookload and bit depth measurements that are already taken during normal drilling operations) to detect stuck pipe conditions. By leveraging data that is already being collected for other purposes, the system provides additional protection functionality without requiring separate dedicated sensors or excessive additional energy consumption.
Data Source
AI summary
Tight spots in movements of a drill string in an oil well are identified by comparing a large interval hookload moving average to a short interval hookload moving average, comparing a large interval bit depth moving average to a short interval bit depth moving average, and DBSCANing the tight spots to identify a fully-stuck event.