Tubular Running Frequency Analysis for Thread Defect Detection
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Solution Overview
Problem
Existing tubular running operations in subterranean wells face challenges in efficiently and safely making and breaking threaded connections due to the inability to quickly identify defects or anomalies, which can lead to inefficiencies and safety risks.
Innovation Solution
Implementing a system with sensors and a frequency spectrum analysis module to automatically detect defects or anomalies in real time during tubular make-up and break-out processes by analyzing torque, rotation, acceleration, and orientation data, using techniques like FFT, STFT, and DWT to identify cyclical disturbances and vibration patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual inspection methods are used for threaded connections, then operational simplicity is maintained, but defect detection capability deteriorates
Solution Approach 1:
The patent replaces manual visual inspection and mechanical measurement methods with an automated sensor-based measurement system. Sensors mounted on tubular running equipment capture data during make-up and break-out operations, eliminating the need for manual inspection while providing continuous, objective defect detection through electronic measurement of torque, rotation, acceleration, and orientation parameters.
Solution Approach 2:
The patent introduces sensors as intermediary devices between the tubular running equipment and the operator. These sensors act as mediators that capture physical parameters during operations and transmit this information to the control system for analysis, enabling indirect observation of connection quality without direct manual intervention in the critical make-up and break-out processes.
2Speed
If real-time frequency spectrum analysis is implemented, then defect identification speed is improved, but computational complexity increases
Solution Approach 1:
The patent performs frequency spectrum analysis in real-time during tubular running operations, converting sensor data to the frequency domain and identifying cyclical disturbances and vibration patterns as they occur. This preliminary action enables immediate defect identification during make-up and break-out processes, allowing operators to take corrective action before defects escalate into failures, rather than waiting for post-operation inspection.
Solution Approach 2:
The patent implements a feedback loop where sensor data is continuously analyzed through frequency spectrum analysis, and the results are immediately fed back to control the tubular running equipment. This real-time feedback mechanism enables dynamic adjustment of operational parameters based on detected anomalies, creating a closed-loop control system that actively prevents defects rather than merely detecting them.
3Reliability
If continuous monitoring during make-up and break-out operations is performed, then operational safety is improved, but time consumption increases
Solution Approach 1:
The patent implements continuous monitoring of tubular running operations through sensors that capture data throughout the entire make-up and break-out processes. This continuous measurement of torque, rotation, acceleration, and orientation parameters ensures that no critical events are missed, providing comprehensive safety coverage during the most vulnerable phases of tubular operations without requiring intermittent stopping or manual intervention.
Solution Approach 2:
The patent replaces time-consuming manual inspection procedures with automated sensor-based monitoring that operates continuously during tubular running operations. This substitution eliminates the need for operators to manually stop operations for inspection, as the electronic measurement and real-time analysis system continuously monitors for defects, actually reducing total operation time while improving safety.
Data Source
AI summary
A method can include obtaining sensor data during a tubular running operation, converting the sensor data to frequency domain, identifying at least one frequency pattern in the sensor data, and comparing the identified at least one frequency pattern to a database of known frequency patterns. An apparatus can include at least one sensor configured to output sensor data in a tubular running operation, and a control system comprising a frequency spectrum analysis module configured to convert the sensor data to frequency domain.