Sensor Frequency Cross-Check for Drilling Fault Detection
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Solution Overview
Problem
Existing sensors in oil/gas drilling lack self-diagnostic capabilities, leading to potential undetected failures that can cause data integrity loss and operational issues.
Innovation Solution
A method that analyzes independent measurements from two different types of sensors, such as standpipe pressure and pump stroke sensors, to verify their functionality by comparing derived fundamental frequencies, alerting on mismatches to identify malfunctioning sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-diagnostics are added to every sensor, then sensor failure detection capability is improved, but system cost increases significantly
Solution Approach 1:
The system enables sensors to self-diagnose by automatically comparing their measurements against predictions from a process model. Each sensor's data is independently validated without requiring manual intervention or additional diagnostic hardware, allowing the sensor network to monitor itself and detect failures autonomously.
Solution Approach 2:
A process model acts as an intermediary between sensors, receiving measurements from multiple sensors and generating expected values for comparison. This mediator enables cross-validation of sensor data without requiring direct communication or complex integration between sensor pairs, simplifying the overall system architecture while maintaining detection capability.
2Loss of information
If sensor failure detection is implemented, then data integrity is improved, but processing time and computational resources increase
Solution Approach 1:
The process model continuously generates expected sensor values in advance based on process conditions and relationships, so that when actual sensor measurements arrive, validation can occur immediately through simple comparison. This preliminary computation of expected values eliminates the need for complex real-time analysis during the validation phase.
Solution Approach 2:
The system transforms the validation problem from comparing multiple sensor readings against each other into comparing each sensor reading against a predicted parameter value derived from process conditions. This parameter transformation simplifies the computational complexity from multi-variable analysis to single-parameter comparison, reducing processing time while maintaining detection accuracy.
Data Source
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AI summary
Methods, computing systems, and computer-readable media for detecting sensor malfunctioning status. The method includes receiving sensor measurements of a first type from a first type of sensor, deriving a first value for a metric from the sensor measurements of the first type, receiving sensor measurements of a second type from a second type of sensor, wherein the second measurements of the second type include a second value for the metric, comparing the first value to the second value, determining whether the first type of sensor or the second type of sensor is malfunctioning based on the comparing, and storing or outputting information indicating whether first type of sensor and the second type of sensor are malfunctioning.