Wellsite Pattern Detection for Drilling Equipment Irregularities
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Solution Overview
Problem
Current wellsite operations lack effective methods for determining potential irregularities in equipment performance, particularly in blowout preventers, leading to inefficiencies and potential failures during oilfield operations.
Innovation Solution
A pattern detection system that utilizes sensors to collect signals from wellsite equipment, builds a library of defined patterns, and employs pattern detectors to characterize, detect, and classify equipment signals, enabling predictive and preventative maintenance, as well as real-time monitoring and adjustment of operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are positioned about the wellsite to measure equipment performance, then equipment monitoring capability is improved, but the ability to determine potential irregularities remains insufficient
Solution Approach 1:
The system continuously monitors equipment signals and compares them against defined patterns, providing feedback when irregularities are detected. This closed-loop approach transforms basic measurement into actionable intelligence for predicting equipment failures.
Solution Approach 2:
A pattern detector acts as an intermediary between raw sensor measurements and irregularity detection. It characterizes, detects, and classifies equipment signals by comparing them against a library of defined patterns, bridging the gap between measurement and reliability assessment.
2Reliability
If maintenance is performed frequently to prevent equipment failure, then equipment reliability is improved, but operational downtime and maintenance costs increase
Solution Approach 1:
The system performs preliminary detection of equipment irregularities by comparing current signals against defined patterns of normal and abnormal operation. This allows maintenance to be scheduled based on actual equipment condition rather than fixed intervals, enabling proactive rather than reactive maintenance planning.
Solution Approach 2:
The equipment effectively monitors its own condition through the pattern detection system, which continuously assesses equipment signals and identifies irregularities. This self-diagnosis capability allows the system to signal when maintenance is needed, replacing the need for frequent scheduled maintenance with condition-based maintenance.
3Reliability
If comprehensive equipment monitoring is implemented, then failure prediction capability is improved, but system complexity increases
Solution Approach 1:
The pattern detector serves as an intermediary that simplifies the complexity of comprehensive monitoring by providing structured characterization, detection, and classification of equipment signals. It transforms raw sensor data into classified patterns that are easier to interpret and act upon.
Solution Approach 2:
The monitoring system is segmented into distinct functional modules: signal characterization, pattern detection, and pattern classification. This modular approach breaks down the complexity of comprehensive monitoring into manageable components, each handling a specific aspect of the analysis.
Data Source
AI summary
A method and system for drilling a wellbore. A method for drilling a wellbore includes: generating a library of defined patterns, each of the defined patterns corresponding to an operational behavior of drilling equipment used to drill the wellbore; collecting, during operation of the drilling equipment, measurements from sensors coupled to the drilling equipment; characterizing the measurements as correlating to one of the defined patterns; detecting the one of the defined patterns in the measurements; extracting a feature from the measurements; determining a condition of the drilling equipment by comparing a value of the feature to a threshold derived from the one of the defined patterns; and activating an actuator coupled to the drilling equipment to adjust operation of the drilling equipment based on the condition.