Power-loss signal model for drilling dysfunction detection
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Solution Overview
Problem
Current drilling operations face challenges in rapidly detecting and mitigating downhole drilling dysfunctions due to non-synchronized timing information from multiple sensors, which complicates data aggregation and processing, leading to potential tool failures and operational inefficiencies.
Innovation Solution
A method for predicting real-time drilling dysfunctions by modeling the wellbore environment using a power-loss model that measures signal energy decay along the drill string, characterized by three parameters (alpha, beta, and gamma), allowing for the detection and mitigation of dysfunctions using surface-acquired data without the need for mid-string dynamic subs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sensors are aggregated using各自独立的时钟 timing information, then data collection from multiple sources is enabled, but timing synchronization accuracy deteriorates due to clock drift and environmental factors
Solution Approach 1:
The patent introduces a reference clock signal as an intermediary that all sensors synchronize to. Instead of each sensor using its own independent clock, a centralized reference clock serves as the master timing source, and all sensor data is time-stamped relative to this common reference, eliminating timing drift between sensors
Solution Approach 2:
The system implements feedback mechanisms where timing information from the reference clock is continuously monitored and used to adjust and correct timing drift in sensor data. The processing system compares actual timing deviations against the reference and applies corrections to maintain synchronization accuracy
2Reliability
If traditional sensor placement with mid-string dynamic subs is used, then downhole drilling dysfunction detection capability is improved, but device complexity and operational costs increase
Solution Approach 1:
The patent extracts the critical sensing function from the complex mid-string dynamic sub assembly and relocates it to the surface. By using surface-acquired data combined with power-loss modeling, the system eliminates the need for complex downhole sensing equipment while maintaining dysfunction detection capability
Solution Approach 2:
The patent replaces the mechanical sensing system (mid-string dynamic subs with physical sensors) with a computational modeling approach. Instead of mechanically measuring conditions downhole, the system uses power-loss models and surface data to infer downhole conditions, substituting physical measurement with mathematical modeling
3Productivity
If rapid dysfunction detection is implemented through complex real-time analysis, then dysfunction identification speed is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing power-loss models and baseline conditions before drilling operations begin. These pre-computed models and reference data are ready for rapid comparison with actual surface-acquired data during drilling, enabling quick dysfunction detection without complex real-time calculations
Solution Approach 2:
The system monitors changes in power-loss parameters derived from surface-acquired data. By tracking deviations in these specific parameters from expected values based on the power-loss model, the system can rapidly identify dysfunctions through simple parameter comparison rather than complex multi-parameter analysis
Data Source
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AI summary
The invention relates to a method, system and apparatus for determining real-time drilling operations dysfunctions by measuring the power-loss of signal propagation associated with a drill string in a wellbore. The invention comprises acquiring a first time series from a mid-string drilling sub sensor associated with a drill string in a wellbore and acquiring a second time series from a sensor associated with the drill string wherein the sensor is on or near a drill rig on the surface of the earth. The process further comprises determining the geometry of the wellbore and determining model parameters alpha and beta for characterizing a wellbore using the first time series, the second time series and the geometry of the wellbore by deriving a power loss of signal propagation. The model parameters may then be used for drilling a subsequent well using surface sensor acquired data to detect drilling dysfunctions.