Self-Tuning Sonic Transmitter for Acoustic Resonance
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Solution Overview
Problem
In acoustic well logging, the variation in formation resonance frequency leads to inferior quality logged data, as the frequency used for acoustic signals often deviates from the optimal resonance frequency, affecting the accuracy of properties like bulk modulus, porosity, and pore pressure measurements.
Innovation Solution
A system and method that identify and utilize the optimal firing frequency for acoustic resonance by transmitting multiple frequencies and analyzing the formation's response, adjusting the frequency used for logging to match the best resonance frequency, which is re-evaluated periodically to ensure data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed frequency is used for acoustic signals, then the device complexity is reduced, but the measurement precision deteriorates due to deviation from optimal resonance frequency
Solution Approach 1:
The system dynamically adjusts the acoustic signal frequency based on real-time formation resonance frequency detection. The frequency is no longer fixed but adapts to match the formation's resonant characteristics, thereby maintaining measurement precision without excessive complexity through automated tuning mechanisms.
Solution Approach 2:
The acoustic signal frequency parameter is changed to match the formation's resonance frequency. By detecting the formation's resonant frequency and adjusting the signal frequency accordingly, the system optimizes measurement accuracy for formation properties while managing complexity through parameter adaptation rather than fixed design.
2Measurement precision
If the frequency is adjusted to match formation resonance, then the measurement precision improves, but the device complexity increases due to frequency tuning requirements
Solution Approach 1:
The system performs self-tuning by automatically detecting the formation's resonance frequency and adjusting its own operating frequency to match. This self-service capability eliminates the need for complex external tuning equipment while maintaining high measurement precision through automated frequency adaptation.
Solution Approach 2:
The system uses feedback from formation response to detect resonance frequency and adjusts the acoustic signal frequency accordingly. This closed-loop feedback mechanism maintains measurement precision while managing complexity through automated control rather than manual or external tuning systems.
3Measurement precision
If multiple frequencies are transmitted to find optimal resonance, then the measurement precision improves, but the productivity decreases due to additional tuning time
Solution Approach 1:
The system performs preliminary frequency tuning and resonance detection before the main measurement process. By establishing the optimal frequency in advance, the system ensures high measurement precision during the actual logging while minimizing the impact on productivity through efficient pre-tuning procedures.
Solution Approach 2:
The system performs frequency tuning periodically rather than continuously, balancing measurement precision with logging speed. By tuning at appropriate intervals during the logging process, the system maintains accuracy without excessive time expenditure on frequency adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and quality of logged data by consistently using the optimal frequency for acoustic measurements, improving the reliability of downhole formation property assessments.
Implementation Method 1
Logged information improves as the frequency used for the acoustic signal is closer to the formation resonance frequency
Data Source
AI summary
A system for self-tuning sonic transmitters which transmits a plurality of frequencies into a downhole formation, then identifies which of the transmitted frequencies generates the best response from the formation. The system then uses the best frequency identified for subsequent logging of formation data until a subsequent tuning sequence is initiated.