The accuracy of the self-diagnosis function of quartz crystal pressure sensors is not a fixed value, but is jointly influenced by five core factors: operating conditions, hardware design, algorithm optimization, fault types, and calibration & maintenance. Each factor indirectly determines the diagnostic accuracy by interfering with the sensor's signal acquisition and fault identification logic. A detailed analysis combined with the petroleum logging scenario is as follows:
I. Complexity of Extreme Operating Conditions
The high temperature, high pressure, strong vibration, and high corrosion characteristics of petroleum logging are the primary external factors affecting diagnostic accuracy:
Temperature Fluctuations: When the downhole temperature exceeds the conventional compensation range (＞200℃) or rises/falls sharply (e.g., switching of drilling fluid circulation), it will slightly alter the resonant characteristics of the quartz crystal. If the temperature compensation algorithm fails to adapt in a timely manner, normal temperature drift may be misjudged as abnormal frequency drift, resulting in a 1–2% decrease in accuracy.
Vibration and Shock: Severe vibration (＞20g RMS) or drilling tool impact (＞200g) during logging while drilling (LWD) may interfere with the stability of the frequency difference of the dual-resonant beam. If the threshold of the vibration filtering algorithm is improperly set, it is easy to confuse "structural loosening faults" with "normal vibration responses".
Corrosion and Seal Failure: In high-sulfur formations, sulfides corrode the sensor seals or wetted materials, gradually damaging the hardware structure. When the initial fault characteristics are not obvious, missed detections may occur. Accurate identification can only be achieved until seal failure leads to drilling fluid intrusion and the fault characteristics become prominent.
II. Redundancy and Adaptability of Sensor Hardware Design
Hardware serves as the foundation of the self-diagnosis function, and the rationality of its design directly determines the sensitivity of fault identification:
Accuracy of Dual-Resonant Beam Structure: The precision of the preset threshold for the frequency difference of the dual beams affects the core fault identification accuracy. If the machining error of the beam exceeds 0.001% FS, it may lead to missed detections of slight abnormalities in the initial stage of crystal performance degradation.
Circuit and Sealing Design: The redundant protection of the power supply circuit and the integrity of the all-metal laser-welded seal determine whether "false faults" are misreported due to circuit failures or seal damage. For example, if the loosening of the circuit interface is not detected by the hardware monitoring module, signal jitter may be misjudged as a circuit fault.
Explosion-Proof and Anti-Interference Design: In areas with dense electromagnetic interference at the wellhead and downhole, insufficient design of the sensor's anti-interference circuit will cause distortion in the transmission of self-diagnosis data, which indirectly affects the accuracy of the surface system's judgment on fault types.
III. Optimization and Operating Condition Adaptability of Intelligent Algorithms
Algorithms are the core of distinguishing between "faults" and "operating condition fluctuations", and their optimization level determines diagnostic accuracy:
Filtering and Baseline Comparison Algorithms: If high-frequency vibration filtering algorithms are not optimized for petroleum logging scenarios, they cannot effectively filter out electromagnetic interference from drilling equipment and formation electrical interference, which will lead to misjudgment of abnormal signals. The completeness and update frequency of historical data baselines affect the identification accuracy of minor drifts; outdated baselines tend to misjudge normal aging drifts as faults.
Dynamic Threshold Adjustment Capability: There are significant differences in operating conditions across different logging scenarios (e.g., logging while drilling, pressure buildup testing). If algorithms cannot dynamically adjust fault thresholds, the problem of "inconsistent diagnostic results for the same anomaly under different scenarios" will occur. For example, minor pressure fluctuations that are normal in logging while drilling scenarios may be identified as anomalies by static thresholds.
IV. Distinctiveness of Fault Type Characteristics
Differences in the characteristics of various faults result in a gradient distribution of diagnostic accuracy:
Core Faults (crystal failure, seal damage, circuit burnout): The fault characteristics are prominent (abrupt changes in the frequency difference of dual beams, circuit parameter overrun). Hardware and algorithms can quickly identify these faults, with an accuracy rate generally ≥ 99.5%.
Minor Anomalies (minor frequency drift, poor circuit contact, temperature compensation deviation): The fault characteristics are vague and tend to overlap with operating condition fluctuations. The identification accuracy is affected by algorithm sensitivity, generally ranging between 98–99%, and the false alarm rate is mainly attributed to such faults.
V. Calibration, Maintenance and Historical Data Accumulation
Routine calibration and data management affect the judgment baseline of the self-diagnosis algorithm:
Calibration Frequency and Accuracy: Failure to perform calibrations in accordance with standards (e.g., once a month) will cause the self-diagnosis thresholds to deviate from actual operating conditions, leading to fault misjudgment or missed detection. Especially under high-temperature and high-pressure conditions, an excessively long calibration cycle will accelerate threshold deviation.
Historical Data Accumulation: The adequacy of historical fault data from different reservoirs and logging conditions affects the deep learning and optimization of the algorithm. When there is no historical data for reference in new work areas, the initial diagnostic accuracy may be 1–2% lower than that in mature work areas, and it needs to be gradually improved through data accumulation from multiple well logging operations.