A method for monitoring and evaluating injection pressure of a water injection wellhead

By processing data and establishing adaptive benchmark templates for oilfield water injection well pressure monitoring methods, and combining them with collaborative anomaly discrimination algorithms, the problems of misjudgment and missed judgment in oilfield water injection well pressure monitoring under complex operating conditions were solved. This enabled intelligent judgment of pressure fluctuation trends and analysis of anomaly causes, improving the robustness and automation level of the monitoring system.

CN121162261BActive Publication Date: 2026-07-24NANJING WEIYE MACHINERY
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Patent Information

Application Number
CN202511261166.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-05
Publication Date
2026-07-24
Estimated Expiration
2045-09-05

AI Technical Summary

Technical Problem

Existing oilfield water injection well pressure monitoring methods are susceptible to multiple influences under complex operating conditions, leading to frequent misjudgments and omissions. They are difficult to identify true trend changes and transient anomalies, lack the ability to self-evolve the spatiotemporal characteristics of pressure fluctuations among well groups, and lack the ability to explore and attribute the trend consistency and regional anomalies among spatially adjacent wells.

Method used

By acquiring pressure sensor data from the target injection well and adjacent wells, denoising and data normalization are performed. Based on multi-timescale segmented statistics and a self-evolving multi-label clustering model, an adaptive benchmark template is established. Combined with a collaborative anomaly detection algorithm, detection thresholds and parameters are dynamically adjusted to generate detailed anomaly event tracing reports, enabling intelligent judgment of pressure fluctuation trends and anomaly cause analysis.

Benefits of technology

It effectively suppresses single-channel noise interference and parameter drift, improves the robustness and accuracy of pressure trend determination, enhances adaptability to complex working conditions, improves the collaborative verification and causal tracing capabilities of abnormal events, and supports the automation and intelligent upgrading of oilfield wellhead pressure monitoring systems.

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Abstract

The application discloses a kind of injection well mouth injection pressure monitoring evaluation methods, comprising: based on target and adjacent injection well multipoint pressure real-time acquisition, utilize geographic distribution label to realize cross well pressure data coupling, after data missing correction, multistage noise reduction and normalization, extract pressure fluctuation characteristics, and input self-evolution multi-label clustering model automatically induces dominant pressure mode, generates benchmark template that is dynamically updated with working condition;Through real-time pressure trend and benchmark template distribution label comparison, determine pressure fluctuation consistency and implement collaborative anomaly tracing analysis for significant deviation, identify abnormal cause type in combination with historical drift and noise characteristics, automatically optimize detection threshold, generate machine-readable anomaly tracing report and assist operation and maintenance decision;Online sample continuous learning mechanism guarantees the adaptability and diagnostic accuracy of model under different working conditions, the application improves the intelligentization, robustness and abnormal response efficiency of injection well area pressure monitoring.
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