用图神经网络分析油气生产中的能耗异常，精准识别偷盗与效率问题。
Graph-Based Deep Learning for Intelligent Detection of Energy Losses, Theft, and Operational Inefficiencies in Oil & Gas Production Networks
- 构建井、站、区块的分层图结构，融合时空关系进行建模。
- 在真实数据上实现0.98的ROC-AUC和超93%的异常召回率。
- 适合能源领域运维人员和智能监控系统开发者参考。
由于油井与设施间存在复杂耦合关系、运行条件动态变化以及标注异常数据稀缺，早期发现能源损失、偷盗及运营低效仍是油气生产系统的重大挑战。传统机器学习方法通常孤立处理生产单元，难以应对时间分布偏移。本文提出一种基于时空图的深度学习框架，将生产系统建模为包含井、设施、区块的分层图，并引入共享基础设施的井间关联。通过基于产液、压力与流量行为的物理启发式规则生成弱监督异常标签。利用时序建模捕捉动态变化，采用时序图注意力网络学习关系依赖。在基于时间的评估中，模型达到约0.98的ROC-AUC和超过0.93的异常召回率，展现出对实际能源运营中主动监测的显著鲁棒性与应用潜力。
原文摘要 · Abstract (English)
Early detection of energy losses, theft, and operational inefficiencies remains a critical challenge in oil and gas production systems due to complex interdependencies among wells and facilities, evolving operating conditions, and limited labeled anomaly data. Traditional machine learning approaches often treat production units independently and struggle under temporal distribution shifts. This study proposes a spatiotemporal graph-based deep learning framework for anomaly detection in oil and gas production networks. The production system is modeled as a hierarchical graph of wells, facilities, and fields, with additional peer connections among wells sharing common infrastructure. Weakly supervised anomaly labels are derived from physically informed heuristics based on production, pressure, and flow behavior. Temporal dynamics are captured through sequence modeling, while relational dependencies are learned using a Temporal Graph Attention Network. Under time-based evaluation, the proposed model achieves an ROC-AUC of about 0.98 and anomaly recall above 0.93, demonstrating improved robustness and practical potential for proactive monitoring in real-world energy operations.
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