State-of-the-art anomaly detection systems deployed in oilfields are expensive, not scalable to a large number of sensors, require manual operation, and provide data with a long delay. To overcome these problems, we design a wireless sensor network system that detects, identifies, and localizes major anomalies such as blockage and leakage that arise in steamflood and waterflood pipelines in oilfields. A sensor network consists of small, inexpensive nodes equipped with embedded processors and wireless communication, which enables flexible deployment and close observation of phenomena without human intervention. Our sensor-network-based system, Steamflood and Waterflood Tracking System (SWATS), aims to allow continuous monitoring of the steamflood and waterflood systems with low cost, short delay, and fine-granularity coverage while providing high accuracy and reliability. The anomaly detection and identification is challenging because of the inherent inaccuracy and unreliability of sensors and the transient characteristics of the flows. Moreover, observation by a single node cannot capture the topological effects on the transient characteristics of steam and water fluid to disambiguate similar problems and false alarms. We address these hurdles by utilizing multimodal sensing and multisensor collaboration, and exploiting temporal and spatial patterns of the sensed phenomena.
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