PipePatrol Theft Detection

Solution for product theft detection

  • Fast and reliable identification and localisation of unauthorised or illegal product discharges
  • Dedicated theft pattern recognition for product theft detection
  • Initiation of alarms within minutes to stop theft in the act

PipePatrol Theft Detection

Solution for product theft detection

  • Fast and reliable identification and localisation of unauthorised or illegal product discharges
  • Dedicated theft pattern recognition for product theft detection
  • Initiation of alarms within minutes to stop theft in the act

Overview

The 2018 Concawe Report found that product theft is now the prime cause of pipeline incidents: illegal tapping accounts for more than 35% of all incidents. For the operator, detecting and locating thefts, although they involve small amounts of product, taken off for a limited time, requires a lot of effort and causes operational problems.

PipePatrol Theft Detection uses dedicated pattern recognition, to provide fast and reliable identification and localisation of unauthorised or illegal product discharges, typically theft. Even for small volumes, the system initiates an alarm within minutes, making it possible to stop theft in the act. To alert the relevant staff, the system provides alarm reporting via e-mail, accompanied by Google Earth® integration showing the theft location, enabling a field team to take coordinated and swift action. For identification of the actual theft times and locations, KROHNE also provide a post theft analysis service.

  • Provides alarm and theft reporting via e-mail
  • Includes the KROHNE SynEnergy v3 web-based user interface
  • Delivers a Google Earth® location report to support the field team, enabling swift reactions
  • Compatible with any device supporting HTML5, e-mail and Google Earth® (e.g. Android, IOS, Windows)
  • Additional post theft analysis service

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  • Fast and reliable identification and localisation of unauthorised or illegal product discharges
  • Dedicated theft pattern recognition for product theft detection
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