The digitalization and automation of oil and gas technology parallel to the growing need for safety in operations creates more opportunities for usage of fiber optic sensing. It has been in use in the industry since the 1990s as distributed temperature sensing (DTS). DTS is established as a well monitoring and pipeline monitoring technique. Nevertheless Distributed Acoustic Fiber Optic Sensing (DAS) is gaining immense attention when it comes to continuous detection of potential threats at pipelines. The technology turns fiber optic cables into “virtual microphones”. It measures the backscattered light and is sensitive to sound, relative temperature changes, vibrations and strain.
The fiber can be used to monitor up to 60 fiber km with one read out unit and is able to prevent damages by detecting unwanted incidents such as digging activities, illegal taps or unauthorized access in real-time. Besides it can provide information about potential leakages and can detect the current position of a PIG. Its versatility is open to integrate additional operator specific use cases that produce temperature changes, strain or sound.
The advantage of high sensitivity simultaneously is a disadvantage. In real field use environmental conditions may differ (e.g. soil texture, climate and fiber deployment), enormous amount of acoustic sources are continuously present and changing, and may interfere with signals of sources that have to be reliable detected.
The real challenge lies in data interpretation, reliable detection and machine learning mechanism without spending massive effort in testing. In our presentation we want to analyze the momentum of DAS in the context of applicability as pipeline monitoring system and its necessary evolutionary steps with advanced analytic and multi-algorithm approach towards maximum reliability with low implementation effort. Finally we will report on a practical technical solution developed at Siemens including our field experience and results obtained with customers.