MONITORING PIPELINE INTEGRITY USING MACHINE LEARNING AIDED FIBER-OPTIC DISTRIBUTED ACOUSTIC SENSING

Abstract: a method for monitoring pipeline integrity is provided. the method includes obtaining, using at least one hardware processor, acoustic signal captured by at least one optical fiber arranged along a pipeline. the method includes inputting, using the at least one hardware processor, the acoustic signal to a machine learning model. the machine learning model is trained to execute a first analysis on a first representation of the acoustic signal and a second analysis on a second representation of the acoustic signal, the first analysis being independent to the second analysis. an output of the trained machine learning model is at least one of a first result based on the first analysis or a second result based on the second analysis. the method includes determining, using the at least one hardware processor, an indication of pipeline integrity based on the at least one of the first result or the second result.

Inventor(s): Abderrahim Fakiri, Shaj K. Manjalivalapil, Juan M. Marin, Islam Ashry, Boon Siew Ooi

CPC Classification: F17D5/06 (PIPE-LINE SYSTEMS; PIPE-LINES (pumps or compressors ; fluid dynamics ; valves or the like ; pipes, laying pipes, supports, joints, branches, repairing, work on the entire line, accessories ; steam traps or the like ; fluid-pressure electric cables ))

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