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This paper examines the utilization of machine learning techniques to maximize the value from the improved data fidelity and reduce commissioning times provided by the latest long-range quantitative distributed fiber-optic sensing interrogator unit technology.

The paper also demonstrates how supervised ML techniques can be used to deliver a step-change improvement in the detection of rolling stock movements which enables a performance improvement across the rail monitoring solution portfolio.

This paper examines the dataset produced at the Hydraulic Fracture Test Site 2 (HFTS2) in the Permian Delaware Basin. HFTS2 was one of the most comprehensive multiwell fiber optic sensing projects. The paper discusses distributed sensing diagnostics (crosswell strain, das microseismic, injection and production monitoring) integrated with other technologies, modeling, validation and interpretation of a broad range of physical processes.