Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline

Alla Yu. Vladova

doi:10.20944/preprints202104.0745.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 April 2021 / Approved: 28 April 2021 / Online: 28 April 2021 (10:39:31 CEST)

Extensive, but remote oil and gas fields of the United States, Canada, and Russia require the construction and operation of extremely long pipelines. Global warming and local heating effects lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this causes changes in the spatial positions and forms of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the remoted pipelines in order to be able to perform remedial measures in time. They are therefore seeking methods for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting uncorrelated time series; generating synthetic features; robust scaling temperature series, tuning the additive regression model to forecast soil temperatures.

green effect; pipelines; remote monitoring; data analysis; machine learning; time series

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Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline

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