Version 1
: Received: 27 April 2021 / Approved: 28 April 2021 / Online: 28 April 2021 (10:39:31 CEST)
How to cite:
Vladova, A.Y. Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline. Preprints2021, 2021040745. https://doi.org/10.20944/preprints202104.0745.v1
Vladova, A.Y. Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline. Preprints 2021, 2021040745. https://doi.org/10.20944/preprints202104.0745.v1
Vladova, A.Y. Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline. Preprints2021, 2021040745. https://doi.org/10.20944/preprints202104.0745.v1
APA Style
Vladova, A.Y. (2021). Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline. Preprints. https://doi.org/10.20944/preprints202104.0745.v1
Chicago/Turabian Style
Vladova, A.Y. 2021 "Time Series Analysis of Geotechnical Data From a Sensor Network Controlling the Remoted Pipeline" Preprints. https://doi.org/10.20944/preprints202104.0745.v1
Abstract
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.
Keywords
green effect; pipelines; remote monitoring; data analysis; machine learning; time series
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
