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Remote Geotechnical Monitoring of a Buried Oil Pipeline

Author

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  • Alla Yu. Vladova

    (V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, 117997 Moscow, Russia)

Abstract

Extensive but remote oil and gas fields in Canada and Russia require extremely long pipelines. Global warming and local anthropogenic effects drive the deepening of seasonal thawing of cryolithozone soils and enhance pathological processes such as frost heave, thermokarst, and thermal erosion. These processes lead to a reduction in the subgrade capacity of the soils, causing changes in the spatial position of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the daily temperatures of unevenly heated soils along pipeline routes. However, they are confronted with the problem of separating anthropogenic heat losses from seasonal temperature fluctuations. To highlight heat losses, we propose a short-term prediction approach to a transformed multidimensional dataset. First, we define the temperature intervals according to the classification of permafrost to generate additional features that sharpen seasonal and permafrost conditions, as well as the timing of temperature measurement. Furthermore, linear and nonlinear uncorrelated features are extracted and scaled. The second step consists of selecting a training sample, learning, and adjusting the additive regression model. Forecasts are then made from the test sample to assess the accuracy of the model. The forecasting procedure is provided by the three-component model named Prophet. Prophet fits linear and nonlinear functions to define the trend component and Fourier series to define the seasonal component; the third component, responsible for the abnormal days (when the heating regime is changed for some reason), could be defined by an analyst. Preliminary statistical analysis shows that the subsurface frozen soils containing the oil pipeline are mostly unstable, especially in the autumn season. Based upon the values of the error metrics, it is determined that the most accurate forecast is obtained on a three-month uniform time grid.

Suggested Citation

  • Alla Yu. Vladova, 2022. "Remote Geotechnical Monitoring of a Buried Oil Pipeline," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1813-:d:823786
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    References listed on IDEAS

    as
    1. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    2. Perpar, Matjaz & Rek, Zlatko & Bajric, Suvad & Zun, Iztok, 2012. "Soil thermal conductivity prediction for district heating pre-insulated pipeline in operation," Energy, Elsevier, vol. 44(1), pages 197-210.
    3. Baak, M. & Koopman, R. & Snoek, H. & Klous, S., 2020. "A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
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