Науковий вісник НГУ

All-weather monitoring of oil and gas production areas using satellite data

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Category: Contens №6 2019

Last Updated on 01 January 2020

Published on 23 December 2019

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Authors:

V.V.Hnatushenko, Dr. Sc. (Tech.), Prof., orcid.org/0000-0003-3140-3788, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.K.Mozgovoy, Cand. Sc. (Tech.), orcid.org/0000-0003-1632-1565, Oles Honchar Dnipro National University, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Hnatushenko, Dr. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0001-5304-4144, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Spirintsev, Cand. Sc. (Tech), Assoc. Prof., orcid.org/0000-0002-0908-1180, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.M.Udovyk, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0002-5190-841X, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 повний текст / full article

Purpose. Development and testing of an information technology for automated processing of medium-resolution satellite images acquired at different times for all-weather monitoring of oil and gas production areas.

Methodology. The information technology implies the use of two algorithms. The algorithm for automated recognition of new oil extraction sites is based on computing the normalized difference index (NDI) of temporal changes for a given pair of satellite images in visible and near-IR bands acquired at different times and selected spectral cannels with subsequent multi-threshold binarization. The algorithm for automated recognition of large metal objects uses bipolarized C-band radar images from Sentinel-1A/B satellites.

Findings. The proposed information technology made it possible to automatically detect the new oil extraction sites and recognition of large metal objects. The overall recognition accuracy, averaged for 20 test areas, was from 87 to 91 % with the kappa coefficient ranging from 0.82 to 0.85. For cloud-covered areas the big metal objects (an extraction units, automotive equipment for oil transportation, etc.) were recognized using SAR imagery from Sentinel-1A/B satellites only.

Originality. Unlike the existing methods for detection of anthropogenic changes of the Earth’s surface by satellite images, the proposed information technology uses immediate calculation of the normalized difference index of temporal changes NDI for a given pair of satellite images acquired at different times, which considerably reduces requirements to the computing power while ensuring higher accuracy of the allocation of the boundaries of new oil production sites. All-weather monitoring is provided using radar data.

Practical value. Owing to high degree of automation, the developed technology can be implemented as a geoinformation web service for all-weather up-to-the-date monitoring of oil extraction areas. This web service can be used to determine the area of fields, control production activity and estimate oil production, supervise development and production activities and assess anthropogenic load in oil production areas.

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