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

User Rating:  / 0
PoorBest 

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



Abstract:

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.

References.

1. Frassy, F., Maianti, P., Marchesi, A., Nodari, F.R., Dalla Via, G., De Paulis, R., … & Gianinetto, M. (2015). Satellite remote sensing for hydrocarbon exploration in new venture areas. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.2015.7326417.

2. Lasica, R. (2015). A new age for oil and gas exploration remote sensing data and analytics are changing the industry. Earth Imaging Journal. Retrieved from http://eijournal.com/print/articles/a-new-age-for-oil-and-gas-exploration-remote-sensing-data-and-analytics-are-chang-ing-the-industry.

3. Bondur, V. G., Vorobyev, V. E., & Lukin, A. A. (2017). Satellite Monitoring of the Northern Territories Disturbed by Oil Production. Izvestiya, Atmospheric and Oceanic Physics, 53(9), 1007-1015. https://doi.org/10.1134/s0001433817090067.

4. Hnatushenko, V. V., Mozgovyi, D. K., Vasyliev, V. V., & Kavats, O. O. (2017). Satellite monitoring of consequences of illegal extraction of amber in Ukraine. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (2), 99-105.

5. Xing, Q., Meng, R., Lou, M., Bing, L., & Liu, X. (2015). Remote Sensing of Ships and Offshore Oil Platforms and Mapping the Marine Oil Spill Risk Source in the Bohai Sea. Aquatic Procedia, (3), 127-132. https://doi.org/10.1016/j.aqpro.2015.02.236.

6. Miegebielle, V., Dubucq, D., Taillandier, C., & Angeliaume, S. (2017). Use of Remote Sensing Radar Techniques for Oil and Gas O&G Facilities Survey in Offshore Domain for Environment and Exploration: Oil Slicks Detection and Interpretation Seeps and Spill. In SPE Health, Safety, Security, Environment, & Social Responsibility Conference ‒ North America. https://doi.org/10.2118/184419-ms.

7. Franklin, M., Chau, K., Cushing, L. J., & Johnston, J. (2019). Characterizing flaring from unconventional oil and gas operations in south Texas using satellite observations. Environmental Science & Technology. https://doi.org/10.1021/acs.est.8b05355.

8. Unger, D., Hung, I-Kuai, Farrish, Kenneth, W., & Dans, Darinda (2015). Quantifying Land Cover Change Due to Petroleum Exploration and Production in the Haynesville Shale Region Using Remote Sensing. Faculty Publications. Paper 43. https://doi.org/10.4018/ijagr.2015040101.

9. Liu, Y., Chao, S., Yang, Y., Zhou, M., Zhan, W., & Cheng, W. (2016). Automatic extraction of offshore platforms using time-series Landsat-8 Operational Land Imager data. Remote Sens. Environ, 175, 73-91. https://doi.org/10.1016/j.rse.2015.12.047.

10. Solari, L., Del Soldato, M., Bianchini, S., Ciampalini, A., Ezquerro, P., Montalti, R., Raspini, F., & Moretti, S. (2018). From ERS 1/2 to Sentinel-1: Subsidence Monitoring in Italy in the Last Two Decades. Frontiers in Earth Science, (6), 149. https://doi.org/10.3389/feart.2018.00149.

11. Mozgovoy, D., Hnatushenko, V., & Vasyliev, V. (2018). Accuracy evaluation of automated object recognition using multispectral aerial images and neural network. In Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018). https://doi.org/10.1117/12.2502905.

12. Mozgovoy, D. K., Hnatushenko, V. V., & Vasyliev, V. V. (2018). Automated recognition of vegetation and water bodies on the territory of megacities in satellite images of visible and IR bands. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-3, 167-172. https://doi.org/10.5194/isprs-annals-IV-3-167-2018.

Visitors

6235748
Today
This Month
All days
202
62425
6235748

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home About the journal EngCat Archive 2019 Contens №6 2019 All-weather monitoring of oil and gas production areas using satellite data