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Abstract

Summary

The novelty of the implemented solutions lies in the improvement of drilling technologies based on the application of modeling algorithms and finding the optimal network configuration to perform a reliable forecast based on the artificial neural network model. Without comprehensive automation, it is impossible to reduce the role of personnel, which implies the robotization of part of the drilling process and technologies of descent operations. The presented concept of a geographically distributed system of intelligent monitoring and management is easily adaptable to various technological processes when working in emergency situations due to information support of construction processes. The introduction of technologies provides a reduction in operating costs, an increase in gas and oil production of about 10% and a reduction in well downtime of at least 50 % from the classic technologies of drilling, construction and operation in remote fields.

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/content/papers/10.3997/2214-4609.202156013
2021-08-04
2024-04-26

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References

  1. 1.DmitrievskiyA.N., EreminN.A., StolyarovV.E.Digital transformation of gas production // Scopus IOP Conf. Series: Materials Science and Engineering700 (2019) 012052.Dirac house, Bristol, England, BS1 6BE: 2019, P. 1–6, DOI:10.1088/1757‑899X/700/1/012052.
     https://doi.org/10.1088/1757-899X/700/1/012052 [Google Scholar]
  2. 2.ЕреминН.А., АрхиповА.И., ЧерниковА.Д., СарданашвилиО.Н., СтоляровВ.Е.Создание высокопроизводительной автоматизированной системы предотвращения осложнений и аварийных ситуаций в процессе строительства нефтяных и газовых скважин // Издательство Neftegaz.RU [4]-2020, С. 38–50.
     [Google Scholar]
  3. 3.ДмитриевскийА.Н., ЕреминН.А., СтоляровВ.Е., СафароваЕ.А.Цифровые скважины и месторождения // Актуальные вопросы исследования нефтегазовых пластовых систем (SPRS-2020): сборник докладов. — III Международная научно-практическая конференция Актуальные вопросы исследования нефтегазовых пластовых систем (SPRS-2020), 23–24 сентября 2020 г. — Газпром ВНИИГАЗ Москва, 2020. — С. 26–38.
     [Google Scholar]
  4. 4.АрхиповА.И., ДмитриевскийА.Н., ЕреминН.А., ЧерниковА.Д., БороздинС.О., СафароваЕ.А., СейнароевМ.Р.Анализ качества данных станции геолого-технологических исследований при распознавании поглощений и газонефтеводопроявлений для повышения точности прогнозирования нейросетевых алгоритмов // Нефтяное хозяйство — 2020, — № 08 (1162), С.63–67. DOI: 10.24887/0028‑2448‑2020‑8‑63‑67
     https://doi.org/10.24887/0028-2448-2020-8-63-67 [Google Scholar]
  5. 5.ДмитриевскийА.Н., СбоевА.Г., ЕреминН.А., ЧерниковА.Д., НаумовА.В., ГрязновА.В., МолошниковИ.А., БороздинС.О., СафароваЕ.А.Об увеличении продуктивного времени бурения нефтегазовых скважин с использованием методов машинного обучения.Георесурсы, 22(4) — 2020, — С. 79–85. DOI: https://doi.org/10.18599/grs.2020.4.79–85
     [Google Scholar]
  6. 6.ДмитриевскийА.Н., ЕреминН.А., ФилипповаД.С., СафароваЕ.А.Цифровой нефтегазовый комплекс России // Георесурсы, Спецвыпуск – 2020, С. 32–35. https://doi.org/10.18599/grs.2020.SI.32–35
     [Google Scholar]
  7. 7.ДмитриевскийА.Н., ЕреминН.А, СафароваЕ.А., ФилипповаД.С., БороздинС.О.Качественный анализ геоданных временного ряда для предупреждения осложнений и аварийных ситуаций при бурении нефтяных и газовых скважин // SOCAR Proceedings — № 3, 2020 — С. 31–36. http://dx.doi.org/10.5510/OGP20200300442
     [Google Scholar]
  8. 8.ЕреминН. А., СтоляровВ. Е.Совершенствование нормативно-правового регулирования развития науки и современных технологий в нефтегазовой отрасли // Автоматизация, телемеханизация и связь в нефтяной промышленности. — 2020. — Т. 569, № 12. — С. 15–26. DOI: 10.33285/0132‑2222‑2020‑12(569)‑15‑26
     https://doi.org/10.33285/0132-2222-2020-12(569)-15-26 [Google Scholar]
  9. 9.ЕреминН.А., СтоляровВ.Е., СафароваЕ.А., ФилипповаД.С.Нормативно-правовое обеспечение при внедрении инновационных разработок в нефтегазовой отрасли // Нефтепромысловое дело, 4 (628) — 2021, С. 51–58. DOI: 10.33285/0207‑2351‑2021‑4(628)‑51‑58
     https://doi.org/10.33285/0207-2351-2021-4(628)-51-58 [Google Scholar]
  10. 1.DmitrievskiyA.N., EreminN.A., StolyarovV.E.Digital transformation of gas production. // Scopus IOP Conf. Series: Materials Science and Engineering 700 (2019) 012052.Dirac house, Bristol, England, BS1 6BE: 2019., P. 1–6, DOI:10.1088/1757‑899X/700/1/012052.
     https://doi.org/10.1088/1757-899X/700/1/012052 [Google Scholar]
  11. 2.EreminN.A., ArkhipovA.I., ChernikovA.D., SardanashviliO.N., StolyarovV.E.Creation of a high-performance automated system to prevent complications and emergencies in the process of construction of oil and gas wells // Neftegaz.RU [4]-2020, P. 38–50.
     [Google Scholar]
  12. 3.DmitrievskyA.N., EreminN.A., StolyarovV.E., SafarovaE.A.Digital wells and fields // Actual issues of Studies of Petroleum Reservoir Systems (SPRS-2020): collection of reports. – III International Scientific and Practical Conference Actual Issues of Studies of Petroleum Reservoir Systems (SPRS-2020), September 23–24, 2020 - Gazprom VNIIGAZ Moscow, 2020. — P. 26 – 38.
     [Google Scholar]
  13. 4.ArkhipovA.I., DmitrievskyA.N., EreminN.A., ChernikovA.D., BorozdinS.O., SafarovaE.A., SeinaroevM.R.Data quality analysis of the station of geological and technological researches in recognizing losses and kicks to improve the prediction accuracy of neural network algorithms // Neftyanoe Khozyaystvo — 2020, — № 08 (1162), Р. 63–67. DOI: 10.24887/0028‑2448‑2020‑8‑63‑67
     https://doi.org/10.24887/0028-2448-2020-8-63-67 [Google Scholar]
  14. 5.DmitrievskyA.N., SboevA.G., EreminN.A., ChernikovA.D., NaumovA.V., GryaznovA.V., MoloshnikovI.A., BorozdinS.O., SafarovaE.A.On increasing the productive time of drilling oil and gas wells using machine learning methods.Georesursy = Georesources, 22(4) — 2020, P. 79–85. DOI: https://doi.org/10.18599/grs.2020
     [Google Scholar]
  15. 6.DmitrievskyA.N., EreminN.A., FilippovaD.S., SafarovaE.A.Digital oil and gas complex of Russia // Georesursy = Georesources, Special issue — 2020, P. 32–35. DOI: https://doi.org/10.18599/grs.2020.SI.32–35
     [Google Scholar]
  16. 7.DmitrievskyA.N., EreminN.A., SafarovaE.A., FilippovaD.S.Qualitative analysis of time series geodata to prevent complications and emergencies during drilling of oil and gas wells // SOCAR Proceedings — № 3, 2020 — P. 31–36. http://dx.doi.org/10.5510/OGP20200300442
     [Google Scholar]
  17. 8.EreminN.A., StolyarovV.E.Improving the legal regulatory framework for the development of science and modern technologies in the oil and gas industry // Automation, telemechanization and communication in oil industry2020, №12 (569), — P. 15–26 DOI: 10.33285/0132-2222-2020-12(569)-15-26
     [Google Scholar]
  18. 9.EreminN.A., StolyarovV.E., SafarovaE.A., FilippovaD.S.Legal support for the implementation of innovative developments in the oil and gas industry // Oilfield engineering, 4 (628) — 2021, P. 51–58. DOI: 10.33285/0207‑2351‑2021‑4(628)‑51‑58
     https://doi.org/10.33285/0207-2351-2021-4(628)-51-58 [Google Scholar]
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Intelligent Technology for Drilling and Well Construction in Russian Oil and Gas Fields
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202156013
/content/papers/10.3997/2214-4609.202156013
/content/papers/10.3997/2214-4609.202156013
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