Везде
Integrated model for digital oil field development management
Table of contents
Annotation Estimate Publication content
References Comments
Share
QR
Metrics
Integrated model for digital oil field development management
Annotation
PII
S0869587324100036-1
Publication type
Article
Status
Published
Authors
I. V. Shpurov 
Affiliation: State Commission on Mineral Reserves
M. Yu. Danko
Affiliation: Tyumen Institute of Oil and Gas LLC
K. S. Kharchenko
Affiliation: Oil and Gas Scientific аnd Research Centre, Lomonosov State University
M. F. Pecherkin
Affiliation: Tyumen Institute of Oil and Gas LLC
A. G. Krotova
Affiliation: Oil and Gas Scientific аnd Research Centre, Lomonosov State University
E. A. Simakov
Affiliation: Tyumen Institute of Oil and Gas LLC
Edition
Volume 94 Issue number 10
Pages
892-899
Abstract
Providing the extractive industry with new technologies to maintain the levels of oil and gas production planned in the strategy of the Russian Federation is the most important task. First of all, an increase in profitable reserves must be ensured; this goal can be achieved both through geological exploration and through the capabilities of “digital transformation” and the formatting of decision-making systems. A field controlled by artificial intelligence is a new reality. At the same time, the main task of AI is not to reduce jobs or completely replace humans in the field, but to provide a systematic, objective approach to decision-making based on physics and mathematics, and not on the personal experience of an individual. Currently, the main planning tool in the oil and gas industry is a geological and hydrodynamic model. Recently, thanks to an increase in design capacity, the industry has made an evolutionary leap in this direction and is transitioning to integrated modeling that combines both the underground and surface parts of the field. The future is neural network technologies, combined with classical methods into a single digital environment for optimizing all processes, from reservoir to processing and sales of products. In the new paradigm, the process of development management at different stages of field development requires a radical transformation of both the tools used for this and changes in governing documents. To meet the ever-accelerating pace of progress, timely updating of the legislative framework is required, development of regulations for integrated modeling, including all elements of research and production, from seismic exploration to field development; in addition, each stage of field development requires its own requirements for the quality and volume of initial information, in accordance with the new production paradigm.
Keywords
управление разработкой месторождений углеводородного сырья трудноизвлекаемые запасы цифровой промысел интегрированное моделирование искусственный интеллект нейросети
Received
21.11.2024
Number of purchasers
0
Views
28
Readers community rating
0.0 (0 votes)
Cite   Download pdf

References

1. Нишкевич Ю.А., Рябец Д.А., Бриллиант Л.С. и др. Опыт организации роботизированных систем управления заводнением в алгоритмах искусственного интеллекта при оптимизации разработки высокообводненных залежей // Геология и недропользование. 2022. № 7. С. 51−58. / Nishkevich Yu.A., Ryabets D.A., Brilliant L.S. et al. Experience in organizing robotic waterflooding control systems in artificial intelligence algorithms for optimizing the development of highly watered deposits // Geology and subsoil use. 2022, no. 7, pp. 51−58. (In Russ.)

2. Андонов К.А., Елишева А.О., Круглов С.П. Управление разработкой зрелых месторождений с применением нейронных сетей на месторождениях ПАО “Лукойл”. https://togi.ru/publication/ (дата обращения 11.07.2024) / Andonov K.A., Elisheva A O., Kruglov S.P. Management of mature field development using neural networks at Lukoil PJSC fields. https://togi.ru/publication/ (date accessed 11.07.2024)

3. Меркель М.А. Предиктивная аналитика отказов ГНО. https://togi.ru/publication/ (дата обращения 11.07.2024) / Merkel M.A. Predictive analytics of GNO failures. https://togi.ru/publication/ (date accessed 11.07.2024)

4. Завьялов А.С. Технологии искусственного интеллекта в управлении добычей. https://togi.ru/publication/ (дата обращения: 11.07.2024) / Zavyalov A.S. Artificial Intelligence Technologies in Production Management. https://togi.ru/publication/ (date accessed 11.07.2024)

5. Зарубин А.Л, Перов Д.В., Рябец Д.А. и др. Автоматизация процессов нейросетевой оптимизации режимов закачки воды на месторождениях АО «НК “Нефтиса”» // Нефть. Газ. Новации 2020. № 8. С 30−35. / Zarubin A.L., Perov D.V., Ryabets D.A. et al. Automation of processes of neural network optimization of water injection modes at the fields of JSC NK Neftisa // Oil. Gas. Innovations. 2020, no. 8, pp. 30−35. (In Russ.)

6. Рябец Д.А, Бескурский В.В., Бриллиант Л.С. и др. Автоматизация процессов управления режимами работы нагнетательных скважин при нейросетевой оптимизации на объекте БС8 Западно-Малобалыкского месторождения // Neftegaz.RU. 2020. № 2 (98). / Ryabets D.A, Beskursky V.V., Brilliant L.S. et al. Automation of processes for controlling the operating modes of injection wells with neural network optimization at the BS8 facility of the Zapadno-Malobalykskoye field // Neftegaz.RU. 2020, no. 2 (98). (In Russ.)

7. Трофимова Е.П., Лучинин С.Б., Валеев Е.М., Южанина А.А. Технико-экономическое обоснование применения мобильных установок подготовки скважинной продукции на нефтяных месторождениях в период пробной эксплуатации // Разработка и эксплуатация нефтяных и газовых месторождений. 2020. № 4. С. 1−49. / Trofimova E.P., Luchinin S.B., Valeev E.M., Yuzhanina A.A. Feasibility study for the use of mobile well product preparation units at oil fields during trial operation // Development and operation of oil and gas fields. 2020. no. 4, pp. 1−49. (In Russ.)

8. Сугаипов Д.А., Батрашкин В.П., Хасанов М.М. и др. Основные принципы модульной стратегии обустройства месторождений в ПАО “Газпром нефть” // Нефтяное хозяйство. 2018. № 12. С. 68– 71. DOI: 10.24887/0028-2448-2018-12-68-71 / Sugaipov D.A., Batrashkin V.P., Khasanov M.M. et al. Basic principles of the modular strategy for field development at PJSC Gazprom Neft // Oil industry. 2018, no. 12, pp. 68−71. (In Russ.) DOI: 10.24887/0028-2448-2018-12-68-71

9. Дашевский А.В., Устимчук М.В., Дубровин К.А. и др. Современные решения для обустройства инфраструктуры малых месторождений // Нефтяное хозяйство. 2018. № 12. С. 124−125. / Dashevsky A.V., Ustimchuk M.V., Dubrovin K.A. et al. Modern solutions for the arrangement of small field infrastructure // Oil Industry. 2018, no. 12, pp. 124−125. (In Russ.)

10. Дашков Р.Ю., Гафаров Т.Н., Облеков Р.Г. и др. Опыт освоения и оптимизация разработки Лунского нефтегазоконденсатного месторождения // Газовая промышленность / Спецвыпуск. 2024. № 2 (866). С. 48−53. / Dashkov R.Yu., Gafarov T.N, Oblekov R.G. et al. Experience of development and optimization of the Lunskoye oil and gas condensate field // Gas industry. Special issue. 2024, no. 2 (866), pp. 48−53. (In Russ.)

Comments

No posts found

Write a review
Translate