Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience
Abstract
:1. Introduction
2. Research Methodology
3. Results of the Expert Survey
4. Transition to Digital and Smart Deposits: General Analysis of the Current State, the Leading Companies’ Experience
- A system of interrelated technologies and business processes that ensure an increase in the efficiency of all elements of oil and gas assets’ production and management;
- A software package that includes a set of applications that allow for modeling and managing processes in the field;
- A way to generate additional value of an oil and gas asset by improving the cycle of data collection, processing, modeling, decision making and their execution;
- Digital twin—cyber-physical oil and gas production system;
- The construction of super-heavy permanently operating systems for monitoring the seismological situation and combined active–passive systems of seismological monitoring;
- Building systems for the integrated control of development processes in various physical fields;
- The integration of borehole probes into self-organizing sensor networks;
- The construction of high-precision positioning systems and identification of deep processes;
- The construction of control and measurement systems for monitoring oil and gas fields and wells and others [6].
- The transformation of physical data into digital, which includes the following processes of collecting information on a real object: verification and rejection of incorrect data and the formation of an archive of events that significantly affect the physical process.
- The creation of a numerical model of a physical process, creation of specialized calculation modules of the digital twin, optimal solutions base formation;
5. Experience of Russian Oil and Gas Production Digitalization
- The launch of a corporate data processing center with an industrial Internet platform and an integrated digital twin of fields;
- The testing of technology for monitoring production facilities using drones and machine vision;
- The use of artificial intelligence in field development;
- Tests of the ice rig monitoring system for offshore drilling;
- The implementation of predictive analytics systems and dynamic equipment status indicators [19].
- The creation of regional models of oil and gas basins; construction of structural–tectonic and geological models of deposits; and modeling in the sphere of exploration drilling;
- The creation of digital models of fields (for example, in Priobskoye field by “Gazprom Neft”);
- Modeling the properties of reservoir fluids for the selection of optimal oil production technologies (they developed a self-learning program “Digital Core”, which predicts the properties of rocks in new fields);
- Conceptual geological modeling;
- Cognitive programs (“Cognitive geologist” project) [24].
6. Specifics of Hydrocarbon Production in the Arctic
- Extreme natural and climatic conditions (polar nights, strong winds combined with low air temperatures, frequent magnetic anomalies in the atmosphere, etc.);
- Ice formations of various natures (icebergs, ice cover of various cohesion);
- The significant (up to 600 km) remoteness of supply bases and the practical lack of infrastructure to ensure the development of offshore fields;
- High sensitivity of the Arctic ecosystem to man-made impacts [50].
6.1. The Problem of Ensuring Cybersecurity in Arctic Oil and Gas Projects
6.2. Power Outages
7. Prospects for the Digital Technologies’ Introduction in the Oil and Gas Industry within Its Competition with RES
7.1. Application of Industry 4.0 Technologies in the Oil and Gas Sector
- A variety of protocols, as well as a variety of data types. As the number of devices and sensors grows, the number of protocols for data collection increases, which urges the need to create new interfaces for organizing device networks and integrating them with existing data ecosystems. In addition, there is a need for a centralized data management system, which should be able to integrate disparate data types to create their single representations.
- Increasing the number of devices and sensors. Hubs, aggregators, gateways and other network equipment are needed to manage the lifecycle of new devices and sensors. At the same time, the amount of data created in the course of work and their dynamic nature may exceed the capabilities of systems used for operational decision support. Sensors, and the data produced by them, must be ordered, combined, matched and transformed.
- Search, exploration and development (upstream).
- Storage and transportation (midstream).
- Processing, marketing and sales (downstream).
- The increase in the oil recovery coefficient;
- The reduction in operating and capital costs;
- The reduction in pollutant emissions;
- The increase in high-tech jobs;
- Improving the energy efficiency of production [75].
7.2. Application of Industry 4.0 Technologies for Renewable Energy
- Improving the stability of the energy system state;
- Providing additional flexibility for renewable energy systems;
- Improving energy efficiency;
- Reducing energy consumption.
- Generation;
- Buffering;
- Transmission;
- Consumption.
7.3. Competitiveness of Renewable Energy in Russia
8. Conclusions
- A high dependence on foreign technological software solutions, characteristic of the entire Russian oil and gas industry;
- The problem of ensuring cybersecurity in Arctic oil and gas projects (ensuring security during data transmission and providing companies with information security specialists);
- Power outages.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Expert Survey
- Specify your company/work place/institution
- Specify your position
- Specify the digital technologies that, in your opinion, are most effective in the extraction of oil and gas resources in the entire industry.
- Big Data
- Digital twins
- Artificial intelligence
- Robotization
- Automated control systems
- Digital communication
- Specify your own option
- Specify the digital technologies that, in your opinion, are most effective when implementing oil and gas projects in the Arctics.
- Big Data
- Digital twins
- Artificial intelligence
- Robotization
- Automated control systems
- Digital communication
- Specify your own option
- Specify the Russian oil and gas company that, in your opinion, is the leader in digital technologies’ introduction
- Gazprom Neft
- Gazprom
- Rosneft
- NOVATEK
- LUKOIL
- TATNEFT
- Specify your own option
- Specify three digital technologies/projects/programs that, in your opinion, are successfully and effectively implemented by Russian oil and gas companies.
- 7.
- Specify three digital technologies/projects that, in your opinion, are most effectively implemented today in the extraction of oil and gas resources in the Arctic.
- 8.
- Specify 3 factors that, in your opinion, influenced the intensification of the introduction of digital technologies in 2019–2021.
- The need to reduce the costs for companies
- Improving the efficiency of business process management
- Adoption of the national program “Digital Economy of the Russian Federation” 2017
- Import substitution and sanctions
- COVID-19 pandemic
- Specify your own option
- 9.
- Specify the main factor for the digital technologies’ introduction intensification in 2019–2021.
- The need to reduce the costs of companies
- Improving the efficiency of business process management
- The adoption of “Digital Economy of the Russian Federation” 2017 national program
- Import substitution and sanctions
- The COVID-19 pandemic
- 10.
- In what cases, in your opinion, is the use of renewable electricity sources more appropriate in comparison with the use of hydrocarbons?
- If it is economically unprofitable to extract hydrocarbons
- In the absence of hydrocarbon resources (remote stations)
- If it is necessary to ensure a favorable environmental situation (Ecological issues)
- No way
- 11.
- Is renewable energy, in your opinion, the most optimal solution for providing energy supply to hard-to-reach Arctic regions?
- Yes
- No
- I find it difficult to answer
- 12.
- Will RES, in your opinion, be able to completely replace hydrocarbon energy in the future?
- Yes
- No
- I find it difficult to answer
References
- Eryomin, N.A.; Stolyarov, V.E.; Sardanashvili, O.N.; Chernikov, A.D. Intelligent drilling in digital field development. Autom. Telemech. Commun. Oil Ind. 2020, 5, 26–36. [Google Scholar] [CrossRef]
- Vlasov, A.I.; Mozhchil, A.F. Technology overview: From digital to intelligent oilfield PRONEFT. Prof. Oil 2018, 3, 68–74. [Google Scholar]
- Tcharo, H.; Vorobyev, A.E.; Vorobyev, K.A. Digitalization of the oil industry: Basic approaches and rationale for “intelligent” technologies. Eurasian Sci. J. 2018, 2, 1–17. Available online: https://esj.today/PDF/88NZVN218.pdf (accessed on 30 January 2022).
- Mazakov, E.B. Knowledge representation and processing in information automated systems of intellectual deposits. J. Min. Inst. 2014, 208, 256–262. [Google Scholar]
- Makhovikov, A.B.; Katuntsov, E.V.; Kosarev, O.V.; Tsvetkov, P.S. Digital Transformation in Oil and Gas Extraction. In Innovation-Based Development of the Mineral Resources Sector: Challenges and Prospects—11th Conference of the Russian-German Raw Materials, Potsdam, Germany, 7–8 November 2018; CRC Press: Boca Raton, FL, USA, 2018; pp. 531–538. [Google Scholar]
- Eryomin, N.A.; Dmitrievsky, A.N. Digital development of Russian Arctic zone: Status and best practices. Reg. Energy Energy Conserv. 2018, 3, 60–61. [Google Scholar]
- Pashali, A.A.; Kolonskikh, A.V.; Khalfin, R.S.; Silnov, D.V.; Topolnikov, A.S.; Latypov, B.M.; Urazakov, K.R.; Katermin, A.V.; Palaguta, A.A.; Enikeev, R.M. A digital twin of well as a tool of digitalization of bringing the well on to stable production in bashneft pjsoc. Neftyanoe Khozyaystvo Oil Ind. 2021, 3, 80–84. [Google Scholar] [CrossRef]
- Clemens, T.; Viechtbauer-Gruber, M. Impact of digitalization on the way of working and skills development in hydrocarbon production forecasting and project decision analysis. SPE Reserv. Eval. Eng. 2020, 23, 1358–1372. [Google Scholar] [CrossRef]
- Shishkin, A.N.; Timashev, E.O.; Solovykh, V.I.; Volkov, M.G.; Kolonskikh, A.V. Bashneft digital transformation: From concept design to implementation. Oil Ind. 2019, 3, 7–12. [Google Scholar] [CrossRef]
- Litvinenko, V.S.; Tsvetkov, P.S.; Dvoynikov, M.V.; Buslaev, G.V. Barriers to implementation of hydrogen initiatives in the context of global energy sustainable development. J. Min. Inst. 2020, 244, 428–438. [Google Scholar] [CrossRef]
- Linnik, Y.N.; Kiryukhin, M.A. Digital technologies in the oil and gas complex. State Manag. Univ. Bull. 2019, 7, 37–40. [Google Scholar]
- Eryomin, N.A.; Dmitrievsky, A.N.; Tikhomirov, L.I. Present and future of intellectual deposits. Oil Gas Innov. 2015, 12, 44–49. [Google Scholar]
- Dmitrievskiy, A.N.; Eryomin, N.A.; Stolyarov, V.E. Digital transformation of gas production. IOP Conf. Ser. Mater. Sci. Eng. 2019, 700, 012052. [Google Scholar] [CrossRef]
- Timchuk, D.D. “Smart wells” technology application at Salym group of deposits illustrated with the example of Western Salym. Sci. Forum. Sib. 2017, 3, 11. [Google Scholar]
- Kondeikina, K.V.; Tsoi, I.V. “Smart wells” technology application illustrated with the example of Western Salym. Acad. J. Sib. 2017, 13, 9. [Google Scholar]
- The Results of Russian Ministry of Energy Work and the Main Results of the Fuel and Energy Complex Functioning in 2020. Tasks for 2021 and the Medium Term. Available online: https://minenergo.gov.ru/node/20515 (accessed on 30 January 2022).
- Passport of the Innovative Development Program by “Gazprom Neft” PJSC until 2025. Saint Petersburg: “Gazprom Neft” PJSC. 2020. Available online: https://www.gazprom.ru/f/posts/97/653302/prir-passport-2018-2025.pdf (accessed on 30 January 2022).
- Passport of the Innovative Development Program by “NK ‘Rosneft’” PJSC. Moscow: “NK ‘Rosneft’” PJSC. 2016. Available online: https://www.rosneft.ru/upload/site1/document_file/FU6HdSZ3da.pdf (accessed on 30 January 2022).
- Zaichenko, I.M.; Fadeev, A.M.; Kostyuchenko, A.I. Building trends in the development of the fuel and energy complex enterprises in the Russian Federation in the context of digital business transformation. South Russ. State Polytech. Univ. (NPI) Bull. 2021, 3, 162–181. [Google Scholar]
- Dvoynikov, M.V.; Kunshin, A.A.; Blinov, P.A.; Morozov, V.A. Development of Mathematical Model for Controlling Drilling Parameters with Screw Downhole Motor. Int. J. Eng. IJE Trans. A Basics 2020, 7, 1423–1430. [Google Scholar]
- Litvinenko, V.S.; Dvoynikov, M.V. Methodology for determining the parameters of drilling mode for directional straight sections of well using screw downhole motors. J. Min. Inst. 2020, 241, 105–112. [Google Scholar] [CrossRef]
- Belozerov, I.P.; Gubaidullin, V. On the concept of technology for determining filtration-capacitance properties of terrigenous reservoirs on a digital core model. J. Min. Inst. 2020, 244, 402–407. [Google Scholar] [CrossRef]
- Prishchepa, O.M.; Borovikov, I.S.; Grokhotov, E.I. Oil and gas potential of the little-studied part of the north-west of the Timan-Pechora oil and gas province according to the results of basin modeling. J. Min. Inst. 2021, 247, 66–81. [Google Scholar] [CrossRef]
- Official Website of “Gazprom Neft” Scientific and Technical Center. Available online: https://ntc.gazprom-neft.ru/ (accessed on 30 January 2022).
- Razmanova, S.V.; Andrukhova, O.V. Oilfield service companies in the framework of economy digitalization: Assessment of innovative development prospects. J. Min. Inst. 2020, 244, 482–492. [Google Scholar] [CrossRef]
- Idrisova, S.A.; Tugarova, M.A.; Stremichev, E.V.; Belozerov, B.V. Digital core. integration of carbonate rocks thin section studies with results of routine core tests. PRONEFT’. Professional’no o Nefti 2018, 2, 36–41. [Google Scholar] [CrossRef] [Green Version]
- Erofeev, A.A.; Nikitin, R.N.; Mitrushkin, D.A.; Golovin, S.V.; Baykin, A.N.; Osiptsov, A.A.; Paderin, G.V.; Shel, E.V. BCYBER FRAC—Software platform for modeling, optimization and monitoring of hydraulic fracturing operations. Neftyanoye Khozyastvo 2019, 12, 64–68. Available online: https://www.oil-industry.net/Journal/archive_detail.php?art=235137 (accessed on 30 January 2022). [CrossRef]
- “NK ‘Rosneft’s’” Annual Report for Year 2019. Available online: https://www.rosneft.ru/upload/site1/document_file/a_report_2019.pdf (accessed on 30 January 2022).
- Rosneft Expands the Geography of the Use of Unmanned Aerial Vehicles to Control the Level of Greenhouse Gases. Available online: https://www.rosneft.ru/press/news/item/207499 (accessed on 30 January 2022).
- RN Digital. Software by “NK ‘ROSNEFT’” PJSC in the Sphere of Field Development. Available online: https://rn.digital/ (accessed on 30 January 2022).
- Lukoil-Technologies Expand the Possibilities of a Unified Information Space. LUKOIL-Technologies. Available online: https://technologies.lukoil.ru/ru/News/News?rid=539160 (accessed on 30 January 2022).
- “LUKOIL” PJSC’s Annual Report. Available online: https://lukoil.ru/FileSystem/9/551394.pdf (accessed on 30 January 2022).
- “Tatneft’s” Official Website. Available online: https://tatneft.ru/press-tsentr/press-relizi/more/8020/?lang=ru (accessed on 30 January 2022).
- “NOVATEK’s” Annual Report 2017. Transformation into a Global Gas Company. Available online: https://docplayer.com/80367186-Transformaciya-v-globalnuyu-gazovuyu-kompaniyu-godovoy-otchet.html (accessed on 30 January 2022).
- “NOVATEK’s” Sustainability Report for Year 2020. Available online: https://www.novatek.ru/common/upload/doc/NOVATEK_SR_2020_RUS.pdf (accessed on 30 January 2022).
- Digitalization of the Industry: Why Oil and Gas Companies Are Introducing Voice Assistants? Available online: https://neftegaz.ru/science/tsifrovizatsiya/711911-tsifrovizatsiya-otrasli-zachem-neftegazovye-kompanii-vnedryayut-golosovykh-pomoshchnikov/ (accessed on 30 January 2022).
- Titkov, I.A. Digital gap in the sphere of technologies on extraction and production of liquefied natural gas: A strategic factor in weakening the economic security of the country. Econ. Soc. Mod. Models Dev. 2020, 10, 309–329. [Google Scholar]
- Gazprom Digital Project Services. A Single Digital Platform. Available online: https://gazpromcps.ru/?page_id=29#section-briefcase (accessed on 30 January 2022).
- The Management Board Approved Gazprom Group’s Digital Transformation Strategy for 2022–2026. Available online: https://www.gazprom.ru/press/news/2021/december/article545124/ (accessed on 30 January 2022).
- “NK ‘Rosneft’s” Annual Report for Year 2020. Available online: https://www.rosneft.ru/upload/site1/document_file/a_report_2020.pdf (accessed on 30 January 2022).
- Pravikov, D.I. Actual approaches to ensuring the safety of industrial automation systems. Methods Tech. Means Ensuring Inf. Secur. 2020, 29, 15–17. [Google Scholar]
- Steve Krouskos Our Capital Confidence Barometer Survey Forms Part of a Wider Range of Insights on the COVID-19 Crisis. Available online: https://www.ey.com/en_ru/ccb/how-do-you-find-clarity-in-the-midst-of-covid-19-crisis (accessed on 30 January 2022).
- Made in Russia: Overview of 20 Russian Operating Systems. Available online: https://3dnews.ru/958857/made-in-russia-obzor20-rossiyskih-operatsionnih-sistem (accessed on 26 February 2022).
- Is There Life on the Russian OS Market? Overview of Popular Russian OS. Available online: https://habr.com/ru/company/digdes/blog/442906/ (accessed on 26 February 2022).
- Comparison of Baikal-M and Elbrus-8SV Processors. Available online: https://habr.com/ru/company/icl_services/blog/558564/ (accessed on 26 February 2022).
- CNA: Semiconductor Manufacturer TSMC Has Stopped Deliveries to Russia. Available online: https://ria.ru/20220227/tayvan-1775376385.html (accessed on 30 January 2022).
- Fedorov, V.P.; Zhuravel, V.P.; Grinyaev, S.N.; Medvedev, D.A. The Northern Sea Route: Problems and prospects of development of transport route in the Arctic. IOP Conf. Ser. Earth Environ. Sci. 2020, 434, 012007. [Google Scholar] [CrossRef]
- Savard, C.; Nikulina, A.Y.; Mécemmène, C.; Mokhova, E. The Electrification of Ships Using the Northern Sea Route: An Approach. J. Open Innov. Technol. Mark. Complex 2020, 6, 13. [Google Scholar] [CrossRef] [Green Version]
- Lindholt, L.; Glomsrød, S. The Role of the Arctic in Future Global Petroleum Supply. Discussion Papers. 2011, p. 645. Available online: https://www.ssb.no/a/publikasjoner/pdf/DP/dp645.pdf (accessed on 30 January 2022).
- Gafurov, A.; Skotarenko, O.; Nikitin, Y.; Plotnikov, V. Digital transformation prospects for the offshore project supply chain in the Russian Arctic. IOP Conf. Ser. Earth Environ. Sci. 2020, 539, 012163. [Google Scholar] [CrossRef]
- Cherepovitsyn, A.E.; Lipina, S.A.; Evseeva, O.O. Innovative Approach to the Development of Mineral Raw Materials of the Arctic Zone of the Russian Federation. J. Min. Inst. 2018, 232, 438–444. [Google Scholar] [CrossRef]
- Sochneva, I.O. Evolution of technical solutions for the Shtokman Gas Condensate Field and a modern view of its arrangement. Neftegaz Bus. Mag. 2021, 4, 74–83. [Google Scholar]
- “CAPTAIN”, a Digital Logistics Management System. “Gazpromneft”’s Official Website. Available online: https://www.gazprom-neft.ru/press-center/news/gazprom-neft-vnedrila-pervuyu-v-mire-tsifrovuyu-sistemu-upravleniya-logistikoy-v-arktike/ (accessed on 30 January 2022).
- Shurupov, N. Block-chain in logistics: “Gazprom Neft’s” experience. Sib. Oil 2018, 150. Available online: https://www.gazprom-neft.ru/press-center/sibneft-online/archive/2018-april/1533012/ (accessed on 30 January 2022).
- “Gazpromneft” Official Website. Available online: https://digital.gazprom-neft.ru/about-project?id=capitan (accessed on 30 January 2022).
- Cherepovitsyn, A.E.; Tsvetkov, P.S.; Evseeva, O.O. Critical analysis of methodological approaches to assessing the sustainability of Arctic oil and gas projects. J. Min. Inst. 2021, 249, 463–479. [Google Scholar] [CrossRef]
- “Gazprom Neft” and PwC in Russia Will Jointly Develop Technologies for Oil Production. “Gazprom Neft”’s Official Website. Available online: https://www.gazprom-neft.ru/press-center/news/gazprom_neft_i_pwc_v_rossii_budut_vmeste_razvivat_tekhnologii_dlya_neftedobychi/ (accessed on 30 January 2022).
- Core and Reservoir Fluids Research. “Gazprom Neft” Scientific and Technical Center. Available online: https://ntc.gazprom-neft.ru/business/exploration/core-analysis/ (accessed on 30 January 2022).
- “Messoyakhaneftegaz” Specialists Have Created Russia’s First Digital Well Construction Project in Partnership with “Schlumberger”. Schlumberger. Available online: https://www.slb.ru/press-center/press-releases/spetsialisty-messoyakhaneftegaza-sozdali-pervyy-v-rossii-tsifrovoy-proekt-stroitelstva-skvazhiny-v-p/ (accessed on 30 January 2022).
- Stolyarova, M.Y.; Valshin, O.K. Digital transformation of drilling planning: A new level of team interaction and the possibility of optimizing wells in a cloud solution DrillPlan. Oil Gas Innov. 2021, 8, 44–48. [Google Scholar]
- GOST 28147-89 Information Processing Systems. Cryptographic Protection. Cryptographic Conversion Algorithm. Available online: https://docs.cntd.ru/document/1200007350 (accessed on 30 January 2022).
- Zmieva, K.A. Problems of energy supply in the Arctic regions. Russ. Arct. 2020, 1, 5–14. [Google Scholar] [CrossRef]
- Eikeland, O.F.; Maria Bianchi, F.; Holmstrand, I.S.; Bakkejord, S.; Santos, S.; Chiesa, M. Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case. Energies 2022, 15, 305. [Google Scholar] [CrossRef]
- Shpenst, V.; Orel, E. Methods of ensuring the operational stability of dc-dc power supply in arctic conditions. Power Eng. Res. Equip. Technol. 2021, 23, 166–179. [Google Scholar] [CrossRef]
- Looney, B. BP statistical review of world energy. In Statistical Review of World Energy, 70th ed.; Whitehouse Associates: London, UK, 2021; p. 72. [Google Scholar]
- Blockchain Technology in the Oil and Gas Industry: A Review of Applications, Opportunities, Challenges, and Risks. IEEE J. 2019, 7, 41426–41444. Available online: https://ieeexplore.ieee.org/document/8675726 (accessed on 30 January 2022).
- Stocker, T. Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014; pp. 465–570. [Google Scholar]
- Kirsanova, N.; Lenkovets, O.; Nikulina, A. The Role and Future Outlook for Renewable Energy in the Arctic Zone of Russian Federation. Eur. Res. Stud. J. 2018, XXI, 356–368. [Google Scholar]
- Anna, B. Classification of the European Union member states according to the relative level of sustainable development. Qual. Quant. 2016, 50, 2591–2605. [Google Scholar] [CrossRef] [Green Version]
- Lu, H.; Guo, L.; Azimi, M.; Huang, K. Oil and Gas 4.0 era: A systematic review and outlook. Comput. Ind. 2019, 111, 68–90. [Google Scholar] [CrossRef]
- Erboz, G. How to define industry 4.0: Main pillars of industry 4.0. In Proceedings of the Managerial trends in the development of enterprises in globalization era: 7th International Conference on Management (ICoM 2017), Nitra, Slovakia, 1–2 June 2017; 2017; pp. 761–767. Available online: https://spu.fem.uniag.sk/fem/ICoM_2017/files/international_scientific_conference_icom_2017.pdf (accessed on 30 January 2022).
- The Internet of Things: Mapping the Value Beyond the Hype. Available online: https://www.mckinsey.com (accessed on 30 January 2022).
- Challenges, Opportunities and Strategies for Integrating IIoT Sensors with Industrial Data Ecosystems. Available online: https://resources.osisoft.com/white-papers/challenges,-opportunities-and-strategies-for-integrating-iiot-sensors-with-industrial-data-ecosystems/ (accessed on 30 January 2022).
- Udie, J.; Bhattacharyya, S.; Ozawa-Meida, L. A Conceptual Framework for Vulnerability Assessment of Climate Change Impact on Critical Oil and Gas Infrastructure in the Niger Delta. Climate 2018, 6, 11. [Google Scholar] [CrossRef] [Green Version]
- Alexandrova, T.V.; Prudsky, V.G. On the conseptual model of oil and gas business transformation in the transitional conditions to the Industry 4.0. Sci. Pap. Univ. Pardubice. Ser. D Fac. Econ. Adm. 2019, 46. Available online: https://www.semanticscholar.org/paper/On-the-conseptual-model-of-oil-and-gas-business-in-Alexandrova-Prudsky/6ad138bb8af0bc6260bcff7d831bbad8fdd15d98 (accessed on 30 January 2022).
- Olneva, T.; Kuzmin, D.; Rasskazova, S.; Timirgalin, A. Big data approach for geological study of the big region West Siberia. In Proceedings of the SPE Annual Technical Conference and Exhibition 2018, Dallas, TX, USA, 24–26 September 2018. [Google Scholar] [CrossRef]
- Cadei, L.; Montini, M.; Landi, F.; Porcelli, F.; Michetti, V.; Origgi, M.; Tonegutti, M.; Duranton, S. Big data advanced anlytics to forecast operational upsets in upstream production system. In Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference 2018, Abu Dhabi, United Arab Emirates, 12–15 November 2018. [Google Scholar] [CrossRef]
- Alfaleh, A.; Wang, Y.; Yan, B.; Killough, J.; Song, H.; Wei, C. Topological data analysis to solve big data problem in reservoir engineering: Application to inverted 4D seismic data. In Proceedings of the SPE Annual Technical Conference and Exhibition 2015, Houston, TX, USA, 28–30 September 2015. [Google Scholar] [CrossRef]
- Duffy, W.; Rigg, J.; Maidla, E. Efficiency improvement in the bakken realized through drilling data processing automation and the recognition and standardization of best safe practices. In Proceedings of the SPE/IADC Drilling Conference and Exhibition 2017, Hague, The Netherlands, 14–16 March 2017. [Google Scholar] [CrossRef]
- Hutchinson, M.; Thornton, B.; Theys, P.; Bolt, H. Optimizing drilling by simulation and automation with big data. In Proceedings of the SPE Annual Technical Conference and Exhibition 2018, Dallas, TX, USA, 24–26 September 2018. [Google Scholar] [CrossRef]
- Lin, A. Principles of big data algorithms and application for unconventional oil and gas resources. In Proceedings of the SPE Large Scale Computing and Big Data Challenges in Reservoir Simulation Conference and Exhibition 2014, Istanbul, Turkey, 15–17 September 2014. [Google Scholar] [CrossRef]
- Yuan, Z.; Qin, W.; Zhao, J. Smart manufacturing for the oil refining and petrochemical industry. Engineering 2017, 3, 179–182. [Google Scholar] [CrossRef]
- Mayani, M.G.; Svendsen, M.; Oedegaard, S. Drilling digital twin success stories the last 10 years. In Proceedings of the SPE Norway One Day Seminar 2018, Bergen, Norway, 18 April 2018. [Google Scholar] [CrossRef]
- Clarke, S.; Kapila, K.; Stephen, M. AR and VR Applications Improve Engineering Collaboration, Personnel Optimization, and Equipment Accuracy for Separation Solutions. In Proceedings of the SPE Offshore Europe Conference and Exhibition 2019, Aberdeen, UK, 3–6 September 2019. [Google Scholar] [CrossRef]
- Koteleva, N.; Buslaev, G.; Valnev, V.; Kunshin, A. Augmented reality system and maintenance of oil pumps. Int. J. Eng. 2020, 33, 1620–1628. [Google Scholar]
- Anagnostopoulos, A. Big data techniques for ship performance study. In Proceedings of the 28th International Ocean and Polar Engineering Conference 2018, Sapporo, Japan, 10–15 June 2018. [Google Scholar]
- MohamadiBaghmolaei, M.; Mahmoudy, M.; Jafari, D.; MohamadiBaghmolaei, R.; Tabkhi, F. Assessing and optimization of pipeline system performance using intelligent systems. J. Nat. Gas Sci. Eng. 2014, 18, 64–76. [Google Scholar] [CrossRef]
- Menegaldo, L.L.; Santos, M.; Ferreira, G.A.N.; Siqueira, R.G.; Moscato, L. SIRUS: A mobile robot for Floating Production Storage and Offloading (FPSO) ship hull inspection. In Proceedings of the 2008 10th IEEE International Workshop on Advanced Motion Control, Trento, Italy, 26–28 March 2008; pp. 27–32. [Google Scholar] [CrossRef]
- Menegaldo, L.L.; Ferreira, G.A.N.; Santos, M.F.; Guerato, R.S. Development and Navigation of a Mobile Robot for Floating Production Storage and Offloading Ship Hull Inspection. IEEE Trans. Ind. Electron. 2009, 56, 3717–3722. [Google Scholar] [CrossRef]
- Belsky, A.A.; Dobush, V.S.; Ivanchenko, D.I.; Gluhanich, D.Y. Electrotechnical complex for autonomous power supply of oil leakage detection systems and stop valves drive control systems for pipelines in arctic region. J. Phys. Conf. Ser. 2021, 1753, 012062. [Google Scholar] [CrossRef]
- Scharl, S.; Praktiknjo, A. The Role of a Digital Industry 4.0 in a Renewable Energy System. Int. J. Energy Res. 2019, 43, 3891–3904. [Google Scholar] [CrossRef]
- Polhamus, M. Joint Venture Has IBM Tackling Renewable Energy’s Grid Effects. VTDigger 2017. Available online: https://vtdigger.org/2017/02/28/joint-venture-ibm-tackling-renewable-energys-grid-effects/ (accessed on 30 January 2022).
- Wang, G.; Konstantinou, G.; Townsend, C.D.; Pou, J.; Vazquez, S.; Demetriades, G.D.; Agelidis, V.G. A Review of Power Electronics for Grid Connection of Utility-Scale Battery Energy Storage Systems. IEEE Trans. Sustain. Energy 2016, 7, 1778–1790. [Google Scholar] [CrossRef] [Green Version]
- Miller, N.; Manz, D.; Roedel, J.; Marken, P.; Kronbeck, E. Utility scale Battery Energy Storage Systems. In Proceedings of the IEEE PES General Meeting, Minneapolis, MN, USA, 25–29 July 2010; pp. 1–7. [Google Scholar] [CrossRef]
- Glenk, G.; Reichelstein, S. Economics of converting renewable power to hydrogen. Nat. Energy 2019, 4, 216–222. [Google Scholar] [CrossRef]
- Carmo, M.; Stolten, D. Energy storage using hydrogen produced from excess renewable electricity: Power to hydrogen. In Science and Engineering of Hydrogen-Based Energy Technologies; Elsevier: Amsterdam, The Netherlands, 2019; pp. 165–199. [Google Scholar]
- Bloess, A.; Schill, W.-P.; Zerrahn, A. Power-to-heat for renewable energy integration: A review of technologies, modeling approaches, and flexibility potentials. Appl. Energy 2018, 212, 1611–1626. [Google Scholar] [CrossRef]
- Kirkerud, J.G.; Bolkesjø, T.F.; Trømborg, E. Power-to-heat as a flexibility measure for integration of renewable energy. Energy 2017, 128, 776–784. [Google Scholar] [CrossRef]
- O’Leary, D.T.; Charpentier, J.-P.; Minogue, D. Promoting Regional Power Trade: The Southern African Power Pool; World Bank: Washington, DC, USA, 1998. [Google Scholar]
- Saborío-Romano, O.; Bidadfar, A.; Sakamuri, J.N.; Göksu, Ö.; Cutululis, N.A. Novel Energisation Method for Offshore Wind Farms Connected to HVdc via Diode Rectifiers. In Proceedings of the IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society 2019, Lisbon, Portugal, 14–17 October 2019; pp. 4837–4841. [Google Scholar] [CrossRef] [Green Version]
- Hedayati, M.; Jovcic, D. Scaled 500A, 900V, Hardware Model Demonstrator of Mechanical DC CB with Current Injection. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) 2018, Sarajevo, Bosnia and Herzegovina, 21–25 October 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Shukla, R.; Chakrabarti, R.; Narasimhan, S.R.; Soonee, S.K. Indian Power System Operation Utilizing Multiple HVDCs and WAMS. In Power System Grid Operation Using Synchrophasor Technology, Springer International Publishing; Springer: Cham, Switzerland, 2019; pp. 403–432. [Google Scholar] [CrossRef]
- Wang, J.; Chen, C.; Lu, X. Guidelines for Implementing Advanced Distribution Management Systems-Requirements for DMS Integration with DERMS and Microgrids; Argonne National Lab.(ANL): Argonne, IL, USA, 2015; pp. 1–76. [Google Scholar]
- Ilic, M.D.; Jaddivada, R.; Korpas, M. Interactive protocols for distributed energy resource management systems (DERMS). IET Gener. Transm. Distrib. 2020, 14, 2065–2081. [Google Scholar] [CrossRef]
- Kim, Y.-J.; Del-Rosario-Calaf, G.; Norford, L.K. Analysis and experimental implementation of grid frequency regulation using behind-the-meter batteries compensating for fast load demand variations. IEEE Trans. Power Syst. 2016, 32, 484–498. [Google Scholar] [CrossRef]
- Wu, D.; Kintner-Meyer, M.; Yang, T.; Balducci, P. Economic analysis and optimal sizing for behind-the-meter battery storage. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting, Boston, MA, USA, 17–21 July 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Electricity Consumption in Russia 2019. Rosinfostat. Available online: https://rosinfostat.ru/potreblenie-elektroenergii/ (accessed on 30 January 2022).
- Dolf, G.; Deger, S. REMAP 2030 Renewable Energy Prospects for the Russian Federation. IRENA. Available online: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2017/Apr/IRENA_REmap_Russia_paper_2017.pdf (accessed on 30 January 2022).
- Public Perceptions on Climate Change and Energy in Europe and Russia: Evidence from Round 8 of the European Social Survey|Resul Umit. Available online: https://resulumit.com/publications/perceptions-climate-energy/ (accessed on 30 January 2022).
- Avenarius, I.G. National Atlas of Russia. Available online: https://elibrary.ru/item.asp?id=23554805 (accessed on 30 January 2022).
- Lanshina, T.A.; John, A.; Potashnikov, V.Y.; Barinova, V.A. The slow expansion of renewable energy in Russia: Competitiveness and regulation issues. Energy Policy 2018, 120, 600–609. [Google Scholar] [CrossRef]
- Ilinova, A.; Dmitrieva, D. Sustainable Development of the Arctic zone of the Russian Federation: Ecological Aspect. Biosci. Biotechnol. Res. Asia 2016, 13, 2101–2106. [Google Scholar] [CrossRef]
- Ilinova, A.; Chanysheva, A. Algorithm for assessing the prospects of offshore oil and gas projects in the Arctic. Energy Rep. 2020, 6, 504–509. [Google Scholar] [CrossRef]
- Gold–Sulphide Deposits of the Russian Arctic Zone: Mineralogical Features and Prospects of Ore Benefication—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/pii/S0009281918301375 (accessed on 30 January 2022).
Type of Activity the Organization Does | Name of the Organization | The Number of Respondents | The Number of Those Who Refused |
---|---|---|---|
Companies specializing in oil and gas production (heads of departments, shift supervisors, geologists and engineers) | “Gazprom Neft” PJSC | 3 | 1 |
“Gazprom Neft Shelf” LLC | 2 | 2 | |
“Gazprom” PJSC | 3 | 1 | |
“NK” “Rosneft” PJSC | 4 | 0 | |
“LUKOIL” PJSC | 2 | 2 | |
“Tatneft” PJSC | 3 | 1 | |
“NOVATEK” PJSC | 3 | 1 | |
Scientific institutes (heads of departments, researchers) | Kola Scientific Center at Russian Academy of Sciences | 2 | 0 |
Arctic and Antarctic Research Institute | 2 | 0 | |
Institute of Oil and Gas at Russian Academy of Sciences | 1 | 1 | |
Universities (deans and heads of specialized departments) | Saint Petersburg Mining University | 2 | - |
Gubkin Russian State University of Oil and Gas | 2 | - | |
Northern (Arctic) Federal University | 2 | - | |
Northeastern Federal University named after M.K. Ammosov | 2 | - | |
Ukhta State Technical University | 1 | 1 | |
Ufa State Petroleum Technical University | 2 | - | |
Kazan Federal University | 2 | - | |
Kola Scientific Center at Russian Academy of Sciences | 3 | 1 | |
Arctic and Antarctic Research Institute | 2 | 2 | |
Institute of Oil and Gas at Russian Academy of Sciences | 3 | 1 |
Qualitative Characteristics | Respondents’ Opinions |
---|---|
Digital technologies are the most effective for the extraction of oil and gas resources in the entire industry | Digital twins—44.4%: |
companies’ representatives—25% | |
scientific institutes’ representatives—5.5% | |
universities’ representatives—13.9% | |
Big Data—25%: | |
companies’ representatives—13.9% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Artificial intelligence—14% | |
companies’ representatives—5.6% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
Others—16.6% | |
companies’ representatives—11% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—2.8% | |
Digital technologies are the most effective for the implementation of oil and gas projects in the Arctic | Digital twins—38.9% |
companies’ representatives—22.2% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—11.1% | |
Robotization—30.3% | |
companies’ representatives—16.4% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—8.3% | |
Digital communication—14% | |
companies’ representatives—5.6% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
Automated control systems—8.4% | |
companies’ representatives—5.6% | |
scientific institutes’ representatives—0% | |
universities’ representatives—2.8% | |
Others—8.4% | |
companies’ representatives—5.6% | |
scientific institutes’ representatives—0% | |
universities’ representatives—2.8% | |
A Russian oil and gas company that is a leader in digital technologies’ introduction | Gazprom Neft—52.8% |
companies’ representatives—30.6% | |
scientific institutes’ representatives—8.3% | |
universities’ representatives—13.9% | |
Rosneft—22.2% | |
companies’ representatives—11.1% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Gazprom—19.4% | |
companies’ representatives—11% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
NOVATEK—5.6% | |
companies’ representatives—2.8% | |
scientific institutes’ representatives—0% | |
universities’ representatives—2.8% | |
Digital technologies/projects/programs that are successfully and effectively implemented by Russian oil and gas companies | Digital Core—33.3% |
companies’ representatives—19.4% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—11.1% | |
Cognitive Geologist—27.7% | |
companies’ representatives—16.6% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Cyber Hydraulic Fracturing—22.2% | |
companies’ representatives—13.8% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
Digital Deposit—22.2% | |
companies’ representatives—13.8% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
Others—16.8% | |
companies’ representatives—5.6% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—5.6% | |
Digital technologies/projects that are most effectively implemented today with the extraction of oil and gas resources in the Arctic | “CAPTAIN” system—27.8% |
companies’ representatives—13.9% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—8.3% | |
Block chain technology “Prirazlomnaya” SISP—19.4% | |
companies’ representatives—8.3% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Cognitive Geologist—19.4% | |
companies’ representatives—8.3% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Digital Core—16.7% | |
companies’ representatives—11.1% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—2.8% | |
Digital twin of the Vostochno-Messoyakhskoye field—16.7% | |
companies’ representatives—11.1% | |
scientific institutes’ representatives—0% | |
universities’ representatives—5.6% |
Factors | Respondents’ Opinions |
---|---|
Three factors that influenced the intensification of digital technologies’ introduction in 2019–2021 | Improving the efficiency of business process management—36.1% |
companies’ representatives—19.4% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—11.1% | |
The need to reduce companies’ expenses—30.5% | |
companies’ representatives—16.6% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—8.3% | |
Import substitution and sanctions—13.9% | |
companies’ representatives—8.3% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—2.8% | |
“Digital Economy of the Russian Federation” national program adopted in 2017—13.9% | |
companies’ representatives—8.3% | |
scientific institutes’ representatives—0% | |
universities’ representatives—5.6% | |
COVID-19 pandemic—5.6% | |
companies’ representatives—2.8% | |
scientific institutes’ representatives—0% | |
universities’ representatives—2.8% | |
The main factor influencing the intensification of digital technologies’ introduction in 2019–2021 | Improving the business process management efficiency—58.3% |
companies’ representatives—38.8% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—13.9% | |
The need to reduce companies’ expenses—33.3% | |
companies’ representatives—16.6% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—11.1% | |
“Digital Economy of the Russian Federation” national program adopted in 2017—8.4% | |
companies’ representatives—0% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—5.6% | |
Import substitution and sanctions—0% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
COVID-19 pandemic—0% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
Factors hindering the digital technologies’ introduction in the extraction of oil and gas resources | Lack of qualified personnel—41.7% |
companies’ representatives—22.2% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—13.9% | |
Lack of an appropriate material and technical base—36.1% | |
companies’ representatives—22.2% | |
scientific institutes’ representatives—5.6% | |
universities’ representatives—8.3% | |
Cyber security issues—22.2% | |
companies’ representatives—11.1% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Low growth in companies’ efficiency compared to expectations—0% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
The high cost of technology—0% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
Sanctions—0% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% |
Question | Respondents’ Opinions |
---|---|
In which cases is the use of renewable energy more appropriate in comparison with the use of hydrocarbons? | If it is economically unprofitable to extract hydrocarbons—52.7% |
companies’ representatives—27.8% | |
scientific institutes’ representatives—8.3% | |
universities’ representatives—16.6% | |
None—25% | |
companies’ representatives—25% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
Ecological issues—16.7% | |
companies’ representatives—2.8% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—11.1% | |
In the absence of hydrocarbon ones (remote stations)—5.6% | |
companies’ representatives—0% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—2.8% | |
Is RES the most optimal solution for providing an energy supply to hard-to-reach Arctic regions? | Yes—66.6% |
companies’ representatives—38.8% | |
scientific institutes’ representatives—11.1% | |
universities’ representatives—16.7% | |
No—19.5% | |
companies’ representatives—13.9% | |
scientific institutes’ representatives—0% | |
universities’ representatives—5.6% | |
No idea—13.9% | |
companies’ representatives—2.8% | |
scientific institutes’ representatives—2.8% | |
universities’ representatives—8.3% | |
Will RES be able to completely replace hydrocarbon energy in the future? | Yes—0% |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—0% | |
No—97.2% | |
companies’ representatives—55.6% | |
scientific institutes’ representatives—13.9% | |
universities’ representatives—27.7% | |
No idea—2.8% | |
companies’ representatives—0% | |
scientific institutes’ representatives—0% | |
universities’ representatives—2.8% |
Company | Technology |
---|---|
Shell | Smart Field |
Chevron | i-field |
BP | Field of the future |
Petoro | Smart Operations |
Halliburton | Real Time Operation |
Schlumberger | Smart Wells |
Gazpromneft, Rosneft | Digital field |
Lukoil | Life-Field |
Oil and Gas Production Steps | Technology | Examples |
---|---|---|
Search, exploration and development | Big Data; Industrial Internet of Things; Digital twins; Augmented reality; | 1, 2 and 3D geological maps [76]; Forecasting possible technological threats [77]; Seismic data processing [78]; Improving drilling efficiency [79,80]; Analysis of oil layers [81]; Smart cleaning of petroleum products [82]; Application of digital twins for drilling [83]; The use of augmented and virtual reality technologies for drilling wells [84] and pumping equipment maintenance [85]. |
Storage and transportation | Big Data Industrial Internet of Things Digital twin Augmented reality | The use of Big Data to optimize the movement of ships [86]; The use of digital technologies for the pipeline system [87]; Use of robots for ship inspection [88,89]; Autonomous pipe leak detection system [90]. |
Processing, marketing and sales | Industrial Internet of Things; Digital twins; Big Data | Introduction of digital technologies into oil refining [82]. |
Renewable Energy Production Steps | Technology | Examples |
---|---|---|
Generation | Industrial Internet of Things; Big Data; Cloud computing; Digital twin; | Increasing the stability of renewable energy generation by improving weather forecasting [92]; Modernization of batteries [93,94] |
Buffering | Industrial Internet of Things; Big Data | Energy storage by means of hydrogen [95,96] and thermal [97,98] energy accumulators. |
Transmission | Industrial Internet of Things; Big Data | Implementation of inter-network connections [99]; Equalization of consumption and generation over long distances using main power lines [100,101,102] |
Consumption | Industrial Internet of Things | Optimization of the distributed electricity system operation depending on the consumer’s needs [103,104]; The use of storage devices on the consumer side in order to compensate for the additional load on the network [105,106]; |
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Samylovskaya, E.; Makhovikov, A.; Lutonin, A.; Medvedev, D.; Kudryavtseva, R.-E. Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience. Resources 2022, 11, 29. https://doi.org/10.3390/resources11030029
Samylovskaya E, Makhovikov A, Lutonin A, Medvedev D, Kudryavtseva R-E. Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience. Resources. 2022; 11(3):29. https://doi.org/10.3390/resources11030029
Chicago/Turabian StyleSamylovskaya, Ekaterina, Alexey Makhovikov, Alexander Lutonin, Dmitry Medvedev, and Regina-Elizaveta Kudryavtseva. 2022. "Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience" Resources 11, no. 3: 29. https://doi.org/10.3390/resources11030029
APA StyleSamylovskaya, E., Makhovikov, A., Lutonin, A., Medvedev, D., & Kudryavtseva, R. -E. (2022). Digital Technologies in Arctic Oil and Gas Resources Extraction: Global Trends and Russian Experience. Resources, 11(3), 29. https://doi.org/10.3390/resources11030029