A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology

A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology

A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology

A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology

A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology
A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№2, 2020

A CONCEPTUAL MODEL OF DIGITAL TWIN FOR THE OIL AND GAS INDUSTRY

Yadigar N. Imamverdiyev

Oil and gas companies hope to use Industrial 4.0 technology to maintain a competitive advantage in the face of a deteriorating structure of hydrocarbon resources, volatile and sharply declining energy prices. The concept of “Oil and Gas 4.0”, based on the approach of Industry 4.0, has been on the agenda of companies for the last 2-3 years. Although Oil and Gas 4.0 is still in its infancy, the technology of digital twin as one of its main technologies has attracted much attention, but there are still some misunderstandings as to how it can be used to create additional benefits in oil and gas operations. A digital twin is a virtual prototype of a real physical object (oil field, well, equipment or infrastructure element), a product or process, the essence of which is to collect digital data and use it to monitor, manage and optimize the physical object. Despite the successful use of digital twin in a number of industries, the oil and gas industry is just beginning to apply this technology, and a number of associated problems are becoming increasingly important. The aim of this work is to analyze the problems of developing digital twin models in the oil and gas industry and to develop a generalized conceptual model for digital twin. The use of digital twin in the oil and gas industry is analyzed in terms of the integration of IT and operational technology. The components of the data management system, virtual object and visualization system, which are part of the proposed digital twin model, are described in detail. Centralized systems are used to manage the data, processes, and models that make up the virtual object. Models are trained through machine learning, and can also use the knowledge of experts and similar digital twins (pp.41-51).

Keywords: oil and gas industry, digitalization, digital transformation, digital twin, digital oilfield, machine learning, IIoT.
DOI : 10.25045/jpit.v11.i2.04
References
  • Sharma P., Hamedifar H., Brown A., & Green R. The dawn of the new age of the industrial Internet and how it can radically transform the offshore oil and gas industry / Offshore Technology Conference, 2017, 7 p. DOI: 10.4043/27638-MS.
  • Schwab K. The fourth industrial revolution. New York: Crown Business Publishing Group. 2016, 192 p.
  • Lu H., Guo L., Azimi, M., & Huang K. Oil and Gas 4.0 era: A systematic review and outlook // Computers in Industry, 201911, pp.68–90.
  • Taliangis P. Digital transformation of the oil, gas and energy value chain // The APPEA Journal, 2018, vol.582, pp.488–492.
  • Reis J., Amorim M., Melão N., & Matos P. Digital transformation: a literature review and guidelines for future research / World Conference on Information Systems and Technologies, 2018, pp.411–421.
  • Rasheed A., San O., & Kvamsdal T. Digital twin: Values, challenges and enablers. arXiv preprint arXiv:1910.01719. 2019, 31 p.
  • Gartner identifies the top 10 strategic technology trends for 2018. Gartner, Inc., 4 October 2017. https://www.gartner.com/newsroom/id/3812063.
  • Gefen C. Digital twin market-growth, size, share, forecast, industry analysis 2019-2027. https://works.bepress.com/charlie-gefen/63/download/
  • Tjønn A. Digital twin through the life of a field / Abu Dhabi International Petroleum Exhibition & Conference, 2018, pp.1–6. DOI: 10.2118/193203-MS.
  • Kritzinger W., Karner M., Traar G., Henjes J., & Sihn W. Digital twin in manufacturing: A categorical literature review and classification // IFAC-PapersOnLine, 2018, vol.51, no.11, pp.1016–1022.
  • Lim K. Y. H., Zheng P., & Chen C. H. A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives // Journal of Intelligent Manufacturing, 2020, vol.31, pp.1313–1337. DOI: 10.1007/s10845-019-01512-w.
  • Grieves M., & Vickers J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen FJ., Flumerfelt S., Alves A. (eds) Transdisciplinary Perspectives on Complex Systems. Springer, 2017, pp.85–113.
  • Grieves M. Digital twin: Manufacturing excellence through virtual factory replication. White paper, 2014, 8 p.
  • Tao F., Zhang H., Liu A., & Nee A.Y. Digital twin in industry: State-of-the-art // IEEE Transactions on Industrial Informatics, 2018, vol.15, no.4, pp.2405–2415.
  • El Saddik A. Digital twins: The convergence of multimedia technologies // IEEE MultiMedia, vol. 25, no. 2, pp. 87-92. DOI:10.1109/mmul.2018.023121167.
  • Barricelli B.R., Casiraghi E., Gliozzo J., Petrini A., & Valtolina S. Human digital twin for fitness management // IEEE Access, 2020, 8, 26637-26664.
  • Negri E., Fumagalli L., & Macchi M. A review of the roles of digital twin in CPS-based production systems // Procedia Manufacturing, 2017, vol.11, pp.939–948.
  • Parks M. Digital twinning: Types of digital twins. https://eu.mouser.com/applications/digital-twinning-types/
  • Qi Q., Tao F., Hu T., Anwer N., Liu A., Wei Y., Wang L., Nee A. Enabling technologies and tools for digital twin // Journal of Manufacturing Systems, 2019, vol.10, pp.129–145.
  • Uhlemann T.H.J., Lehmann C., & Steinhilper R. The digital twin: Realizing the cyber-physical production system for industry 4.0 // Procedia CIRP, 2017, vol.61, pp.335–340.
  • Qi Q., & Tao F. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison // IEEE Access, 2018, vol.6, pp.3585–3593.
  • Zeynalli A., Butdayev R., & Salmanov V. Digital transformation in oil and gas industry / SPE Annual Caspian Technical Conference, 2019, 7 p. 
    DOI: 10.2118/198337-MS.
  • Berge J. Digital transformation and IIoT for oil and gas production / Offshore Technology Conference, 2018, 10 p. DOI: 10.4043/28643-MS.
  • Devold H., Graven T., & Halvorsrød S.O. Digitalization of oil and gas facilities reduce cost and improve maintenance operations / Offshore Technology Conference, 2017, 16 p. DOI:10.4043/27788-MS.
  • Poddar T. Digital twin bridging intelligence among man, machine and environment / Offshore Technology Conference Asia, 2018, 4 p. DOI: 10.4043/28480-MS.
  • LaGrange E. Developing a digital twin: The roadmap for oil and gas optimization / SPE Offshore Europe Conference and Exhibition, 2019, 14 p. 
    DOI: 10.2118/195790-MS.
  • Min Q., Lu Y., Liu Z., Su C., & Wang B. Machine learning based digital twin framework for production optimization in petrochemical industry // International Journal of Information Management, 2019,49, pp.502–519.
  • Nadhan D., Mayani M. G., & Rommetveit R. Drilling with digital twins / IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition, 2018.
  • Mayani M.G., Baybolov T., Rommetveit R., Ødegaard S.I., Koryabkin V., & Lakhtionov S. Optimizing drilling wells and increasing the operation efficiency using digital twin technology / IADC/SPE International Drilling Conference and Exhibition, 2020, 10 p. DOI:10.2118/199566-MS.
  • Temizel C., Canbaz C.H., Palabiyik Y., Putra D., Asena A., Ranjith R., and Jongkittinarukorn K. A comprehensive review of smart/intelligent oilfield technologies and applications in the oil and gas industry / SPE Middle East Oil and Gas Show and Conference, 2019, 22 p. DOI: 10.2118/195095-MS.
  • Пилипенко Д. Цифровое месторождение. Взгляд компании SAP // Дайджест НефтеГаз, 2018, №4(11) с.10–11.
  • Kosenkov S., Turchaninov V.Y., Korovin I.S., & Ivanov D.Y. Digital twin of the oil well, based on data mining technologies / Proc. of the 2nd International Conference on Modeling, Simulation and Optimization Technologies and Applications, 2018, pp.233–238.
  • Du L., & Yao A. Digital techniques and its application in oil and gas pipelines // Oil & Gas Storage and Transportation, 2007, vol.26, no.6, pp.7–10.
  • Hlady J., Glanzer M., & Fugate L. Automated creation of the pipeline digital twin during construction: Improvement to construction quality and pipeline integrity / 12th International Pipeline Conference2018, 12 p. DOI: 10.1115/IPC2018-78146.
  • Alguliyev R.M., Imamverdiyev Y.N., Sukhostat L.V. Intelligent diagnosis of petroleum equipment faults using a deep hybrid model // SN Applied Sciences, vol.2, 2020, pp.1–16.
  • Hajizadeh Y. Machine learning in oil and gas; a SWOT analysis approach // Journal of Petroleum Science and Engineering, 2019, vol.176, pp.661–663.
  • Zhang C., Zhou G., Hu J., & Li J. Deep learning-enabled intelligent process planning for digital twin manufacturing cell // Knowledge-Based Systems, 2020, vol.191, 105247.