Enhancing Performance of Digital Twin in the Oil and Gas Industry

Authors

  • Abhinav Parashar A Singh  Independent Researcher, USA
  • Neepakumari Gameti   Independent Researcher, USA

Keywords:

Digital Twin, Oil & Gas Industry, Optimization Design, Gas Production, Pipeline Monitoring

Abstract

The oil and gas (O&G) industry is using a range of digital technologies to improve operational safety, efficiency, and productivity in response to Industry 4.0's goals of lowering operating and capital costs, health and environmental dangers, and project life cycle unpredictability. Digital twins (DT) of assets may be created by O&G corporations utilizing state-of-the-art technology. Due to the fact that the industry is still in the early phases of adopting DT, there has been a lack of widespread deployment of the technology, limiting the benefits that may be achieved. An urban gas industry, crucial for energy supply stability and environmental sustainability, relies on efficient gas pressure management and prediction. Positive pressure equipment and gas governors ensure safe and stable gas supply by converting high-pressure gas to low-pressure. The O&G industry, facing regulatory demands, skill gaps, and low oil prices, embraces Digital Twin (DT) technology for innovation. The use of real-time data and simulations allows DTs to improve operating safety and proficiency. This study examines the use of DT for gas pressure control in the sector. It describes DT development progress, manufacturing enhancement, operation control and predictive management stressing on AI, IoT & big data. Key findings include improved pressure prediction accuracy, optimised production processes, and enhanced energy efficiency and stability in gas supply chains. The analysis is a basis for the identification of the possibilities of implementing DT in the O&G industry and demonstrates its yields for improvement of sustainability and competitiveness of companies in the sector.

References

  1. J. Li, Y. She, Y. Gao, M. Li, G. Yang, and Y. Shi, “Natural gas industry in China: Development situation and prospect,” Nat. Gas Ind. B, 2020, doi: 10.1016/j.ngib.2020.04.003.
  2. E. G. Rami, B. Jean-Jacques, D. Bruno, and M. François, “Modelling of a pressure regulator,” Int. J. Press. Vessel. Pip., 2007, doi: 10.1016/j.ijpvp.2006.10.002.
  3. J. Jia, B. Wang, R. Ma, Z. Deng, and M. Fu, “State Monitoring of Gas Regulator Station Based on Feature Selection of Improved Grey Relational Analysis,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2022.3184333.
  4. B. Boddu, “CLOUD DBA STRATEGIES FOR SQL AND NOSQL DATA MANAGEMENT FOR BUSINESS-CRITICAL APPLICATIONS,” https://ijcem.in/wp-content/uploads/CLOUD-DBA-STRATEGIES-FOR-SQL-AND-NOSQL-DATA-MANAGEMENT-FOR-BUSINESS-CRITICAL-APPLICATIONS.pdf, vol. 7, no. 1, p. 8, 2022.
  5. S. Mann, A. Balyan, V. Rohilla, D. Gupta, Z. Gupta, and A. W. Rahmani, “Artificial Intelligence-based Blockchain Technology for Skin Cancer Investigation Complemented with Dietary Assessment and Recommendation using Correlation Analysis in Elder Individuals,” 2022. doi: 10.1155/2022/3958596.
  6. T. Poddar, “Digital twin bridging intelligence among man, machine and environment,” in Offshore Technology Conference Asia 2018, OTCA 2018, 2018. doi: 10.4043/28480-ms.
  7. M. Gholami Mayani, M. Svendsen, and S. I. Oedegaard, “Drilling digital twin success stories the last 10 years,” in Society of Petroleum Engineers - SPE Norway One Day Seminar 2018, 2018. doi: 10.2118/191336-ms.
  8. D. Nadhan, M. G. Mayani, and R. Rommetveit, “Drilling with digital twins,” in Proceedings of the IADC/SPE Asia Pacific Drilling Technology Conference, APDT, 2018. doi: 10.2118/191388-ms.
  9. T. R. Wanasinghe et al., “Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges,” 2020. doi: 10.1109/ACCESS.2020.2998723.
  10. E. VanDerHorn and S. Mahadevan, “Digital Twin: Generalization, characterisation and implementation,” Decis. Support Syst., 2021, doi: 10.1016/j.dss.2021.113524.
  11. A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” 2021. doi: 10.1016/j.ptlrs.2021.05.009.
  12. M. Zhang, Y. Zuo, and F. Tao, “Equipment energy consumption management in digital twin shop-floor: A framework and potential applications,” in ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control, 2018. doi: 10.1109/ICNSC.2018.8361272.
  13. M. Pregnolato et al., “Towards Civil Engineering 4.0: Concept, workflow and application of Digital Twins for existing infrastructure,” Autom. Constr., 2022, doi: 10.1016/j.autcon.2022.104421.
  14. M. Grieves, “Digital Twin : Manufacturing Excellence through Virtual Factory Replication,” White Pap., 2014.
  15. M. Liu, S. Fang, H. Dong, and C. Xu, “Review of digital twin about concepts, technologies, and industrial applications,” J. Manuf. Syst., 2021, doi: 10.1016/j.jmsy.2020.06.017.
  16. B. Boddu, “Data Governance and Quality in Data Warehousing and Business Intelligence,” https://www.ijfmr.com/research-paper.php?id=10876, vol. 3, no. 6, p. 8, 2021.
  17. H. H. Hosamo, P. R. Svennevig, K. Svidt, D. Han, and H. K. Nielsen, “A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics,” Energy Build., 2022, doi: 10.1016/j.enbuild.2022.111988.
  18. Y. Yi, Y. Yan, X. Liu, Z. Ni, J. Feng, and J. Liu, “Digital twin-based smart assembly process design and application framework for complex products and its case study,” J. Manuf. Syst., 2021, doi: 10.1016/j.jmsy.2020.04.013.
  19. D. M. Botín-Sanabria, S. Mihaita, R. E. Peimbert-García, M. A. Ramírez-Moreno, R. A. Ramírez-Mendoza, and J. de J. Lozoya-Santos, “Digital Twin Technology Challenges and Applications: A Comprehensive Review,” 2022. doi: 10.3390/rs14061335.
  20. F. Shen, S. S. Ren, X. Y. Zhang, H. W. Luo, and C. M. Feng, “A Digital Twin-Based Approach for Optimization and Prediction of Oil and Gas Production,” Math. Probl. Eng., 2021, doi: 10.1155/2021/3062841.
  21. R. Shankar, G. V. K. Sasirekha, C. Ramanathan, and J. Bapat, “Knowledge-based Digital Twin for Oil and Gas 4.0 Upstream Process: A System Prototype,” in Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022, 2022. doi: 10.1109/IoTaIS56727.2022.9975974.
  22. B. Boddu, “DevOps for Database Administration: Best Practices and Case Studies,” https://jsaer.com/download/vol-7-iss-3-2020/JSAER2020-7-3-337-342.pdf, vol. 7, no. 3, p. 5, 2020.
  23. M. R. S. and P. K. Vishwakarma, “An Efficient Machine Learning Based Solutions for Renewable Energy System,” Int. J. Res. Anal. Rev., vol. 9, no. 4, pp. 951–958, 2022.
  24. D. Balderas, A. Ortiz, E. Méndez, P. Ponce, and A. Molina, “Empowering Digital Twin for Industry 4.0 using metaheuristic optimization algorithms: case study PCB drilling optimization,” Int. J. Adv. Manuf. Technol., 2021, doi: 10.1007/s00170-021-06649-8.
  25. A. Buja, M. Apostolova, A. Luma, and Y. Januzaj, “Cyber Security Standards for the Industrial Internet of Things (IIoT)- A Systematic Review,” in HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, 2022. doi: 10.1109/HORA55278.2022.9799870.
  26. H. S. Arpitha, K. R. Anand, and B. Gullapalli, “Digital Transformation of Oil & Gas Fields Architecting Multi-Services Digital Private Network on 5G NR-U Model,” in 2022 IEEE Wireless Antenna and Microwave Symposium, WAMS 2022, 2022. doi: 10.1109/WAMS54719.2022.9848382.
  27. S. R. Newrzella, D. W. Franklin, and S. Haider, “5-Dimension Cross-Industry Digital Twin Applications Model and Analysis of Digital Twin Classification Terms and Models,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3115055.
  28. O. Elijah et al., “A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3121302.
  29. M. F. Mostafa and S. A. Fahmy, “Maintenance Strategy Selection Using AHP: A Case Study in the Oil and Gas Industry,” in 2020 IEEE 7th International Conference on Industrial Engineering and Applications, ICIEA 2020, 2020. doi: 10.1109/ICIEA49774.2020.9101952..

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-08-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

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How to Cite

[1]
Abhinav Parashar A Singh, Neepakumari Gameti , " Enhancing Performance of Digital Twin in the Oil and Gas Industry" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.484-494, July-August-2023.