Enhancing Performance of Digital Twin in the Oil and Gas Industry

Abhinav Parashar A Singh and Neepakumari Gameti

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 484-494

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.

Keywords: Digital Twin; Oil & Gas Industry; Optimization Design; Gas Production; Pipeline Monitoring (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT238324
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/CSEIT238324 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT238324.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i4:id:hcseit238324

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().