A study on cloud and edge computing for the implementation of digital twins in the Oil &amp; Gas industries

Knebel, Francisco Paiva; Trevisan, Rafael; Nascimento, Givanildo Santana do; Abel, Mara; Wickboldt, Juliano Araújo

doi:10.1016/j.cie.2023.109363

Cited by 19 publications

(6 citation statements)

References 36 publications

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“…It is essentially a speedier alternative to a conventional cloud-based system, which powers the Internet of Things more efficiently [57]. With edge computing, the oil and gas sector can better monitor and optimize the performance of its assets, such as pipelines, wells, rigs, and refineries by using real-time data and predictive analytics [58]. This can help identify and mitigate potential hazards, such as equipment failures, leaks, fires, or explosions, and reduce operational costs and environmental impacts [58].…”

Section: Ai In the Oil And Gas Sectormentioning

confidence: 99%

“…With edge computing, the oil and gas sector can better monitor and optimize the performance of its assets, such as pipelines, wells, rigs, and refineries by using real-time data and predictive analytics [58]. This can help identify and mitigate potential hazards, such as equipment failures, leaks, fires, or explosions, and reduce operational costs and environmental impacts [58]. Furthermore, machine learning using the least squares support vector machine has been employed to characterize pipeline operation and leakage in the oil and gas sector.…”

Section: Ai In the Oil And Gas Sectormentioning

confidence: 99%

“…Furthermore, machine learning using the least squares support vector machine has been employed to characterize pipeline operation and leakage in the oil and gas sector. The system can detect and locate pipeline leakage more accurately and precisely [58].…”

Section: Ai In the Oil And Gas Sectormentioning

confidence: 99%

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Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Tang

2024

J. Eng. Res. Rep.

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Artificial intelligence (AI) has gained much popularity in various sectors and has found applications in multiple areas, including occupational health and safety (OHS) risk management of the high-risk construction, mining, and oil and gas sectors. OHS risk management centers on identifying, assessing and controlling occupational risks systematically to prevent work-related injuries, illnesses and deaths. This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects. In the construction sector, AI can be employed in building information modeling during the design stage to identify and deal with the hazards of building models. AI can be deployed in construction sites through computer vision, sensor networks, knowledge-based systems, and machine learning to capture real-time site conditions, analyze the videos or pictures captured, and provide feedback to workers for appropriate responses. A similar setup involving the same components is also used for managing the OHS risks of surface or underground mining, particularly for monitoring the environmental conditions, detecting the presence of hazardous gases, and identifying hazards in locations that are remote and difficult to assess. Sensors can be attached to personal protective equipment and watches and the signals transmitted via Bluetooth to permit data collection for analysis and response by AI. In the oil and gas sector, sensors are extensively used to collect process safety data from wells, pipelines, valves, etc. for analytical and predictive Al. Al, especially, machine learning is used to create personalized training for workers based on their learning pace and characteristics. However, the major barriers identified are high cost, lack of support and skilled employees, ethical issues, and the uncertainty of AI.

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Section: Ai In the Oil And Gas Sectormentioning

confidence: 99%

Section: Ai In the Oil And Gas Sectormentioning

confidence: 99%

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Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Tang

2024

J. Eng. Res. Rep.

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“…(2) a local set up on the other end; (3) and edge computing somewhere in between (Knebel et al, 2023;Rasheed et al, 2020). Cloud computing, either by using commercial cloud services or a cloud service developed in-house, is not presented in detail in this paper but is an alternative for realization of digital twins.…”

Section: Introductionmentioning

confidence: 99%

“…Commercial cloud services make it possible to allocate data storage and computational power, with the drawback that data must be shared outside the organization. An in-house cloud service would diminish the need to share data outside the organization but can come with higher costs (Balasubramanian & Aramudhan, 2012;Knebel et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Automated data transfer for digital twin applications: Two case studies

Molin,

Wärff,

Lindblom

et al. 2024

Water Environment Research

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Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor‐made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method.Practitioner Points Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.

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