Journals / GİDB Dergi / 2026 / Sayı: 24

A REVIEW ON SUPERVISORY CONTROL AND DATA ACQUISITION (SCADA) SYSTEMS FOR FPSO UNITS

FPSO Gemilerinde SCADA Sitemlerinin Uygulanması Hakkında Derleme Çalışması

Özden İlkan Aydın(Istanbul Technıcal University)0000-0003-2437-9054
Mustafa Oğuzhan Akdoğan(ISTANBUL TECHNICAL UNIVERSITY)0009-0001-8805-5107
Pages
32–44
Publication Date
2026-04-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Additionally, the study outlines a comprehensive methodology for the implementation, validation, and continuous improvement of the system, offering a roadmap for engineers, policymakers, and offshore operators in shaping the future of FPSO operations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Floating Production, Storage, and Offloading (FPSO) units, which are key assets in the development of deepwater fields, face significant challenges in real-time data acquisition, operational transparency, and system integration.
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Key Findings & Data:This study presents a holistic SCADA-integrated framework specifically designed to enhance real-time production monitoring and operational intelligence in FPSO environments.
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Conclusion & Impact:Additionally, the study outlines a comprehensive methodology for the implementation, validation, and continuous improvement of the system, offering a roadmap for engineers, policymakers, and offshore operators in shaping the future of FPSO operations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:FPSO · SCADA · AÇIK DENİZ PETROL VE DOĞALGAZ ÜRETİMİ

Abstract

Floating Production, Storage, and Offloading (FPSO) units, which are key assets in the development of deepwater fields, face significant challenges in real-time data acquisition, operational transparency, and system integration. This study presents a holistic SCADA-integrated framework specifically designed to enhance real-time production monitoring and operational intelligence in FPSO environments. The proposed framework also supports proactive decision-making, increased asset reliability, and optimized process control. Furthermore, it enables the seamless flow of actionable insights from subsea operations to central control units, thereby creating a more synchronized and adaptive production environment. This holistic approach not only targets technical efficiency but also aligns with broader industry trends such as digital transformation, remote operability, and sustainability. By offering a scalable model adaptable to various FPSO configurations and geographic conditions, it provides strategic advantages in cost reduction, risk mitigation, and productivity enhancement. Additionally, the study outlines a comprehensive methodology for the implementation, validation, and continuous improvement of the system, offering a roadmap for engineers, policymakers, and offshore operators in shaping the future of FPSO operations.

Özet

Derin deniz sahalarının geliştirilmesinde kilit öneme sahip varlıklar olan Yüzer Üretim, Depolama ve Boşaltma (FPSO) üniteleri, gerçek zamanlı veri toplama, operasyonel şeffaflık ve sistem entegrasyonunda önemli zorluklarla karşılaşmaktadır. Bu çalışma, FPSO ortamlarında gerçek zamanlı üretim izleme ve operasyonel yetenekleri geliştirmek için özel olarak tasarlanmış bütünsel bir SCADA entegre altyapısı sunmaktadır. Önerilen altyapı ayrıca proaktif karar verme, varlık güvenilirliğinin artırılması ve optimize edilmiş süreç kontrolünü desteklemektedir. Bunun yanında su altı operasyonlarında merkezi kontrol ünitelerine karşılaşabilecek riskli durumların sorunsuz akışını sağlayarak daha senkronize bir üretim ortamı oluşturmaktadır. Bu bütünsel yaklaşım sadece teknik verimliliği hedeflemekle kalmaz, aynı zamanda dijital dönüşüm, uzaktan kontrol ve sürdürülebilirlik gibi daha geniş endüstri istekleri ile de uyumludur. Çeşitli FPSO operastonlarına ve coğrafi koşullara uyarlanabilir bir model sunarak, maliyet düşürme, risk azaltma ve verimlilik artırmada stratejik avantajlar sağlamaktadır. Ek olarak, çalışma, sistemin uygulanması, doğrulanması ve sürekli iyileştirilmesi için kapsamlı bir metodoloji ortaya koyarak, mühendisler ve açık deniz operatörleri için FPSO operasyonlarının geleceğini şekillendirmede bir yol haritası sunmaktadır.

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Cite This Article

Aydın, Ö. İ., Akdoğan, M. O. (2026). A REVIEW ON SUPERVISORY CONTROL AND DATA ACQUISITION (SCADA) SYSTEMS FOR FPSO UNITS. GİDB Dergi, Sayı: 24, 32–44. https://ajindex.com/articles/1586665

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References (33)

  1. Abbas, H. A. (2014). Future SCADA challenges and the promising solution: the agent-based SCADA. International journal of critical infrastructures, 10(3-4), 307-333.
  2. Abdulsalam, A., Okechukwu, M., Olukotun, K., & Onagun, Q. (2020). Analysis of bio-enhancers for pH and viscosity control in drilling fluid systems. Int. J. Res. Innov. Appl. Sci.(IJRIAS), (I).
  3. Adewoyin, M. A. (2021). Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry. Unpublished manuscript.
  4. Adewoyin, M. A. (2022). Advances in risk-based inspection technologies: Mitigating asset integrity challenges in aging oil and gas infrastructure. Open Access Research Journal of Multidisciplinary Studies, 4(1), 140-146.
  5. Allen, C., & Smith, R. (2012, February). New State-of-the-Art Asset-Optimization Data Applications for Intelligent Completions in Digital Oilfields. In SPE North Africa Technical Conference and Exhibition (pp. SPE-150848). SPE.
  6. Aminu, M., Akinsanya, A., Dako, D. A., & Oyedokun, O. (2024). Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms. International Journal of Computer Applications Technology and Research, 13(8), 11-27.
  7. Bello, O., Holzmann, J., Yaqoob, T., & Teodoriu, C. (2015). Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. Journal of Artificial Intelligence and Soft Computing Research, 5(2), 121-139.
  8. Carvajal, G., Maucec, M., & Cullick, S. (2017). Intelligent digital oil and gas fields: concepts, collaboration, and right-time decisions. Gulf Professional Publishing.
  9. Dienagha, I. N., Onyeke, F. O., Digitemie, W. N., & Adekunle, M. (2021). Strategic reviews of greenfield gas projects in Africa: Lessons learned for expanding regional energy infrastructure and security. GSC Advanced Research and Reviews, 8(01), 187-195.
  10. Dutra, T., Machado, L., Rodriguez, M., Almeida, I., Montanha, B., Manzali, M.,. & Herdeiro, M. (2010, December). Monitoring and Optimizing Oil Fields by a Real-Time Production Operation (RTPO) System. In SPE Latin America and Caribbean Petroleum Engineering Conference (pp. SPE-138316). SPE.
  11. Egbuhuzor, N. S., Ajayi, A. J., Akhigbe, E. E., Ewim, C. P. M., Ajiga, D. I., & Agbede, O. O. (2023). Artificial intelligence in predictive flow management: Transforming logistics and supply chain operations. International Journal of Management and Organizational Research, 2(1), 48-63.
  12. Esan, O. J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Olatunde, J. (2023). Leading with Lean Six Sigma and RPA in high-volume distribution: A comprehensive framework for operational excellence. Int. J. Multidiscip. Res. Growth Eval, 4(1), 1158-1164.
  13. Ezeanochie, C. C., Afolabi, S. O., & Akinsooto, O. L. U. W. A. D. A. Y. O. M. I. (2022). Advancing automation frameworks for safety and compliance in offshore operations and manufacturing environments. Journal name missing.
  14. Gowid, S. S. (2017). Improving the profitability, availability and condition monitoring of FPSO terminals (Doctoral dissertation, Loughborough University).
  15. Han, Y., Zhen, X., Moan, T., & Huang, Y. (2021). Real time prediction of operational safety limits for dynamic positioning of an FPSO in a Deepwater Artificial Seabed system. Marine Structures, 80, 103093.
  16. Honjo, S., Matsumoto, S., & Sano, T. (2021, August). Maximizing production with real-time integrity operating windows. In Offshore Technology Conference (p. D021S021R005). OTC.
  17. Hwang, H. J., Lee, J. H., Hwang, J. S., & Jun, H. B. (2018). A study of the development of a condition-based maintenance system for an LNG FPSO. Ocean Engineering, 164, 604-615.
  18. Igbadumhe, J. F., & Feijo, L. (2023, October). Challenges and Solutions for Asset Integrity Activities on Normally Unattended Offshore Facilities. In Offshore Technology Conference Brasil (p. D031S041R004). OTC.
  19. Ilori, O., Lawal, C. I., Friday, S. C., Isibor, N. J., & Chukwuma-Eke, E. C. (2022). The Role of Data Visualization and Forensic Technology in Enhancing Audit Effectiveness: A Research Synthesis. J. Front. Multidiscip. Res, 3(1), 188-200.
  20. Kanu, M. O., Dienagha, I. N., Digitemie, W. N., Ogu, E., & Egbumokei, P. I. (2022). Optimizing Oil Production through Agile Project Execution Frameworks in Complex Energy Sector Challenges. Energy Sector Innovations Journal, 8(4), 91-104.
  21. Ogunwole, O., Onukwulu, E. C., Sam-Bulya, N. J., Joel, M. O., & Achumie, G. O. (2022). Optimizing automated pipelines for realtime data processing in digital media and e-commerce. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 112-120.
  22. Ogunwole, O., Onukwulu, E. C., Sam-Bulya, N. J., Joel, M. O., & Ewim, C. P. (2022). Enhancing risk management in big data systems: A framework for secure and scalable investments. International Journal of Multidisciplinary Comprehensive Research, 1(1), 10-16.
  23. Ojika, F. U., Onaghinor, O. S. A. Z. E. E., Esan, O. J., Daraojimba, A. I., & Ubamadu, B. C. (2023). A predictive analytics model for strategic business decision-making: A framework for financial risk minimization and resource optimization. IRE Journals, 7(2), 764-766.
  24. Omisola, J. O., Shiyanbola, J. O., & Osho, G. O. (2023). A KPI-Driven Decision Intelligence Model: Using Integrated Dashboards to Enhance Strategic Operational Control in Advanced Manufacturing.
  25. Onyeke, F. O., Odujobi, O., Adikwu, F. E., & Elete, T. Y. (2024). The role of data-driven insights in industrial control systems: Advancing predictive maintenance and operational efficiency in refinery processes. Engineering Science & Technology Journal, 5(12), 3266-3277. Olayinka, O. H. (2022). Ethical implications and governance of AI models in business analytics and data science applications. International Journal of Engineering Technology Research & Management, 6(11).
  26. Onaghinor, O., Uzozie, O. T., & Esan, O. J. (2022). Optimizing Project Management in Multinational Supply Chains: A Framework for Data-Driven Decision-Making and Performance Tracking. Int. J. Multidiscip. Res. Growth Eval, 3(1), 907-913.
  27. Onyeke, F. O., Odujobi, O., Adikwu, F. E., & Elete, T. Y. (2023). Functional safety innovations in burner management systems (BMS) and variable frequency drives (VFDs): A proactive approach to risk mitigation in refinery operations. International Journal of Science and Research Archive, 10(2), 1223-1230.
  28. Onukwulu, E. C., Fiemotongha, J. E., Igwe, A. N., & Ewim, C. P. M. (2023). Marketing strategies for enhancing brand visibility and sales growth in the petroleum sector: Case studies and key insights from industry leaders. International Journal of Management and Organizational Research, 2(1), 74-86.
  29. Ozobu, C. O., Onyekwe, F. O., Adikwu, F. E., Odujobi, O., & Nwulu, E. O. (2023). Developing a national strategy for integrating wellness programs into occupational safety and health management systems in Nigeria: A conceptual framework. International Journal of Multidisciplinary Research and Growth Evaluation, 4(1), 914-927.
  30. Ubamadu, B. C., Bihani, D., Daraojimba, A. I., Osho, G. O., Omisola, J. O., & Etukudoh, E. A. (2022). Optimizing Smart Contract Development: A Practical Model for Gasless Transactions via Facial Recognition in Blockchain. Int. J. Multidiscip. Res. Growth Eval, 4(1), 978-989.
  31. Uzozie, O. T., Onaghinor, O., Esan, O. J., Osho, G. O., & Olatunde, J. (2022). Global Supply Chain Strategy: Framework for Managing Cross-Continental Efficiency and Performance in Multinational Operations. Int. J. Multidiscip. Res. Growth Eval, 3(1), 938-943.
  32. Vijayakumaran, C., Muthusenthil, B., & Manickavasagam, B. (2020). A reliable next generation cyber security architecture for industrial internet of things environment. International Journal of Electrical and Computer Engineering, 10(1), 387.
  33. Dutra, T., Machado, L., Rodriguez, M., Almeida, I., Montanha, B., Manzali, M.,. & Herdeiro, M. (2010, December). Monitoring and Optimizing Oil Fields by a Real-Time Production Operation (RTPO) System. In SPE Latin America and Caribbean Petroleum Engineering Conference (pp. SPE-138316). SPE.