Developing a Model for Real-Time Decision Support Integration in FPSO-Based Deepwater Production Operations

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@article{1709214,
      author = {Andrew Tochukwu Ofoedu, Joshua Emeka Ozor, Oludayo Sofoluwe, Dazok Donald Jambol},
      title = {Developing a Model for Real-Time Decision Support Integration in FPSO-Based Deepwater Production Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {2},
      pages = {196-211},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709214.pdf},
      abstract = {Floating Production, Storage, and Offloading (FPSO) units are critical assets in deepwater oil and gas production, offering flexible and efficient solutions for hydrocarbon extraction in remote and challenging environments. However, the operational complexity, dynamic conditions, and safety-critical nature of FPSO-based deepwater production present significant challenges to effective decision-making. Real-time decision support systems (DSS) have emerged as essential tools to enhance situational awareness, optimize processes, and mitigate risks by integrating data from multiple sources and providing timely, actionable insights. Despite advances in automation and data analytics, the integration of real-time DSS tailored specifically for FPSO operations remains underdeveloped. This proposes a novel conceptual model for real-time decision support integration within FPSO-based deepwater production operations. The model is designed to assimilate heterogeneous data streams?including process measurements, environmental conditions, asset health indicators, and operational parameters?into a unified framework. Leveraging advanced technologies such as Internet of Things (IoT) sensors, edge computing, and artificial intelligence/machine learning (AI/ML), the model facilitates real-time data processing, predictive analytics, and visualization to support operators and engineers in making informed decisions rapidly. Key features of the model include multi-layered data integration, predictive maintenance forecasting, anomaly detection, and automated alert generation, all embedded within an intuitive human-machine interface. The architecture emphasizes scalability, interoperability with existing control systems, and adaptability to varying FPSO configurations. Validation approaches, including simulation and pilot implementation, are discussed to demonstrate the model?s potential to improve operational efficiency, safety, and reliability. By providing a structured and technology-enabled framework for decision support, this model addresses the unique challenges of deepwater FPSO operations and contributes to the digital transformation of offshore production. Future research directions include empirical validation, integration of autonomous control capabilities, and extension to other offshore asset types, ultimately enhancing resilience and sustainability in complex marine environments.},
      keywords = {Developing, Model, Real-time, Decision support, Integration, FPSO-based, Deepwater production, Operations},
      month = {August},
  }