Smart Instrumentation and Advanced Process Control for Improving Reliability and Operational Efficiency in Saudi Oil and Gas Facilities under Vision 2030

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@article{1722785,
      author = {Mohammed Juned Nijami},
      title = {Smart Instrumentation and Advanced Process Control for Improving Reliability and Operational Efficiency in Saudi Oil and Gas Facilities under Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {134-146},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722785.pdf},
      abstract = {Saudi oil and gas facilities have reached a stage where they must improve production continuity, energy efficiency, asset integrity, and emissions performance simultaneously. This study considers how smart instrumentation and advanced process control can be combined to create an operating approach that is focused on reliability, rather than being presented as separate digital projects. It reviews the literature published between 2020 and 2025 on intelligent sensors, Industrial Internet of Things connectivity, soft sensing, fault diagnosis, predictive maintenance, digital twins, model predictive control, nonlinear and economic predictive control, and real-time optimisation, with a specific emphasis on upstream production, gas compression, processing and refining. Instrumentation quality influences the amount of useful information available to higher levels of control, and advanced control uses this verified information to generate coordinated actions that reduce process variability, prevent equipment from operating in harmful areas, and utilise capacity within safe limits. Research in the oil and gas industry has produced positive results in machine-learning-based fault detection, self-diagnosis of multiphase meters, compressor control, gas-lift stabilisation, and health-aware optimisation. Further evidence from Saudi Arabia, based on facilities that have undergone digital transformation, also demonstrates the strategic value of large-scale sensing and closed-loop optimisation. The paper proposes an integrated Sense-Validate-Predict-Optimize-Act-Learn architecture, along with a five-stage implementation plan aligned with Vision 2030. The key conclusion is that the greatest reliability benefits are achieved when instrument assurance, analytics, process control, maintenance, cybersecurity, and workforce governance are designed as part of a single lifecycle system.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722785}
  }