Howden’s white paper explores predictive analytics using Digital Twins to reduce downtime and optimize operations in oil and gas. It contrasts data-driven (machine learning) and model-driven (physics-based) approaches, highlighting the latter's scalability for heterogeneous assets. Hybrid Digital Twins combine both methods for better accuracy and pattern recognition. A key focus is building scalable, open IIoT architectures to future-proof deployments. The paper emphasizes aligning Digital Twin strategies with asset types and operational environments to ensure long-term value, adaptability, and continuous performance improvement.

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