Flow assurance

This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.
The advancement of flow-assurance technology can be characterized as a transition from reactive to proactive, from laboratory-scale examination to field applicability, and from data-driven to big data and physics integration.
This paper explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.

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