Digital frameworks and autonomous operations are transforming the future of flow assurance. Recent developments in flow assurance feature three core trends: infrastructure optimization for decarbonization, autonomous well interventions, and hybrid modeling. The industry encounters persistent challenges regarding uncertainties in multiphase flow measurement, pipeline integrity for CO2 under transient conditions, and scale management in deepwater environments. Overcoming these limitations necessitates a transition from reactive empirical correlations to proactive digital frameworks and autonomous operations. Paper SPE 225871 details a real-time process twin for a large-scale network for carbon capture and storage. By forecasting critical thermodynamic parameters, continuous surveillance allows operators to optimize the transport of dense-phase CO2 and prevent accelerated corrosion during unplanned shutdowns. Paper OTC 36149 presents an autonomous methodology for the chemical treatment of inorganic scale in ultradeepwater carbonate reservoirs. Using existing completion rig infrastructure instead of conventional stimulation vessels reduces operational costs, logistical complexity, and the emission of carbon equivalents. Paper SPE 228033 introduces a neural network constrained by fundamental physical laws to solve Navier-Stokes equations for multiphase flow measurement. Embedding mass and momentum conservation directly into the training process improves prediction accuracy across varying flow regimes and minimizes reliance on empirical calibration.
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Changhyup Park (2026) studied this question.
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