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.
Additional recommended papers explore kinetic hydrate inhibitors for uninsulated subsea tiebacks (OTC 36371), conceptual mapping of constraints for expanding industrial CO2 transport networks (SPE 225810), and thermodynamic modeling of elemental sulfur deposition in sour gas fields under high pressure (SPE 230009).
The evolution of flow assurance depends on integrating fundamental physics with data analytics. Furthermore, the transition toward specialized frameworks, such as vertical artificial intelligence (AI) and agentic AI, presents a transformative direction. Using in-house, field-oriented data enables companies to deploy purpose-built autonomous agents, elevating domain expertise while ensuring the critical security of big data.
Summarized Papers
225871 Real-Time Process Twin for CCS: A Critical Tool for Risk Management and Operational Insights by Nayad A. Wadia, John Wood Group, et al.
OTC 36149 Innovative Autonomous Chemical Treatment Using Completion Rig: Achieving Gains Without Well-Service Stimulation Vessel by Fernando A. Silva, Petrobras, et al.
SPE 228033 Advancing Multiphase Flow Measurement: Machine Learning vs. Traditional MPFM in Flow-Loop Testing by Henry Qi Jin, Medeng, et al.
Recommended Additional Reading
OTC 36371 Lean, Fast, and Feasible: Kinetic Hydrate Inhibitor Enables Cost-Effective Flow-Assurance Strategy To Improve Offshore Project Economics; Unlocking Marginal Tieback Production by Anthony Cheong, Sarawak Shell, et al.
SPE 225810 Conceptual Flow-Assurance Design of an Expanding Carbon Capture and Storage Transport Network by K. Szklarczyk-Marshall, Pace CCS, et al.
SPE 230009 Elemental Sulfur Management in Acid Gas Field Development: A Case Study From Uzbekistan by S.E. Togaev, Surhan Gas Chemical Operating Company, et al.
Changhyup Park, SPE, is a professor in the Department of Energy and Resources Engineering at Kangwon National University, South Korea, where he also holds joint appointments in the Department of Integrative Engineering for Hydrogen Safety and the Convergence Program for Carbon Neutral Industry, reflecting his broad expertise across emerging energy sectors. He holds BS, MS, and PhD degrees from Seoul National University in South Korea. Park has held visiting scholar positions at Stanford University and The University of Texas at Austin. He has published more than 70 peer-reviewed journal papers and has served as an active volunteer with SPE. Park received the 2020 Regional Distinguished Achievement Award for Petroleum Engineering Faculty for the Northern Asia Pacific Region.