Development of a Stochastic Multiphase Flow Model for Severe Slugging Prediction and Control in Deepwater Riser-Pipeline Systems Offshore Nigeria

Severe slugging in deepwater riser-pipeline systems is a stochastic phenomenon arising from the interaction of multiphase fluid dynamics, terrain geometry, and operational boundary conditions in ways that deterministic slug flow models cannot fully capture, leading to under-prediction of slug frequency and pressure extremes in Nigerian deepwater flowlines. This study develops an original stochastic multiphase flow model for severe slugging prediction and control applicable to deepwater riser-pipeline systems offshore Nigeria. The theoretical model builds on the two-fluid Navier-Stokes formulation for slug unit dynamics and introduces a stochastic perturbation model for bubble growth and collapse processes at the riser base, parameterised from physical experiments in a high-pressure multiphase flow facility replicating Nigerian deepwater fluid properties and riser geometry ratios. Stochastic ensemble simulations of 10,000 slug cycles provide probability distributions of slug length, frequency, and peak pressure that are compared against deterministic OLGA predictions and against instrumented field data from a West African deepwater tieback. The stochastic model reduces the peak pressure prediction error from 23.4 percent for deterministic simulation to 8.6 percent compared to measured field pressure extremes. An original model-based slug control algorithm that leverages real-time riser differential pressure measurements to trigger proactive choke throttling is developed and demonstrated to reduce slug severity by 62 percent in simulation trials. Keywords: severe slugging, stochastic flow model, deepwater riser, multiphase flow, Nigeria offshore.