Parametric Differentiable Flow Modeling for Fluid Production Optimization Under Flow Assurance Constraints

Abstract

This work presents a Physics-Informed Neural Network (PINN) framework for multiphase steady-state production systems and its application to constrained production optimization. The PINN is constructed to approximate the coupled momentum and energy equations of a flowline–riser configuration with state-dependent thermophysical properties obtained from tabulated PVT (Pressure-Volume-Temperature) data via differentiable bilinear interpolation. The network is trained by enforcing the governing equations and boundary conditions, yielding continuous pressure and temperature fields along the spatial domain without requiring labeled data. Once trained, the neural network weights are frozen and embedded into a production optimization problem, where choke settings and well activation decisions are optimized under global water-handling limits and local operability constraints, including bottom-hole pressure requirements and hydrate avoidance conditions evaluated along the entire flow path. The problem is cast as a mixed-integer nonlinear program, with physical feasibility enforced through a differentiable PINN surrogate.