-Production optimization of gas-lifted oil fields under facility, routing, and pressure constraints has attracted the attention of researchers and practitioners for its scientific challenges and economic impact. The available methods fall into one of two categories: nonlinear or piecewise-linear approaches. The nonlinear methods optimize simulation models directly or use surrogates obtained by curve fitting. The piecewise-linear methods represent the nonlinear functions using a convex combination of sample points, thereby generating a Mixed-Integer Linear Programming (MILP) problem. The nonlinear methods rely on compact models, but can get stuck in local minima, whereas the piecewise-linear methods can reach globally optimal solutions, but their models tend to get very large. This work combines these methods, whereby piecewise-linear models are used to approximate production functions, which are then composed with convex-quadratic models that approximate pressure drops. The end result is a Mixed-Integer Convex Programming (MICP) problem which is more compact than the MILP model and for which globally optimal solutions can be reached.

Developing Oil & Gas assets requires planning production on multiple horizons: (1) the long-term production plan includes strategic decisions for technology and recovery strategies to maximize the Net Present Value (NPV) of the project, (2) the mid-term horizon includes the drilling program and reservoir depletion/injection rates and (3) the short-term optimization (real-time production optimization or RTPO) aims to maximize the usage of the existing facilities. In the case of RTPO, both subsurface and surface systems are important, the goal being to maximize the daily production while honoring all operational constraints. RTPO requires a comprehensive integrated model covering the entire production system and an accurate mathematical formulation of the problem. This implies finding an appropriate optimization strategy and solver to find an optimal solution within a reasonable time. Sustainable production optimization solutions also assume continuous model update, maintenance and improvement, as the production system behavior changes over time. In this paper, we develop an integrated model for a complex multi-field asset. The production system includes 12 gas wells, 24 gas-lifted oil wells, 4 gas-injection wells, 4 CO2-injection wells, subsea manifolds, gas pipelines, offshore process facilities and CO2 removal units. Gas production from each field is gathered in a single gas pipeline system connected to a gas processing facility located onshore. Control variables include wellhead pressures, routing of wells, gas lift rates, flaring and re-injection rates. Many capacity, pressure and compositional constraints are considered through the whole production system. The production optimization model including binary variables and non-smooth non-linear functions is rather challenging to solve. Each part of the integrated model is approximated with multidimensional piecewise-linear functions to a desired degree of accuracy. The resulting Mixed Integer Linear Program (MILP) can be solved efficiently with existing commercial solvers. The optimization solution is used to answer different types of challenge: (1) platform start-up, (2) unexpected failure of a gas compressor, (3) maintenance on a group of wells and (4) changing reservoir conditions. Production increase driven by RTPO ranges from 1 to 5 % with no additional CAPEX. The implementation of the production optimization solution is also discussed. The importance of the usability, user training and solution maintenance is highlighted.