A ReLU-based linearization approach for maximizing oil production in subsea platforms: An application to flow splitting

Abstract

Oil and gas extraction from subsea reservoirs is a complex process that is highly reliant on accurate mathematical modeling for optimization. A key challenge is identifying control settings to maximize oil production while accounting for the nonlinear behavior and constraints of the underlying physical systems. This paper introduces a new method using Rectified Linear Units (ReLU) neural networks to model these complexities. We focus on a subsea system connecting multiple oil wells to a manifold with multiple headers, explicitly modeling flow splitting to optimize oil yield. Our approach employs mixed-integer reformulations of ReLU neural networks to approximate pressure-drop functions in risers, allowing efficient optimization of production platforms considering routing possibility and flow-splitting operations. Computational analysis employing a branch-and-cut approach and p-split partitioning (for the ReLU model) shows that our ReLU-based methodology converges faster than standard piecewise-linear (PWL) approaches while delivering comparably effective results. Notably, ReLU found solutions in scenarios where PWL failed to find an incumbent solution within the time limit. These results underscore a significant advancement in the optimization of offshore production platforms by using ReLU-based models.