Parallel Dantzig–Wolfe decomposition for real-time optimization—Applied to a complex oil field

Abstract

This paper studies Dantzig–Wolfe decomposition for real-time optimization of process systems with a decentralized structure. The idea is to improve computational efficiency and transparency of a solution. The contribution lies in the application of the Dantzig–Wolfe method which allows us to efficiently decompose an optimization problem into parts. Moreover, we show how the algorithm can be parallelized for even higher efficiency. The nonlinear system is modeled by piecewise linear models with the added benefit that error bounds can be computed. In this context alternative parameterizations are discussed. The properties of the method are studied by applying it to a model of a complex petroleum field with severe production optimization challenges due to rate dependent gas-coning wells. The model resembles the Troll west oil rim, a huge gas and oil field on the Norwegian Continental shelf. Finally, the paper discusses workflows in production optimization as a means to explain how the proposed methodology can be applied in practice.