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A novel optimization approach for oil and gas production process considering model parameters uncertainties
Author(s) -
Gao Xianwen,
Liu Tan,
Yuan Qingyun,
Wang Lina
Publication year - 2016
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22560
Subject(s) - sorting , gas oil ratio , mathematical optimization , process (computing) , genetic algorithm , production (economics) , compensation (psychology) , process optimization , nonlinear system , nonlinear programming , optimization problem , computer science , multi objective optimization , engineering , petroleum engineering , algorithm , mathematics , psychology , physics , quantum mechanics , environmental engineering , psychoanalysis , economics , macroeconomics , operating system
Through analyzing the integrated oil and gas production process, a multi‐objective optimization model for the integrated oil and gas production process is established through considering nonlinear reservoir behaviour, multiphase flow in wells, and constraints from the surface facilities. In order to reduce the influence of model parameter uncertainty in the oil and gas production process, an error compensation method based on the Gaussian mixture model (GMM) is proposed to compensate the model. Non‐ dominated sorting genetic algorithm‐II (NSGA‐II) is used as the optimization algorithm. Moreover, an operational strategy using post‐ optimization is applied to solve the optimization model, so as to ensure the feasibility of the obtained optimal set‐point. Finally, a novel optimization approach for the oil and gas production process considering model parameter uncertainty is proposed. Simulation results indicate that the proposed optimization method is feasible and effective.