masterThesis
Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo
Fecha
2014-01-23Registro en:
DANTAS JUNIOR, Gaspar Fontineli. Nonlinear model predictive controller applied to slug control in oil production processes. 2014. 74 f. Dissertação (Mestrado em Automação e Sistemas; Engenharia de Computação; Telecomunicações) - Universidade Federal do Rio Grande do Norte, Natal, 2014.
Autor
Dantas Junior, Gaspar Fontineli
Resumen
Slugging is a well-known slugging phenomenon in multiphase flow, which may cause
problems such as vibration in pipeline and high liquid level in the separator. It can be
classified according to the place of its occurrence. The most severe, known as slugging in the
riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it
is capable of causing severe pressure fluctuations in the flow of the process, excessive
vibration, flooding in separator tanks, limited production, nonscheduled stop of production,
among other negative aspects that motivated the production of this work .
A feasible solution to deal with this problem would be to design an effective method for
the removal or reduction of the system, a controller. According to the literature, a
conventional PID controller did not produce good results due to the high degree of nonlinearity
of the process, fueling the development of advanced control techniques. Among
these, the model predictive controller (MPC), where the control action results from the
solution of an optimization problem, it is robust, can incorporate physical and /or security
constraints.
The objective of this work is to apply a non-conventional non-linear model predictive
control technique to severe slugging, where the amount of liquid mass in the riser is
controlled by the production valve and, indirectly, the oscillation of flow and pressure is
suppressed, while looking for environmental and economic benefits.
The proposed strategy is based on the use of the model linear approximations and
repeatedly solving of a quadratic optimization problem, providing solutions that improve at
each iteration. In the event where the convergence of this algorithm is satisfied, the predicted
values of the process variables are the same as to those obtained by the original nonlinear
model, ensuring that the constraints are satisfied for them along the prediction horizon.
A mathematical model recently published in the literature, capable of representing
characteristics of severe slugging in a real oil well, is used both for simulation and for the
project of the proposed controller, whose performance is compared to a linear MPC
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