Marco Aurélio de Aguiar | Universidade Federal de Santa Catarina - UFSC (Federal University of Santa Catarina) (original) (raw)
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Papers by Marco Aurélio de Aguiar
Operating policies for oil production systems based purely on static behavior can incur excessive... more Operating policies for oil production systems based purely on static behavior can incur excessive gas flaring and potentially violate environmental regulations, specially when the system undergoes transients in response to predictable and unanticipated events. On the other hand, optimal control strategies can reach an optimal steady state by accounting for the system dynamics, while handling the transients triggered by such events. This paper develops optimal control strategies for a representative class of offshore oil production systems that consist of a gas-lift injection system, subsea equipment, and surface processing units. The optimal control problems are solved with the collocation method, which discretizes time and uses polynomial interpolation to find a continuous-time solution. The collocation method results in a large, but sparse nonlinear programming problem which is solved with state-of-the-art algorithms for dynamic optimization. We show an application where the proposed method can be used to plan and schedule processing equipment maintenance.
Production optimization of gas-lifted oil fields under facility, routing, and pressure constraint... more 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.
Keywords: MINLP; MILP; Mixed-Integer Convex Programming; Oil Production Optimization.
Operating policies for oil production systems based purely on static behavior can incur excessive... more Operating policies for oil production systems based purely on static behavior can incur excessive gas flaring and potentially violate environmental regulations, specially when the system undergoes transients in response to predictable and unanticipated events. On the other hand, optimal control strategies can reach an optimal steady state by accounting for the system dynamics, while handling the transients triggered by such events. This paper develops optimal control strategies for a representative class of offshore oil production systems that consist of a gas-lift injection system, subsea equipment, and surface processing units. The optimal control problems are solved with the collocation method, which discretizes time and uses polynomial interpolation to find a continuous-time solution. The collocation method results in a large, but sparse nonlinear programming problem which is solved with state-of-the-art algorithms for dynamic optimization. We show an application where the proposed method can be used to plan and schedule processing equipment maintenance.
Production optimization of gas-lifted oil fields under facility, routing, and pressure constraint... more 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.
Keywords: MINLP; MILP; Mixed-Integer Convex Programming; Oil Production Optimization.