Alex Furtado Teixeira

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Production optimization of gas-lifted oil wells with routing and pressure constraints is a complex problem, which has attracted the interest of engineers and scientists alike. To this end, a mixed-integer nonlinear model is developed to automatically decide upon flowing well production to a single or multiple manifolds (in the paper referred to as automatic(More)
In this work we design data-driven soft sensors of downhole pressure for gas-lift oil wells. We employ a two-step procedure. First, discrete-time (N)ARX models are identified offline from historical data. Second, recursive predictions of these multiple models are combined with current measured data (of variables other than the downhole pressure) by means of(More)
In gas-lifted oil wells the monitoring of downhole pressure plays an important role. However, the permanent downhole gauge (PDG) sensor often fails. Because maintenance or replacement of PDGs is usually unfeasible, soft-sensors are promising alternatives to monitor the downhole pressure in the case of sensor failure. In this paper, a data-driven soft-sensor(More)
A practical control algorithm for stabilizing flow in risers and oil production wells should meet several requirements. i) be simple, ii) able to operate with low-cost measurements and possibly contaminated with noise and iii) stabilize the flow without setting a value for the bottom pressure. An algorithm has been proposed which does not fix any reference(More)
A methodology is proposed for production optimization of oilfields consisting of multiple offshore reservoirs. In such complex systems, several production units are interconnected by a subsea pipeline network that transfers fluids to onshore terminals. A graph-based model of production units, pipelines, and nonlinear phenomena leads to a mixed-integer(More)
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