Abstract

The Permanent Downhole Gauge (PDG) is a pressure and temperature sensor located subsea near the perforation point in the offshore oil production system. This sensor is very useful in operation problems detection (fouling, valves, plugging, etc.), multiphasic flow analysis, production tests adjustment, control strategy and model identification. Given its location under hazardous conditions, it may come to failure or imprecisions in measurement. The high costs related to the maintenance make it infeasible to perform. To overcome this issue, a methodology based on digital signal processing (DSP) is proposed in order to design a low pass digital filter aiming the PDG pressure reconstruction. The proposed methodology requires no plant model, low computational cost and only the Christmas-Tree pressure measurement. The proposed methodology is compared with two commonly employed black-box methodologies: Neural Network and Transfer Function Estimation. The results show the success of the designed filter and the advantages over the black-box approaches. The estimation of the PDG pressure from three operating wells using real process data was performed.

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