Simulation Train Design and Optimization in Offshore Oil and Gas Production facilities: Simulation train design
Translated title
Simulation Train Design and Optimization in Offshore Oil and Gas Production facilities: Optimization in offshore oil and gas production facilities
Author
Rani Nath, Suborna
Term
4. term
Education
Publication year
2022
Submitted on
2022-09-29
Pages
78
Abstract
Formålet var at opbygge en simulationsmodel af et flertrins separatoranlæg for olie og gas og undersøge, hvordan ændringer i driftstryk og -temperatur påvirker olie- og gasstrømning og indtjening. Processen blev simuleret i Aspen HYSYS (et proces-simuleringsværktøj). Resultaterne viser, at højere tryk i første og andet separationstrin mindsker olieproduktionen og øger gasproduktionen, mens lavere temperatur i disse trin øger olieproduktionen. Modellen fungerer som et optimeringsværktøj til at vælge driftstryk og -temperaturer for et offshore separatoranlæg, så profitten fra olie- og gassalg maksimeres under givne begrænsninger. Til optimeringen blev der anvendt responsflade-metode (RSM) ved at opbygge en multipel lineær regressionsmodel (MLR) for de udvalgte responser. Denne model identificerede et optimalt driftspunkt ved P1 = 38.08 bar, T1 = 54.09℃, P2 = 18.00 bar og T2 = 40.56℃ med en forudsagt profit på 0.208 milli. $/day.
The goal was to build a simulation model of a multi-stage oil and gas separation system to examine how operating pressure and temperature affect oil and gas flow rates and profitability. The process was simulated in Aspen HYSYS (process simulation software). The results show that increasing the pressure in the first and second separation stages reduces oil production and increases gas production, while lowering the temperature in those stages increases oil production. The model serves as an optimization tool to select operating pressures and temperatures for an offshore separation train to maximize profit from oil and gas sales under given constraints. For the optimization, Response Surface Methodology (RSM) was applied by constructing a Multiple Linear Regression (MLR) model for the chosen responses. This model identified an optimal operating point at P1 = 38.08 bar, T1 = 54.09℃, P2 = 18.00 bar, and T2 = 40.56℃, with a predicted profit of 0.208 milli. $/day.
[This summary has been rewritten with the help of AI based on the project's original abstract]
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