| dc.description.abstract | Subsea petroleum extraction systems may be large and complex, and many decisions affect the production. Maintaining high production levels is not a trivial task. As decisions are made based on available information and experience, better decisions come with better information. Decision support tools may provide essential information to achieve better production levels.In this master thesis, different methods are proposed as decision support tools. The aim is to increase the production from a part of a subsea production system, consisting of a manifold with seven producing wells and two flowlines, given certain system constraints. The methods are based on well models and numerical optimization, and both static and dynamic optimization is considered. The well models are non-linear, and binary decisions are also present. The problems that arise are complex MINLP problems, and are solved by combining brute force , Branch & Bound , and a nonlinear solver. The solution of the problems is implemented in MATLAB, and tested on predefined test scenarios, with no, little or extensive dynamics present. The performance is assessed by simulations, and by calculating the resulting average production.It was found that static optimization to decide the well settings, such as valve openings and flowline routing, has a great potential to increase the oil production from the system. The results when applying a dynamic approach to the system were not conclusive, but the methods proposed showed no indications of any major performance increase, relative to applying only static optimization. | nb_NO |
