| dc.description.abstract | As the use of subsea processing increases and technology advances, subsea layout possibilities and configurations diversify and get more complex, causing manual design processes to be cumbersome and often suboptimal. Besides, most subsea layout and processing equipment design is done when there are still significant uncertainties (e.g. cost, oil price, reserves), which makes it challenging to define an optimal solution.
This thesis explores the use of model-based optimization and probabilistic analysis to provide decision support to field planners when designing offshore oil and gas systems. It can be divided into two parts: the first provides methods to determine main aspects of the field (e.g. production profile and drilling schedule) during early phases of field development, when there are considerable uncertainties. The second part of this thesis proposes methods to optimize subsea layout considering subsea processing, which is typically neglected.
To address the problem in the first part of this thesis, a deterministic non-linear model from the literature was taken as basis and optimizations were performed to determine the number of wells and plateau rate that maximize net present value considering varying levels of uncertainties. The uncertainties considered were oil price, oil in place, and well productivity. Two optimization methods were tested, one exact and one heuristic.
For the second part of the thesis, a mixed integer non-linear programming model is used to find optimal subsea layouts considering subsea processing. The model considers variation in reservoir deliverability and operating pressure for subsea equipment, besides estimating equipment cost, reliability and maintenance aspects, and pressure drop in discharge piping. A hybrid method is proposed to tackle large instances of the problem, combining a genetic algorithm for solving integer variables with a nested non-linear optimization for continuous variables. | en_US |
