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andreasen2019.pdf (1.98 MB)
Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
In this article the optimization of a realistic oil and gas separation plant has
been studied. Two different fluids are investigated and compared in terms of
the optimization potential. Using Design of Computer Experiment (DACE) via
Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate
models using Kriging have been established for selected model responses. The
surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the
recoverable oil production. A total of 10 variables representing pressure and
temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a
constraint function is included to ensure that the optimal solution allows export
of oil with an RVP < 12 psia. The main finding is that, while a high pressure is
preferred in the first separation stage, apparently a single optimal setting for the
pressure in downstream separators does not appear to exist. In the second stage
separator apparently two different, yet equally optimal, settings are revealed.
In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of
pressure and temperature yields equally optimal results.