The EOS has been widely applied to characterize reservoir fluids. On the other hand, an EOS is generally not predictive without tuning its parameters to match relevant experimental data. Since the manual EOS-tuning process is artistic and strongly dependent on an engineer's past experience, different engineers may get completely different results and few engineers could obtain the satisfactory results without certain tries and guesses. Problems include what the minimum number of components should be used, how the original system should be lumped, how many and what kinds of regression variables should be included into the regression variable set, and so on. This paper presents a fully automatic technology to efficiently perform the reservoir fluid characterization. A method to handle multiple fluid samples, a fully automatic tuning procedure, a best lumping algorithm, and the fully automatic determination of the minimum number of components will be described. Some applications will be summarized.

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