Shahriar Osfouri

Gas condensate reservoirs are characterized by a distinctive retrograde behavior and potential for condensate drop out during production and sampling. Efficient modeling of gas condensate reservoir requires careful phase behavior studies of samples collected prior to and during the production life of reservoir. In this work, an integrated characterization and tuning algorithm is proposed to analyze the pressure-volume-temperature (PVT) behavior of gas condensate samples. Each characterization and tuning scenario is described by a “path” which specifies the class of fluid, splitting and lumping (if any), the type of correlation, and grouping strategy (static or dynamic). Different characterization approaches were tested for the effective description of heavy end. Meanwhile, dynamic and static strategies were implemented to tune the equation of state (EOS) through non-linear regression. The optimum combination of characterization and tuning approach was explored for each sample by a rigorous analysis of the results. It was found out that the exponential distribution function gives the best performance for heavy end characterization in a dynamic tuning strategy. Also, analyses indicate that using higher single carbon number may not necessarily make EOS tuning more accurate. In addition, the optimum step is reached in either the third or fourth step for most cases in a dynamic tuning approach, and is sensitive neither to the characterization path nor to the selected end carbon number.