Multiple EOS Fluid Characterization for Modeling Gas Condensate Reservoir with Different Hydrodynamic System: A Case Study of “S” Field
Sugiyanto Bin Suwono, Luky Hendraningrat, Dwi Hudya Febrianto, Bagus Nugroho, Taufan Marhaendrajana
A proper analysis and fluid characterization is an essential key for successful modeling the behaviour of gas condensate reservoir. This paper demonstrates a robust multiple equation of state (EOS) modeling process for gas condensate reservoir at S field. S is a new major gas condensate field in East Indonesia with estimated IGIP greater than 2 Tcf and CGR range from 3-80 STB/MMscf. S field is divided into two structures: the northern part is a carbonate reefal build-up, namely Mentawa, member of Minahaki formation, and the southern part is a platform carbonate Minahaki formation. The hydrodynamic condition in both formations poses a challenge to fluid characterization, where Mentawa member has both oil and gas with active aquifer, while Minahaki formation only has gas bearing rock with aquifer.
Senoro field has collected 36 samples, measured from down-hole and surface. The samples also cover composition analysis for surface recombined fluid. The required laboratory experiment such as CCE, DL and CVD have also been measured. The mathematical recombination was performed as a quality check to measure well-stream composition.
Two EOS models have been developed successfully to determine physical properties and to predict the fluid behaviour of S Field. The heptanes-plus fraction is split into three pseudo-components to characterize fluid using Gamma distribution model. The fine-tuned fluid properties from all available data match both EOS models satisfactorily. These EOS models have also been matched with historical single radial welltest model. Compositional grading has also been developed to generate compositional map.
These established EOS models are used for compositional simulation. The gas and condensate profiles now could be predicted for optimizing field development plan. The use of EOS models can lead not only to a further field development strategy, but also to optimize the surface processing facilities.
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