Determining Gas Condensate Composition Using Well Test Data and Optimization Algorithms

This study proposes an optimization-based approach to determine the actual fluid composition of gas condensate reservoirs when obtaining representative samples are impossible. The method incorporates the well tests hydrodynamic modeling, laboratory analyses of non-representative lean samples, and field data, including the gas-condensate ratio (GCR). The reservoir composition is assumed to be a linear combination of lean gas and its equilibrium condensate. The proportionality (mixing) parameter is obtained by minimizing the discrepancy between observed and simulated GCR values obtained using tNavigator. Two variants are considered: 1) a scalar parameter, corresponding to mixing of equilibrium gas and condensate; 2) a vector-valued mixing parameter, permitting per-component adjustment for improved accuracy. For the vector mixing parameter, a check is performed for compliance with the gamma distribution of the obtained heavy component fractions relative to their molecular weight. The approach is verified for a synthetic case with a known reservoir composition. For a detailed 34-component “lean”’ sample model, the scalar parameter approach accurately reproduces key PVT properties such as the dew point pressure and condensate dropout curve from constant-volume depletion tests. Reduced-component fluid models require the vector-valued mixing parameter to achieve comparable accuracy. To evaluate robustness against field uncertainties, Gaussian noise is introduced into the actual GCR data. Numerical experiments confirm that the method remains reliable if the error in noisy data does not exceed 10% relative to actual GCR.

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