TY - JOUR

T1 - A method of determination of the gas condensate composition based on the well test data and optimization algorithms

AU - Старовойтова, Ботагоз Николаевна

AU - Имомназаров, Бунёд Холматжонович

AU - Байкин, Алексей Николаевич

N1 - Starovoytova B.N., Imomnazarov B.Kh., Baykin A.N. A method of determination of the gas condensate composition based on the well test data and optimization algorithms // Georesursy.-2026.-V.28, № 2. - doi: https://doi.org.10.18599/grs.2026.2.3 This work was supported by the Ministry of Science and Higher Education of the Russian Federation (Project No. FSUS-2025-0016), and the Gazpromneft-NSU Scientific and Educational Center. The authors thank Rock Flow Dynamics for providing an academic license for the tNavigator simulator.

PY - 2025

Y1 - 2025

N2 - 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 mixing 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 carried out for compliance with the gamma distribution of the obtained heavy component fractions relative to the molecular weight. The approach is verified for a synthetic case with a known reservoir composition. For detailed 34-component “lean”' sample model, the scalar parameter approach accurately reproduces such key PVT properties as the dew point pressure and condensate dropout curve from constant-volume depletion test. 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 is reliable if the error in noisy data does not exceed 10% relative to actual GCR.

AB - 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 mixing 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 carried out for compliance with the gamma distribution of the obtained heavy component fractions relative to the molecular weight. The approach is verified for a synthetic case with a known reservoir composition. For detailed 34-component “lean”' sample model, the scalar parameter approach accurately reproduces such key PVT properties as the dew point pressure and condensate dropout curve from constant-volume depletion test. 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 is reliable if the error in noisy data does not exceed 10% relative to actual GCR.

U2 - https://doi.org/10.18599/grs.2026.2.3

DO - https://doi.org/10.18599/grs.2026.2.3

M3 - Article

VL - 28

JO - Георесурсы

JF - Георесурсы

SN - 1608-5043

IS - 2

ER -