TITLE:
Rapid PVT Model Improvement Using Risk Analysis Technique in Gas Condensate Reservoirs
AUTHORS:
Seyyed Mahdia Motahhari, Amir Bagherian, Mohammad Amin Safarzadeh
KEYWORDS:
Gas Condensate Reservoir, Fluid Model, Risk Analysis, PVT Model, Residual Mean Square
JOURNAL NAME:
Open Access Library Journal,
Vol.11 No.12,
December
12,
2024
ABSTRACT: A realistic model of reservoir fluid samples is essential before conducting reservoir simulations, especially for gas condensate reservoirs. Errors in PVT modeling typically stem from three main sources: fluid sampling, fluid property measurement, and fluid model construction. This work presents a rapid method for constructing a more realistic PVT fluid model before tuning. Three fluid samples from different Iranian gas condensate reservoirs were selected to achieve this. A suitable equation of state (EOS) and appropriate correlations for key factors like critical pressure, temperature, acentric factor, and binary interaction coefficients were chosen using sensitivity and risk analysis techniques. The optimal default selection of a PVT model produces a representative model of the real fluid sample with minimal variation in variables when matching laboratory data. This approach is applicable to various PVT modeling packages. Before model optimization, a base model is selected based on literature and experience. The sensitivity and risk analysis technique uses the residual mean square (RMS) error as the objective function. The results of this work indicate that a significant number of models constructed using the derivative method had lower RMS errors compared to the base model. The risk analysis technique is shown to provide the best default selection for the PVT fluid model. While some approaches in the literature recommend using specific EOS and correlations for gas condensate samples, the results of this work show that the interaction effect of PVT model variables leads to the best combination of EOS and correlations for each PVT sample. This approach can be extended to improve the PVT modeling process.