Prediction of the gas–condensate well productivity

A semi-implicit, equation of state (EOS)-based compositional, and one-dimensional radial reservoir simulator was developed for well-test data interpretation and performance prediction for a single gas–condensate well. An implicit-pressure/explicit-composition numerical solution approach was used for simplicity and adaptability. The Peng–Robinson equation of state (PR-EOS) was used to model the fluid-phase behavior, and volumetric predictions were improved by considering shift factors. The model was used to design a comprehensive methodology for field application of a compositional simulator, which consists of four steps: parametric study, well-test design, well-test interpretation, and productivity forecasting. The non-Darcy effects were studied by analyzing the relative influence of inertial and drag forces in systems controlled by the viscous and capillary forces. A generalized equation to estimate relative inertial resistance was developed and incorporated into the model. At relatively low gas-flow rates, the effects of high viscous-to-capillary force ratio dominate the non-Darcy effects by reducing the condensate saturation. The drag force effect was considered in a gas–condensate flow study and no experimental data are available for testing of these systems. When the pressure drop does not generate an appreciable liquid saturation, below 40%, drag force effects are found negligible in the near-wellbore fluid flow. At high condensate saturation (So>50%), drag force effects reduce liquid saturation, improving gas–condensate well performance. A technique for well-test interpretation was also introduced, which is based on simulator output data for analysis of a field pressure-transient data. It was shown that current techniques can be applied for determination of gas–condensate well performance by means of the pseudo-pressure and pseudo-time functions, as defined in this study.