A fundamental step towards accurate reservoir characterization and hydrocarbon in-place estimation involves correct fluid type identification. Precise modeling is a challenge for gas condensates due to their complex near-critical behavior. Moreover, this becomes an uphill task when commingled production from a compartmentalized reservoir is going on. This work presents a data-driven workflow to solve the fluid type identification challenge, for a compartmentalized and layered field that was previously characterized as having shallow gas condensate and deeper oil layers. Consequently, a single, robust and coherent EOS model was obtained to successfully predict field-wide hydrocarbon properties. Fluid analysis of seven different separator samples of five formations from three wells were analyzed in this study. The development and implementation of the new workflow includes multiple stages. An extensive data analysis is carried out, which includes compositional comparative analysis of the samples, comparison between lab and producing gas-oil-ratios, and establishment of compositional grading utilizing key PVT parameters. Next, a decision tree methodology was utilized to prioritize samples based on lab adjustments along with saturation pressure and reservoir pressure comparison and apply the compositional gradient approach on the most representative dataset. Finally, a common EOS model for the field was developed, tuned, and validated through quality checks by a simulation model. Comparative compositional analysis helped in rectifying PVT lab's fluid classification for one of the samples based on C7+ composition. Evidence of compositional variation with depth is observed; an increase in C7+ percentage and reduction in API shows heavier components in the fluid column from top to bottom. The high-priority data points exhibited a gradient without contact (representative of near-critical fluids), in agreement with the comparative analysis. Furthermore, about 30% liquid dropout for a pressure change of 200-300 psi for the oil sample is also pointing towards a near-critical system. The compositional gradient modeling enabled a single coherent solution to be applied, which accurately predicts both oil and gas properties. Finally, the EOS model was tested and validated through a simulation model-based workflow whose results are consistent with the measured lab parameters. This final test was important, as there was always the alternative hypothesis: a gas-condensate underlain by a volatile oil with a distinct GOC. Some of the data points were consistent with this hypothesis, and it was not immediately obvious as to which type of fluid existed in the reservoir. Other evidence included unusual GOR versus time behavior, which was inconsistent with a sharp GOC. The resulting PVT model enabled the prediction of gas vs. condensate fluids field-wide for a highly complex fluid system. An attempt to unravel the enigma through integrated data analysis has helped in providing a new perspective of near-critical fluid system for field.

Gas-condensate reservoirs exhibit complex multi-phase flow behavior below saturation pressures resulting from compositional variations. Below saturation pressure, condensate starts dropping-out within the reservoir and eventually develops a bank in the near wellbore region; effectively reducing the relative permeability to gas flow. As a result, production of main phase (gas) decreases with development of this bank while condensate is left behind in the reservoir. Since, reserves estimation is a part of the development plans of all fields, it is important to account for condensate drop-out while deriving a condensate recovery factor during the initial stage of the life of a reservoir. Otherwise a high condensate recovery factor (if assumed equal to or close to that of gas phase) might result in unrealistic optimistic cash flow projections. Given the importance of condensate recovery factor estimation, a data driven analytical method has been developed and will be discussed in detail in this paper; while overcoming the limitation of available data for green fields. An analytical methodology has been developed for calculation of condensate recovery factor in gas condensate reservoirs in the absence of a constant volume depletion (CVD) study laboratory report. The method uses either a linear or a non-linear correlation-based relationship between condensate drop-out and pressure. This enables derivation of an equation for calculation of condensate recovery factor while incorporating critical parameters such as dew point pressure, initial and abandonment pressures and condensate gas ratios (CGRs) at initial and abandonment conditions. A stepwise procedure will be described to derive all these parameters with limited available data in the early stages of the life of reservoir. The methodology adopted was applied to more than 30 green and brown gas condensate fields with varying CGR's. For a few mature fields, results derived from the proposed methodology were compared with recovery factors estimated from numerical simulation models. Results of both approaches appeared to be in a good agreement with an accuracy of +/−10%. As expected, recovery factors derived assuming a simple linear decline of CGR with pressure were less accurate than those calculated using a non-linear correlation-based decline of CGR with pressure. For richer gas condensate fields with high compressibility factors, the z-factor was incorporated with pressure in the form of P/z, which resulted in a more accurate estimation of recovery factors. A comparison has been made where recovery factors are estimated from the described approach and the other reference approaches; and conclusions drawn from these comparisons are discussed. Various approaches have been used historically for condensate recovery factor estimation while relying heavily on a good amount of data available. A simple analytical approach has been discussed which mitigates the impact of poor data availability especially in green fields and yet can yield useable condensate recovery factor calculations. This enables better reserve estimation of condensates in gas condensate reservoirs in early stages of life of reservoirs, while leading to efficient and realistic field development plans in futures.