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Parametric Study of Enhanced Condensate Recovery of Gas Condensate Reservoirs using Design of Experiment

机译:通过实验设计对提高凝析气藏凝析率进行参数研究

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Gas condensate reservoirs usually exhibit reduced well productivity because of condensate dropout that occurs below the dew point pressure. Gas recycling has become one of the most favorable methods of improving recovery of condensed liquid. However, understanding the influence of different injection and reservoir parameters on productivity is of great importance when planning a gas recycling scheme. Traditional methods of sensitization during reservoir simulation for gas condensate fields creates the challenge of quick identification of the most critical properties for sensitization, and hence delay of overall simulation project delivery. This work aims at identifying the key variables that influence productivity of a gas condensate reservoir under a gas recycling scheme using the design of experiment approach (DOE). DOE represents a more effective method for computer-enhanced, systematic approach to experimentation, considering all the factors simultaneously. Identification of these parameters will help simulators achieve best optimization targets and also save time and resources during dynamic simulation projects. Furthermore, it will be shown that experimental design can be used to fit responses (condensate/gas production) to mathematical models that will be able to predict outputs for any given combination of variables.
机译:气体凝析油藏通常表现出井生产率的下降,这是因为凝结油的滴落低于露点压力。气体再循环已成为改善冷凝液回收率的最有利方法之一。但是,在计划气体回收计划时,了解不同的注入和储层参数对生产率的影响非常重要。气藏凝析气田储层模拟过程中的传统增敏方法给快速识别增敏作用的最关键特性带来了挑战,因此延迟了整个模拟项目的交付。这项工作旨在使用实验方法(DOE)的设计来确定影响气体再循环方案下影响凝析气藏产能的关键变量。 DOE是同时考虑所有因素的一种更有效的计算机增强系统实验方法。识别这些参数将帮助模拟器实现最佳优化目标,并在动态模拟项目中节省时间和资源。此外,将显示实验设计可用于使响应(冷凝水/气体产生)适应数学模型,该数学模型将能够预测任何给定变量组合的输出。

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