Fluid Property Prediction with Unified Equation of State in a Compositional Surface Network Comprising 5000+ Wells

K. Mogensen, Jyotsna Asarpota, Y. Bansal
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引用次数: 2

Abstract

ADNOC has embarked on the second phase of its ambitious integrated capacity model (ICM) project with the overall aim to optimise its fluid production portfolio from the well level to the processing facilities. A key feature of the new software tool is the ability to track and predict fluid properties over time across the entire production network, comprising thousands of wells and a myriad of pipelines. The reservoir fluid composition is assigned at well level for each producing reservoir. The compositional tracking over time is straightforward for many wells, but complicating factors do arise, such as Lateral compositional variation related to complex reservoir charging history Vertical compositional gradients, especially for near-critical fluids The presence of initial and secondary gas caps, resulting in gas coning Injection of miscible gas for enhanced oil recovery The fluid systems range from medium-API oil to gas condensates and the key chemical components vary as follows: C1 [5-80%], CO2 [0.5-8%], and H2S [0-35%]. Mixing of pressurized fluids with different compositions at various junctions in the network requires a robust thermodynamics model to capture the associated variation in fluid properties, particularly density and viscosity as a function of pressure and temperature. We demonstrate that it is possible to constrain one unified equation of state applicable to all fluids, as long as the fluid systems used for the tuning span the entire range of compositions observed. Mixing of fluid streams is computationally much simpler if each stream is made up of the same components (although in different amounts) with the same component properties. On average, the predicted fluid density is within 1% of the measured value from a multi-stage separator test.
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用统一状态方程预测5000多口井组成的地面网络中的流体性质
ADNOC已经开始了其雄心勃勃的综合产能模型(ICM)项目的第二阶段,其总体目标是从井水平到处理设施优化其流体生产组合。新软件工具的一个关键特点是能够跟踪和预测整个生产网络(包括数千口井和无数管道)的流体特性。油藏流体成分在每个生产油藏的井位上进行分配。对于许多井来说,随着时间的推移,成分跟踪是简单的,但也会出现复杂的因素,例如与复杂的储层注注历史相关的横向成分变化,垂直成分梯度,特别是对于接近临界的流体,初始和二次气顶的存在导致气体锥进,注入混相气体以提高采收率。流体系统的范围从中等api的石油到天然气凝析油,其关键化学成分如下:C1 [5-80%], CO2 [0.5-8%], H2S[0-35%]。在网络的不同节点混合不同成分的加压流体需要一个强大的热力学模型来捕捉流体性质的相关变化,特别是密度和粘度作为压力和温度的函数。我们证明,只要用于调谐的流体系统跨越所观察到的整个组成范围,就有可能约束一个适用于所有流体的统一状态方程。如果每一种流体都由具有相同组分性质的相同组分(尽管数量不同)组成,那么混合流体的计算就会简单得多。平均而言,预测的流体密度与多级分离器测试的实测值相差在1%以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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