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Influence of the polymer properties and numerical schemes on tertiary oil recovery processes

机译:聚合物性质和数值方案对三次采油工艺的影响

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摘要

Chemical Enhanced Oil Recovery (EOR) processes comprise a number of flooding techniques aimed at increasing the operational life of mature oilfields. Among these, polymer flooding is one of the most developed; its functionality is to increment the aqueous viscosity, avoiding the formation of viscous fingering. Reservoir simulators consider this influence as well as other physical properties (e.g., adsorption, permeability reduction). However, the polymer degradation is usually not considered even though it plays a critical role in the viscosity. In this paper this mechanism is analyzed and coupled with the previously mentioned physical phenomena in order to present a complete study of their influence in the EOR process. Moreover, since a fully second-order accuracy scheme is used along with a Total Variation Diminishing (TVD) flux-limiting function, the influence of the latter on the recovery factor is also discussed. Results showed that the negative effect of the polymer adsorption was the most relevant physical phenomenon in terms of the oil recovery. Furthermore, the analysis of the discretization of the differential equations showed that traditional, linear first-order schemes created numerical diffusion affecting negatively the macroscopic sweeping efficiency, which disappeared when TVD techniques were used. Reservoir simulators allow determining the desired designing properties for future polymers in relationship with the characteristics of the oilfield to be exploited. (C) 2019 Elsevier Ltd. All rights reserved.
机译:化学强化采油(EOR)工艺包括多种驱油技术,旨在延长成熟油田的使用寿命。其中,聚合物驱是最先进的驱油技术之一。它的功能是增加水性粘度,避免形成粘性指状。储层模拟器考虑了这种影响以及其他物理性质(例如,吸附,渗透率降低)。但是,即使聚合物降解在粘度中起关键作用,通常也不会考虑。在本文中,对该机理进行了分析,并与前面提到的物理现象相结合,以便对它们在EOR过程中的影响进行完整的研究。此外,由于使用了完全二阶精度方案以及总变化量减小(TVD)流量限制功能,因此还讨论了后者对恢复因子的影响。结果表明,就油采收率而言,聚合物吸附的负面影响是最相关的物理现象。此外,对微分方程离散化的分析表明,传统的线性一阶方案产生了数值扩散,从而对宏观波及效率产生了负面影响,而当使用TVD技术时,这种影响就消失了。储层模拟器可以根据所开发油田的特性确定未来聚合物所需的设计性能。 (C)2019 Elsevier Ltd.保留所有权利。

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