IMMISCIBLE VISCOUS FINGERING MODELING OF TERTIARY POLYMER FLOODING BASED ON REAL CASE OF HEAVY OIL RESERVOIR MODEL

B. M. O. Silveira, V. Botechia, D. J. Schiozer, A. Skauge
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Abstract

In immiscible displacements, lower viscosity injected fluids with higher mobility than crude oil can create viscous fingers, affecting displacement efficiency. The Buckley–Leverett approach for relative permeabilities (kr) may not represent accurately 2D features like increased water saturation in viscous fingering. Based on the physics issue, this work applies Sorbie's 4-Steps methodology to a 3D simulation of an offshore heavy oil reservoir focusing on waterflooding and tertiary polymer flooding, assessing their impact on oil production forecasts. It also explores the application of this methodology to coarse grid simulation models, employing pseudo kr functions by data assimilation. During tertiary polymer injection, two processes were identified in oil displacement: viscous crossflow mechanism and oil bank mobilization by a second finger. This combination resulted in earlier and increased oil production. For both strategies, refining the grid increased simulation runtime from minutes to days compared to coarse grids, making it impractical for intensive processes. From data assimilation, the best solution with matched field indicators reduced runtime from days to minutes. This study expanded the 4-Steps methodology for 3D reservoir simulation, proposing kr as uncertainties. Data assimilation enhances the methodology, generating pseudo kr for coarser grid simulations, reducing computational costs, and capturing small-scale phenomena.
基于重油油藏模型实际案例的三次聚合物水淹不相溶粘指模
在不相溶位移中,比原油流动性更高的低粘度注入流体会产生粘指,影响位移效率。相对渗透率(kr)的 Buckley-Leverett 方法可能无法准确反映二维特征,如在粘指过程中水饱和度的增加。基于物理学问题,这项工作将索比四步方法应用于海上重油储层的三维模拟,重点是注水和三次聚合物注水,评估它们对石油产量预测的影响。研究还探讨了如何将该方法应用于粗网格模拟模型,并通过数据同化采用伪 kr 函数。在三次聚合物注入过程中,确定了石油位移的两个过程:粘性横流机制和第二根手指调动油库。这两个过程的结合使石油产量提前并增加。对于这两种策略,与粗网格相比,细化网格会将模拟运行时间从几分钟增加到几天,因此对于密集流程来说并不实用。通过数据同化,最佳解决方案与匹配的实地指标将运行时间从几天缩短到几分钟。这项研究扩展了三维储层模拟的 4 步方法,提出了 kr 作为不确定因素。数据同化增强了该方法,为更粗的网格模拟生成了伪 kr,降低了计算成本,并捕捉到了小尺度现象。
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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