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A Robust Embedded Discrete Fracture Modeling Workflow for Simulating Complex Processes in Field-Scale Fractured Reservoirs

机译:一种强大的嵌入离散断裂建模工作流程,用于模拟现场裂缝储层中的复杂过程

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Traditionally, fractured reservoir simulations use Dual-Porosity, Dual-Permeability (DPDK) models that can idealize fractures and misrepresent connectivity. The Embedded Discrete Fracture Modeling (EDFM) approach improves flow predictions by integrating a realistic fracture network grid within a structured matrix grid. However, small fracture cells with high conductivity that pose a challenge for simulators can arise and ad hoc strategies to remove them can alter connectivity or fail for field-scale cases. We present a new gridding algorithm that controls the geometry and topology of the fracture network while enforcing a lower bound on the fracture cell sizes. It honors connectivity and systematically removes cells below a chosen fidelity factor. Furthermore, we implemented a flexible grid coarsening framework based on aggregation and flow-based transmissibility upscaling to convert EDFMs to various coarse representations for simulation speedup. Here, we consider pseudo-DPDK (pDPDK) models to evaluate potential DPDK inaccuracies and the impact of strictly honoring EDFM connectivity via Connected Component within Matrix (CCM) models. We combine these components into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications. We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models. The coarse simulation results show biases consistent with the underlying assumptions (e.g., pDPDK can over-connect fractures). The preservation of fracture connectivity via the CCM aggregation strategy provides better accuracy relative to the fine EDFM forecast while maintaining computational speedup. We then demonstrate the robustness of the proposed EDFM workflow for practical studies through application to an improved oil recovery (IOR) study for a fractured carbonate reservoir. Our automatable workflow enables quick screening of many possibilities since the generation of full-field grids (comprising almost a million cells) and their preprocessing for simulation completes in a few minutes per model. The EDFM simulations, which account for complicated multiphase physics, can be generally performed within hours while coarse simulations are about a few times faster. The comparison of ensemble fine and coarse simulation results shows that on average, a DPDK representation can lead to high upscaling errors in well oil and water production as well as breakthrough time while the use of a more advanced strategy like CCM provides greater accuracy. Finally, we illustrate the use of the Ensemble Smoother with Multiple Data Assimilation (ESMDA) approach to account for field measured data and provide an ensemble of history-matched models with calibrated properties.
机译:传统上,裂缝储层模拟使用双孔隙度,双渗透率(DPDK)模型,可以理解裂缝和畸形的连接。嵌入的离散断裂建模(EDFM)方法通过在结构化矩阵网格内积分逼真的裂缝网络网格来改善流预测。然而,具有高导电性的小型骨折细胞,对模拟器构成挑战,并且可以出现和临时策略来消除它们可以改变连接或失败的现场尺度案例。我们提出了一种新的网格算法,用于控制骨折网络的几何形状和拓扑,同时在裂缝细胞尺寸上执行下限。它授予连接,系统地去除所选择的保真因子以下的细胞。此外,我们通过基于聚合和基于流的传输性来实现灵活的网格粗细框架,从而将EDFM转换为用于模拟加速的各种粗略表示。在这里,我们考虑伪DPDK(PDPDK)模型来评估潜在的DPDK不准确性以及严格纪念EDFM连接通过矩阵(CCM)模型内的连接组件的影响。我们将这些组件结合到实际工作流程中,可以有效地从成千上万的地质逼真自然骨折的随机实现来有效地生成升高的EDFM,用于集合应用。我们首先考虑一个简单的水机般的例子,以说明我们的骨折上升,以获得粗(PDPDK和CCM)模型。粗仿真结果显示与潜在的假设一致的偏差(例如,PDPDK可以过度连接骨折)。通过CCM聚合策略保存裂缝连通性,可在维护计算加速的同时相对于精细EDFM预测提供更好的准确性。然后,我们展示了拟议的EDFM工作流程的稳健性,通过应用于改进的碳酸盐储层改善的储存(IOR)研究。我们的自动工作流程可以快速筛选许多可能性,因为全场网格(包括近百万个电池)和它们的预处理进行了预处理,在每模型几分钟内完成。考虑到复杂的多相物理的EDFM模拟,通常可以在小时内完成,而粗略模拟速度速度速度较快。集合细分和粗略仿真结果的比较表明,平均而言,DPDK表示可以导致井油和水生产中的高升高误差,以及使用比如CCM等更高级策略的突破时间提供更大的准确性。最后,我们说明了与多个数据同化(ESMDA)方法的使用,以解释现场测量数据,并提供具有校准属性的历史匹配模型的集合。

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