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Accurate Modeling of Faults by Multipoint, Mimetic, and Mixed Methods

机译:通过多点,模拟和混合方法对故障进行精确建模

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The predominant way of modeling faults in industry-standard flow simulators is to introduce so-called transmissibility multipliers in the underlying two-point discretization. Although this approach provides adequate accuracy in many practical cases, two-point discretizations are only consistent for K-orthogonal grids and may introduce significant discretization errors for grids that severely depart from being K-orthogonal. Such grid-distortion errors can be avoided by lateral or vertical stair-stepping of deviated faults at the expense of errors in the geometrical fault description. In other words, modelers have the choice of either making (geometrical) errors by adapting faults to a grid that is almost K-orthogonal, or introducing discretization errors because of the lack of K-orthogonality if the grid is adapted to deviated faults. We propose a method for accurate description of faults in solvers based on a hybridized mixed or mimetic discretization, which also includes the MPFA-O method. The key idea is to represent faults as internal boundaries and calculate fault transmissibilities directly instead of using multipliers to modify grid-dependent transmissibilities. The resulting method is geology-driven and consistent for cells with planar surfaces and thereby avoids the grid errors inherent in the two-point method. We also propose a method to translate fault transmissibility multipliers into fault transmissibilities. This makes our method readily applicable to reservoir models that contain fault multipliers.
机译:在行业标准的流量模拟器中,对故障进行建模的主要方法是在基础的两点离散化中引入所谓的传递系数。尽管此方法在许多实际情况下都提供了足够的精度,但是两点离散化仅对于K正交网格是一致的,并且可能会为严重偏离K正交的网格引入明显的离散化误差。可以通过偏向断层的横向或垂直阶梯来避免这种网格变形错误,但要以几何故障描述中的错误为代价。换句话说,建模者可以选择通过使故障适应几乎为K正交的网格来产生(几何上的)错误,或者如果网格适合于偏离的断层,则由于缺乏K正交性而引入离散化错误。我们提出了一种基于混合的或模拟的离散化方法来准确描述求解器中的故障的方法,该方法还包括MPFA-O方法。关键思想是将故障表示为内部边界,并直接计算故障的透射率,而不是使用乘数来修改与电网有关的透射率。生成的方法是由地质驱动的,并且对于具有平坦表面的单元格是一致的,从而避免了两点方法固有的网格误差。我们还提出了一种将故障传递系数乘以故障传递系数的方法。这使得我们的方法很容易适用于包含故障乘数的油藏模型。

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