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A system-theoretical approach to selective grid coarsening of reservoir models

机译:系统理论的储层模型选择性网格粗化

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From a system-theoretical point of view and for a given configuration of wells, there are only a limited number of degrees of freedom in the input output dynamics of a reservoir system. This means that a large number of combinations of the state vari ables (pressure and saturation values) are not actually controllable and observable from the wells, and accordingly, they are not affecting the input-output be havior of the system. In an earlier publication, we there fore proposed a control-relevant upscaling methodol ogy that uniformly coarsens the reservoir. Here, we present a control-relevant selective (i.e. non-uniform) coarsening (CRSC) method, in which the criterion for grid size adaptation is based on ranking the grid block contributions to the controllability and observability of the reservoir system. This multi-level CRSC method is attractive for use in iterative procedures such as computer-assisted flooding optimization for a given configuration of wells. In contrast to conventional flow based coarsening techniques our method is indepen dent of the specific flow rates or pressures imposed at the wells. Moreover the system-theoretical norms employed in our method provide tight upper bounds to the 'input-output energy' of the fine and coarse systems. These can be used as an a priori error-estimate of the performance of the coarse model. We applied our algorithm to two numerical examples and found that it can accurately reproduce results from the correspond ing fine-scale simulations, while significantly speeding up the simulation.
机译:从系统理论的角度来看,对于给定的井配置,储层系统的输入输出动力学中只有有限数量的自由度。这意味着状态变量的许多组合(压力和饱和度值)实际上是不可控制的,无法从井中观察到,因此,它们不会影响系统的输入输出行为。在较早的出版物中,我们提出了一种与控制相关的放大方法,该方法可以使储层均匀地变粗。在这里,我们提出了一种与控制有关的选择性(即非均匀)粗化(CRSC)方法,其中网格大小自适应的标准基于对网格块对储层系统可控性和可观测性的贡献进行排名。这种多层CRSC方法非常适合用于迭代过程中,例如针对给定井眼配置的计算机辅助驱油优化。与常规的基于流量的粗化技术相比,我们的方法与施加在井上的特定流量或压力无关。此外,我们的方法中采用的系统理论规范为精细和粗糙系统的“输入-输出能量”提供了严格的上限。这些可以用作粗略模型性能的先验误差估计。我们将我们的算法应用于两个数值示例,发现该算法可以准确地重现对应的精细规模仿真的结果,同时显着加快了仿真速度。

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