Reservoir simulation

Machine Learning Upscales Realistic Discrete Fracture Simulations

This paper analyzes several configurations of convolutional neural networks suited for predicting upscaled fracture permeabilities and shape factors required to close a dual porosity/dual permeability model.

The components of the reference upscaled fracture permeability tensor and the shape factor
The components of the reference upscaled fracture permeability tensor and the shape factor, overlaid on the fracture traces (red lines) for anisotropic test case.

Upscaling of discrete fracture networks to continuum models such as the dual-porosity/dual-permeability (DP/DP) model is an industry-standard approach in modeling fractured reservoirs. In the complete paper, the author parametrizes the fine-scale fracture geometries and assesses the accuracy of several convolutional neural networks (CNNs) to learn the mapping between this parametrization and DP/DP model closures. The accuracy of the DP/DP results with the predicted model closures was assessed by a comparison with the corresponding fine-scale discrete fracture matrix (DFM) simulation of a two-phase flow in a realistic fracture geometry. The DP/DP results matched the DFM reference solution well.

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