Machine-Learning Upscales Realistic Discrete Fracture Simulations

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203962, “Upscaling of Realistic Discrete Fracture Simulations Using Machine Learning,” by Nikolai Andrianov, SPE, Geological Survey of Denmark and Greenland, prepared for the 2021 SPE Reservoir Simulation Conference, Galveston, Texas, 4–6 October. The paper has not been peer reviewed. 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. The DP/DP model also was significantly faster than DFM simulation. Introduction The goal of this study was to evaluate the effect of different CNN architectures on prediction accuracy for the DP/DP model closures and on the accuracy of DP/DP simulations in comparison with fine-scale DFM simulations. As a starting point, two CNN configurations were considered that have achieved breakthrough performance in image-classification tasks. The author adopted these architectures to the problem of learning the mapping between pixelated fracture geometries and the DP/DP model closures and indicated several key features in the CNN structure that are crucial for achieving high prediction accuracy. Mapping of fracture geometries requires significant effort, which limits the possibilities for creating large training data sets with realistic fracture geometries. The author, therefore, used the synthetic random linear fractures’ data set to train the CNNs and the fracture geometry from the Lägerdorf outcrop for testing purposes. It was demonstrated that an optimal CNN configuration yielded the DP/DP model closures such that the corresponding DP/DP results matched well the two-phase DFM simulations on a subset of the Lägerdorf data. The run times for the DP/DP model were a fraction of the time needed to accomplish the DFM simulations. Problem formulation is presented in a series of equations in the complete paper.