用双域深度学习加速高对比度油藏模拟中的基函数计算
Dual-Domain Deep Learning Method to Accelerate Local Basis Functions Computation for Reservoir Simulation in High-Contrast Porous Media
- 在频域和空间域同时提取渗透率特征，加速基函数生成
- 计算速度显著提升，精度与传统方法相当
- 适合需要快速模拟的油藏工程场景
在能源科学中，非均质多孔介质中的达西流是油藏模拟的核心问题。然而，此类介质显著的多尺度特性给传统数值方法带来了巨大的计算负担。混合广义多尺度有限元法（MGMsFEM）为解决这一难题提供了有效框架，但多尺度基函数的构造仍极为耗时。本文提出一种双域深度学习框架，用于加速 MGMsFEM 中多尺度基函数的计算。通过在频率域和空间域同时提取并解码渗透率场特征，该方法可快速生成基函数的数值矩阵。数值实验表明，所提框架在保持高逼近精度的同时实现显著的计算加速，具备未来在真实油藏工程中应用的潜力。
原文摘要 · Abstract (English)
In energy science, Darcy flow in heterogeneous porous media is a central problem in reservoir sim-ulation. However, the pronounced multiscale characteristics of such media pose significant challenges to conventional numerical methods in terms of computational demand and efficiency. The Mixed Generalized Multiscale Finite Element Method (MGMsFEM) provides an effective framework for addressing these challenges, yet the construction of multiscale basis functions remains computationally expensive. In this work, we propose a dual-domain deep learning framework to accelerate the computation of multiscale basis functions within MGMsFEM for solving Darcy flow problems. By extracting and decoding permeability field features in both the frequency and spatial domains, the method enables rapid generation of numerical matrices of multiscale basis functions. Numerical experiments demonstrate that the proposed framework achieves significant computational acceleration while maintaining high approximation accuracy, thereby offering the potential for future applications in real-world reservoir engineering.
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