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<p>Multiphase flow simulation in porous media is a challenging task with significant industrial implications. Pore Network Modeling (PNM) is an effective method that provides accurate results within a reasonable computational timeframe. However, as the complexity of these systems increases, particularly when simulating whole-core-sized pore networks (Digital Plugs) with millions of elements, there is a growing demand to improve the computational efficiency of PNM. The primary computational bottleneck is the pressure field update process, which involves solving a linear system of balance equations. To address this issue, our research focuses on evaluating the performance of a recently developed multiscale preconditioner for solving this system. We compare its performance against a state-of-the-art algebraic multigrid (AMG) method. Networks where the multiscale preconditioner outperforms AMG are identified, and a multilevel strategy is implemented to extend the capabilities of the method to networks with up to 200 million pore bodies. This shows the multiscale method is a promising alternative to accelerate multiphase simulations in Pore Network Modeling.</p>

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A Graph-Partitioning-Based Multiscale Preconditioner for Modeling Flow in Unstructured Pore Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月3日

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  • Alfredo Jaramillo,
  • Bradley McCaskill,
  • Mohammad Piri,
  • Shehadeh Masalmeh

摘要

Multiphase flow simulation in porous media is a challenging task with significant industrial implications. Pore Network Modeling (PNM) is an effective method that provides accurate results within a reasonable computational timeframe. However, as the complexity of these systems increases, particularly when simulating whole-core-sized pore networks (Digital Plugs) with millions of elements, there is a growing demand to improve the computational efficiency of PNM. The primary computational bottleneck is the pressure field update process, which involves solving a linear system of balance equations. To address this issue, our research focuses on evaluating the performance of a recently developed multiscale preconditioner for solving this system. We compare its performance against a state-of-the-art algebraic multigrid (AMG) method. Networks where the multiscale preconditioner outperforms AMG are identified, and a multilevel strategy is implemented to extend the capabilities of the method to networks with up to 200 million pore bodies. This shows the multiscale method is a promising alternative to accelerate multiphase simulations in Pore Network Modeling.