用分步训练的神经网络精准模拟注水井附近压力变化。
WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks
- 分阶段缩小求解域并调整等效井半径，迭代逼近真实井结构。
- 首次实现从注水开始到结束全程的压力精确推断。
- 适合做油藏反演与动态模拟的研究者使用。
准确刻画井筒特征对地下流体模拟和油藏表征至关重要。物理信息神经网络（PINNs）近年来成为储层建模的有力工具，可无缝融合监测数据与物理方程。然而，现有基于PINN的研究在注水初期难以准确捕捉井附近的流体压力。为此，本文提出WellPINN，一种结合多个顺序训练的PINN模型的建模流程，通过将求解域逐步分解为收缩子域，并同步减小等效井半径，迭代逼近真实井尺寸。结果表明，该流程首次实现了在全注水周期内，从注水速率精确推断流体压力，显著提升了PINNs在反问题建模与运营情景仿真中的潜力。本文所有数据与代码将公开于https://github.com/linuswalter/WellPINN。
原文摘要 · Abstract (English)
Accurate representation of wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) have recently emerged as a promising method for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells, particularly during the early stage after injection begins. To address this, we propose WellPINN, a modeling workflow that combines the outputs of multiple sequentially trained PINN models to accurately represent wells. This workflow iteratively approximates the radius of the equivalent well to match the actual well dimensions by decomposing the domain into stepwise shrinking subdomains with a simultaneously reducing equivalent well radius. Our results demonstrate that sequential training of superimposing networks around the pumping well is the first workflow that focuses on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper will be made openly available at https://github.com/linuswalter/WellPINN.
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