用傅里叶神经算子加速真实地质下二氧化碳储存模拟，提升决策效率。
Fourier Neural Operator based surrogates for $CO_2$ storage in realistic geologies
- 基于傅里叶神经算子构建代理模型，实现高分辨率实时模拟
- 相比传统方法提速10万倍，预测精度损失极小
- 适用于碳捕集、地热与氢能存储等地下能源系统
本研究旨在开发代理模型以加速碳捕集与封存（CCS）技术的决策流程。地下二氧化碳储层选址常需昂贵且复杂的流体模拟。本文基于傅里叶神经算子（FNO）构建模型，实现真实地质条件下二氧化碳羽流迁移的实时、高分辨率模拟。模型在由真实地下参数生成的综合数据集上训练，相较传统方法实现约10⁵倍计算加速，预测精度损失可忽略。我们还探索了超分辨率实验以降低模型训练成本，并提出多种策略提升预测可靠性，这对实际地质场地评估至关重要。该框架基于NVIDIA Modulus库，支持快速筛选潜在储层。所提工作流程与策略可推广至地热储层建模与氢气储存等其他能源场景。本研究将科学机器学习模型拓展至更贴近真实地下含水层/储层的三维复杂系统，为下一代地下碳封存数字孪生奠定基础。
原文摘要 · Abstract (English)
This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $CO_2$ storage sites often necessitates expensive and involved simulations of $CO_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO_2$ plume migration. The model is trained on a comprehensive dataset generated from realistic subsurface parameters and offers $O(10^5)$ computational acceleration with minimal sacrifice in prediction accuracy. We also explore super-resolution experiments to improve the computational cost of training the FNO based models. Additionally, we present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites. This novel framework, based on NVIDIA's Modulus library, will allow rapid screening of sites for CCS. The discussed workflows and strategies can be applied to other energy solutions like geothermal reservoir modeling and hydrogen storage. Our work scales scientific machine learning models to realistic 3D systems that are more consistent with real-life subsurface aquifers/reservoirs, paving the way for next-generation digital twins for subsurface CCS applications.
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