Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change. However, they face uncertainties due to geological factors and operational limitations, resulting in possibilities of induced seismic events or groundwater contamination. To overcome these challenges, we propose a specialized machine-learning (ML) model to manage extensive reservoir models efficiently. While ML approaches hold promise for geological carbon storage, the substantial computational resources required for large-scale analysis are the obstacle. We've developed a method to reduce the training cost for deep neural operator models, using domain decomposition and a topology embedder to link spatio-temporal points. This approach allows accurate predictions within the model's domain, even for untrained data, enhancing ML efficiency for large-scale geological storage applications.

翻译：地质碳储存与能源储存是实现净零碳排放及应对气候变化的关键技术。然而，由于地质因素和运行限制带来的不确定性，此类技术可能引发诱发地震或地下水污染等风险。为克服这些挑战，我们提出一种专用机器学习模型，以高效管理大型储层模型。尽管机器学习方法在地质碳存储领域具有潜力，但大规模分析所需的高昂计算资源仍是主要障碍。我们开发了一种降低深度神经算子模型训练成本的方法，通过领域分解和拓扑嵌入器关联时空点。该方法能够在模型域内实现精准预测，即使对未训练数据亦有效，从而提升机器学习在大规模地质存储应用中的效率。