In this work, we develop a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked reservoirs. The model combines different networks to encode heterogeneous properties effectively. By predicting the concentration rate, we are able to accurately model the transport process. Numerical experiments demonstrate that our neural operator approach achieves accuracy comparable to that of the finite element method. The previously introduced Enriched DeepONet architecture has been revised, motivated by the architecture of the popular multi-head attention of transformers, to improve its performance without increasing the compute cost. The computational efficiency of the proposed model enables rapid and accurate predictions of solute transport, facilitating the optimization of reservoir management strategies and the assessment of environmental impacts. The data and code for the paper will be published at https://github.com/ehsanhaghighat/STONet.

翻译：本文提出了一种新型神经算子——溶质运移算子网络（STONet），用于高效模拟微裂隙储层中的污染物运移过程。该模型通过整合不同网络有效编码非均质特性。通过预测浓度变化率，我们能够精确模拟运移过程。数值实验表明，该神经算子方法达到了与有限元法相当的精度。受Transformer中流行的多头注意力机制结构启发，我们对先前提出的Enriched DeepONet架构进行了改进，在不增加计算成本的前提下提升了其性能。所提模型的计算效率实现了溶质运移的快速准确预测，有助于优化储层管理策略和评估环境影响。本文相关数据与代码将发布于https://github.com/ehsanhaghighat/STONet。