Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is computationally intensive, requiring geochemical calculations at each grid cell and each time step within a discretized simulation domain. Although recent advancements have integrated machine learning techniques as surrogates for geochemical simulations, ensuring computational efficiency and accuracy remains a challenge. This chapter investigates machine learning models as replacements for a geochemical module in a reactive transport in porous media simulation. We test this approach on a well-documented cation exchange problem. While the surrogate models excel in isolated predictions, they fall short in rollout predictions over successive time steps. By introducing modifications, including physics-based constraints and tailored dataset generation strategies, we show that machine learning surrogates can achieve accurate rollout predictions. Our findings emphasize that, when judiciously designed, machine learning surrogates can substantially expedite the cation exchange problem without compromising accuracy, offering significant potential for a range of reactive transport applications.

翻译：多孔介质中的反应输运在地下储层过程中起着关键作用，影响着流体性质与地球化学特征。然而，将流体流动和输运过程与地球化学反应进行耦合计算量巨大，需要在离散化的模拟域中对每个网格单元和每个时间步进行地球化学计算。尽管近期研究进展已将机器学习技术作为地球化学模拟的替代模型，但确保计算效率与精度仍是一项挑战。本章研究了在孔隙介质反应输运模拟中，采用机器学习模型替代地球化学模块的可行性。我们在一个文献记载充分的阳离子交换问题上验证了该方法。研究发现，替代模型在单步预测中表现优异，但在连续时间步的滚动预测中效果欠佳。通过引入基于物理的约束条件和定制化的数据集生成策略等改进措施，我们证明了机器学习替代模型能够实现精确的滚动预测。研究结果表明，经过合理设计的机器学习替代模型能够在保证精度的前提下，显著加速阳离子交换问题的求解，这为一系列反应输运应用提供了重要潜力。