To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering CO2 in geological formations for long-term storage presents several challenges. In this study, we address these issues by modeling the decision-making process for carbon storage operations as a partially observable Markov decision process (POMDP). We solve the POMDP using belief state planning to optimize injector and monitoring well locations, with the goal of maximizing stored CO2 while maintaining safety. Empirical results in simulation demonstrate that our approach is effective in ensuring safe long-term carbon storage operations. We showcase the flexibility of our approach by introducing three different monitoring strategies and examining their impact on decision quality. Additionally, we introduce a neural network surrogate model for the POMDP decision-making process to handle the complex dynamics of the multi-phase flow. We also investigate the effects of different fidelity levels of the surrogate model on decision qualities.

翻译：为应对全球变暖并减轻气候变化相关风险，碳捕集与封存（CCS）已成为一项关键技术。然而，在地质构造中长期安全封存CO₂面临诸多挑战。本研究通过将碳封存运营的决策过程建模为部分可观测马尔可夫决策过程（POMDP）来解决这些问题。我们采用置信状态规划来求解该POMDP，以优化注入井和监测井的选址，目标是在保证安全性的前提下最大化CO₂封存量。仿真实验结果表明，该方法能够有效确保长期安全的碳封存运营。通过引入三种不同的监测策略并分析其对决策质量的影响，我们展示了该方法的灵活性。此外，我们引入了一个用于POMDP决策过程的神经网络替代模型，以处理多相流动的复杂动力学特性。我们还研究了替代模型不同保真度水平对决策质量的影响。