优化碳封存作业以实现长期安全
Optimizing Carbon Storage Operations for Long-Term Safety
AI总结：
本文将碳封存作业建模为POMDP，利用信念状态规划和神经网络代理模型优化注入井与监测井位置，在确保安全的前提下最大化CO2封存量，并通过仿真验证了方法的有效性。
摘要：
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，将CO2安全封存在地质构造中以实现长期储存面临若干挑战。在本研究中，我们通过将碳封存作业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。我们使用信念状态规划求解该POMDP，以优化注入井和监测井的位置，目标是在保持安全的同时最大化封存的CO2量。仿真中的实证结果表明，我们的方法能够有效确保安全的长期碳封存作业。我们通过引入三种不同的监测策略并考察它们对决策质量的影响，展示了该方法的灵活性。此外，我们为POMDP决策过程引入了一个神经网络代理模型，以处理多相流的复杂动力学。我们还研究了代理模型不同保真度水平对决策质量的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
