IUCRC Planning Grant University of Southern California: Center for CO2 Storage Modeling, Analytics, and Risk Reduction Technologies (CO2-Smart)

地质二氧化碳储存为减少大气中的二氧化碳提供了一个可行的选择。这一领域项目的成功实施取决于在多物理流动建模、地下监测数据分析以及风险评估和缓解战略方面的科学和技术进步。该行业-大学二氧化碳储存建模、监测、分析和降低风险技术合作研究中心(CO2-SMART)旨在创建一个协同研究计划，以加快新型人工智能技术的开发、适应和部署，以实现高效和安全的二氧化碳地质储存。该中心的潜在成员包括该行业的主要二氧化碳生产部门，包括上下游石油和天然气公司、化学和石化公司以及发电厂和公用事业公司。该中心将培训下一代工程师和科学家，使其成为实施和管理大型全球控制系统项目的未来领导者。这将有助于应对我们时代最紧迫的挑战之一，产生广泛和重大的社会、能源安全和公共卫生影响。该中心将由一个由中心成员组成的行业顾问委员会指导，他们将协助中心继续专注于应对在地下成功实施二氧化碳储存所面临的新兴行业挑战。该中心的技术重点将是整合多物理建模和模拟、地下监测数据采集以及现代人工智能和数据科学算法方面的最新进展，以开发安全和可持续的二氧化碳地下地质储存的可靠技术。新兴的基于人工智能的技术必将用高效的自动化平台取代传统的劳动密集型建模工作流，其中大数据分析不仅用于增强数据处理、可视化和管理，还用于提供先进的物理信息预测分析、异常/故障检测和实时运营的决策支持能力。该中心将专注于几个关键的研发重点领域，包括(1)地下流体、岩石和裂缝属性的建模和表征，以及与潜在的多物理过程有关的参数，(2)地下流动过程的多尺度和多物理建模和预测，(3)使用多物理监测数据模式和高级深度学习架构的高分辨率动态表征和成像，(4)与复杂的多物理系统有关的现代风险评估和不确定性量化技术，以及(5)在风险和不确定性下注入的二氧化碳羽流运动的优化和控制。在这些研究领域开发和推进最先进的技术将直接造福于行业成员，并服务于一般工业和科学界，以及广大公众。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Geologic CO2 storage offers a viable option for reducing CO2 in the atmosphere. Successful implementation of projects in this area hinges on scientific and technological advances in multi-physics flow modeling, subsurface monitoring data analytics, and risk assessment and mitigation strategies. This Industry-University Collaborative Research Center for CO2 Storage Modeling, Monitoring, Analytics, and Risk Reduction Technologies (CO2-SMART) aims to create a synergistic research program to accelerate the development, adaptation, and deployment of novel AI-enriched technologies to enable efficient and safe implementation of the geologic storage of CO2. The prospective members of the Center include the main CO2-producing sectors of the industry, including upstream and downstream oil and gas companies, chemical and petrochemical companies, as well as power plants and utility companies. The Center will train the next generation of engineers and scientists as future leaders for implementing and managing large-scale GCS projects. This will help to address one of the most pressing challenges of our time with broad and significant societal, energy security, and public health impacts. The Center will be guided by an industry advisory board of Center members who will assist it in maintaining its focus on addressing the emerging industry challenges for successful implementation of CO2 storage in the subsurface. The technical focus of the Center will be on integrating recent advances in multi-physics modeling and simulation, subsurface monitoring data acquisition, and modern AI and data science algorithms to develop reliable technologies for safe and sustainable subsurface geologic storage of CO2. The emerging AI-based technologies are bound to replace the traditional labor-intensive modeling workflows with efficient automated platforms, where big data analytics is used not only to enhance data processing, visualization, and management, but also to provide advanced physics-informed predictive analytics, anomaly/fault detection, and decision support capabilities for real-time operation. The Center will focus on several key research and development thrust areas, including (1) modeling and characterization of subsurface fluid, rock, and fracture properties, as well as the parameters pertaining to the underlying multi-physics processes, (2) multiscale and multi-physics modeling and prediction of subsurface flow processes, (3) high-resolution dynamic characterization and imaging using multi-physics monitoring data modalities and advanced deep learning architectures, (4) modern risk assessment and uncertainty quantification technologies pertaining to complex multi-physics systems, and (5) optimization and control of the injected CO2 plume movement under risk and uncertainty. Developing and advancing state-of-the-art technologies in these research areas will directly benefit the industry members and serve the general industrial and scientific communities, as well as the public at large.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.