Full waveform seismic monitoring of CO2 injection and storage with broadband and fiberoptic sensors

使用宽带和光纤传感器对二氧化碳注入和储存进行全波形地震监测

• 批准号: RGPIN-2022-03172
• 负责人: Innanen, Kristopher
• 金额: $ 5.46万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=755105
• 关键词: Full; waveform; seismic; monitoring; CO2

A critical geoscientific priority in Canada is to enable Carbon Capture, Utilization, and Storage (CCUS), in which greenhouse gases are permanently sequestered in geological structures, reducing their impact on climate change.  Applied seismology stands to play a critical monitoring role in this. My group and I will seek to make breakthroughs in our ability to use seismic methods to monitor CO2 storage, making full use of data, reliably and at low cost. The research will be supported by collaboration with the Consortium for Research in Elastic Wave Exploration Seismology, a group with the capacity to carry out custom land seismic experiments, and with the Containment and Monitoring Institute through their Field Research Station (CaMI.FRS). My HQP and I will leverage this collaborative environment to create new, full waveform inversion-based seismic methods for ensuring safe and stable storage. In the FWI variants needed for reservoir model-building, basic mathematical and numerical issues exist, that make mapping of multiple petrophysical properties, and their changes with time, more complicated to produce. We will address this through algorithm-building and numerical analysis, and validate the results with custom field data acquired at the CaMI.FRS.  My group and I will also target the computational expense of FWI-based monitoring. In recent years, hardware/software advances have made computational tools for FWI more easily available, and, critically, they have also made available two new technologies which will be central to the problem of low-cost, reliable monitoring. The first is Distributed Acoustic Sensing (DAS), in which fiberoptic cables are used as flexible, easily-deployable acquisition systems. The reduction in cost associated with DAS data makes it a key to new monitoring technology, but the unusual properties of the data mean that special methods will be needed to leverage its potential. My group and I will merge DAS with high-frequency FWI as one of our core proposed research objectives. The second technology is the suite of computational platforms associated with deep learning, and the possibilities they introduce for extension, and computationally efficient optimization, of FWI. My group has a record of developing deep learning tools for seismic inversion, but the data-rich problem of CO2 monitoring has not yet been broached. In the proposed plan, my group and I will develop custom-designed neural networks, constrained with physics-guiding involving wave theory and petrophysics, to output indicators of containment and/or storage failures. In this research plan, the science needed to create tools for monitoring of geological storage of CO2 will be carried out playing a role in helping Canada achieve its climate objectives. Importantly, by means of mentoring, outreach, and targeted recruitment, this research will be accomplished at the same time as we train a diverse cohort of HQP, in an inclusive environment.

加拿大的一个关键的地球科学优先事项是实现碳捕获、利用和储存（CCUS），其中温室气体被永久封存在地质结构中，减少其对气候变化的影响。 应用地震学将在这方面发挥关键的监测作用。我和我的团队将寻求在我们使用地震方法监测二氧化碳储存的能力方面取得突破，充分利用数据，可靠且成本低。该研究将得到与弹性波勘探地震学研究联盟（一个有能力进行定制陆地地震实验的组织）以及遏制和监测研究所（通过其现场研究站（CaMI.FRS））的合作支持。我和我的HQP将利用这个合作环境来创建新的、基于全波形反演的地震方法，以确保安全稳定的存储。在储层建模所需的FWI变体中，存在基本的数学和数值问题，这使得绘制多个岩石物理特性及其随时间的变化更加复杂。我们将通过算法构建和数值分析来解决这个问题，并使用在CaMI. FRS获得的自定义字段数据来验证结果。 我和我的团队还将针对基于FWI的监控的计算费用。近年来，硬件/软件的进步使FWI的计算工具更容易获得，而且，关键的是，它们还提供了两种新技术，这将是低成本，可靠的监测问题的核心。第一种是分布式声学传感（DAS），其中光纤电缆被用作灵活，易于部署的采集系统。与DAS数据相关的成本降低使其成为新监测技术的关键，但数据的不寻常属性意味着需要特殊的方法来发挥其潜力。我和我的小组将把DAS与高频FWI合并，作为我们提出的核心研究目标之一。第二项技术是与深度学习相关的计算平台套件，以及它们为FWI的扩展和计算效率优化引入的可能性。我的团队有开发地震反演深度学习工具的记录，但二氧化碳监测的数据丰富问题尚未得到解决。在拟议的计划中，我和我的团队将开发定制设计的神经网络，受到涉及波动理论和岩石物理学的物理指导的约束，以输出遏制和/或存储故障的指标。在这一研究计划中，将开展创造监测二氧化碳地质储存工具所需的科学研究，以帮助加拿大实现其气候目标。重要的是，通过指导，推广和有针对性的招聘，这项研究将在我们在包容性的环境中培养多样化的HQP队列的同时完成。