Joint Inversion and Uncertainty Quantification of Electromagnetic and Seismic Data

电磁和地震数据的联合反演和不确定性量化

• 批准号: 566906-2021
• 负责人: Malcolm, AlisonAE
• 金额: $ 3.81万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=751085
• 关键词: Joint; Inversion; Uncertainty; Quantification; Electromagnetic

We propose to collaborate with a petroleum industry partner to develop and test a novel approach to helping solve two problems: 1. monitoring efforts to mitigate climate change by capturing released carbon dioxide and storing it in the subsurface and 2. effectively monitoring oil and gas subsurface reserves to ensure none have been missed during production. The solution to both problems requires accurate monitoring of where fluids are and how they are moving in the subsurface. We propose to expand our current approaches that typically apply only a single monitoring technique at a time. Here we aim to combine two monitoring techniques: Seismic, which is good at localizing changes and Electromagnetic, which is good at determining what properties have changed and by how much. When we combine these techniques, we will do so using fast ways of simulating our data, so that we can see how well our modelled data match our recorded data. Once we have these fast simulation techniques, we can combine them with ways of sampling many models to allow us to not just estimate the changes, but also give a measure of the uncertainty of these changes. This aims to improve our ability to regulate these sorts of carbon sequestration techniques by giving a more detailed and nuanced picture of where the carbon is and how likely it is to remain there.

我们建议与一个石油行业合作伙伴合作开发和测试一种新的方法，以帮助解决两个问题：1.通过捕获释放的二氧化碳并将其储存在地下来监测缓解气候变化的努力；2.有效地监测石油和天然气地下储量，以确保在生产过程中没有遗漏任何东西。这两个问题的解决方案都需要准确监测流体在哪里以及它们在地下的运动方式。我们建议扩展我们目前的方法，这些方法通常一次只应用一种监测技术。在这里，我们旨在结合两种监测技术：地震和电磁，前者擅长定位变化，后者擅长确定哪些属性发生了变化以及变化幅度有多大。当我们组合这些技术时，我们将使用模拟数据的快速方法来实现这一点，这样我们就可以看到建模的数据与记录的数据匹配得有多好。一旦我们拥有了这些快速模拟技术，我们就可以将它们与对许多模型进行抽样的方法结合起来，使我们不仅可以估计变化，还可以给出这些变化的不确定性的衡量标准。这项研究旨在提高我们监管这类碳封存技术的能力，方法是更详细、更细致入微地描述碳在哪里，以及它留在那里的可能性有多大。