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, Alison
• 金额: $ 3.9万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723082
• 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。通过捕获已释放的二氧化碳并将其存储在地下和2中，从而监视减轻气候变化的努力，并有效地监测石油和天然气亚面储量，以确保在生产过程中没有错过。解决这两个问题的解决方案都需要准确监视流体的位置以及它们在地下的移动方式。我们建议扩展当前通常仅应用单个监视技术的方法。 在这里，我们旨在结合两种监测技术：地震技术，它擅长本地化变化和电磁，这很擅长确定哪些属性已经变化以及进行了多少。 当我们结合这些技术时，我们将使用快速模拟数据的方法进行此操作，以便我们可以看到建模的数据与录制数据匹配的程度。 一旦我们拥有这些快速的仿真技术，我们就可以将它们与取样许多模型相结合，以使我们不仅估计更改，还可以衡量这些变化的不确定性。 这旨在通过对碳在哪里以及留在那里的可能性进行更详细且细微的细微细致图片来提高我们调节这些类型的碳封存技术的能力。