Carbon Capture and Storage (CCS) is a collective term for technologies that allow society to unlock the benefits of energy intensive processes like fertiliser production and combustion of fuels (fossil or biologically sourced) without releasing the CO₂ to the atmosphere. Hence, CCS could assist in accelerating decarbonisation while society pursues a just energy transition. This paper aims to summarise the learnings of three research projects that all investigated aspects of marine monitoring for CCS from a CO₂ storage operator’s perspective. The QICS (Quantifying and Monitoring Potential Ecosystem Impacts of Geological Carbon Storage), ETI MMV (Energy Technologies Institute Measurement, Monitoring and Verification of CO₂ Storage), and STEMM-CCS (Strategies for Environmental Monitoring of Marine CCS) projects collectively represent over twelve years of dedicated research to assess environmental impacts and to develop technologies for detection, location, and quantification of potential leakage from offshore geological storage of CO₂. Each project used controlled releases in representative environments to test their methods and technologies. QICS as the first of the three projects, focused on the understanding of sensitivities of the UK marine environment to a potential leak from a CO₂ storage complex and tested technologies to detect such emissions. The ETI MMV project brought together research and industry partners to develop and sea trial an operational, integrated and cost-effective marine monitoring system for geological CO₂ storage. As a commercial project, these results have never been published before and this paper shares for the first-time insights from this work. In February 2020, STEMM-CCS, completed its quest to test techniques for environmental monitoring over a marine CO₂ storage site in the UK North Sea, further improved near seabed leakage characterisation capabilities, and delivered a first marine CCS demonstration level ecological baseline. This paper aims to summarise some of the key insights from the three projects and provides references where available for the interested reader. The key finding of all three projects is that the impacts of small to medium CO₂ leakages from large-scale storage are limited and localised. Technology capabilities exist for integrated marine CO₂ storage monitoring and their performance has been benchmarked at controlled release trials. Even small leakages of 10−50 L/min can be detected at unknown locations in a large area of interest. Finally, the first important steps towards automated monitoring data analysis have been made, including automated leakage signal detection from Side Scan Sonar data (ETI MMV project) and automated species identification from marine biology images (STEMM-CCS project). Some remaining challenges include missed/false alerts because of large variations in the background signal, the cost of monitoring large areas over long periods, and making real-time decisions based on big data. Continued work to reduce the cost of marine monitoring technologies and advancing automation of data processing and analysis will be important in order to support safe and efficient offshore CCS deployment at large scale.

碳捕集与封存（CCS）是技术的统称，该技术使社会能够在不将CO ₂释放到大气中的情况下，释放化肥生产和燃料燃烧（化石或生物来源）等能源密集型过程的收益。因此，CCS可以协助加速脱碳，同时社会追求公正的能源转型。本文旨在总结三个研究项目的经验，这些研究都从CO ₂封存运营商的角度研究了CCS海洋监测的各个方面。QICS（定量和监测地质碳储存对生态系统的潜在影响），ETI MMV（能源技术研究所对CO _2的测量，监测和验证）储存”和STEMM-CCS（海洋CCS环境监测策略）项目共同代表了十二年的专门研究，以评估环境影响并开发用于检测，定位和量化CO ₂海上地质储存中潜在泄漏的技术。每个项目都在代表性环境中使用受控发行版来测试其方法和技术。QICS是三个项目中的第一个，重点是了解英国海洋环境对CO ₂潜在泄漏的敏感性存储复杂且经过测试的技术以检测此类排放。ETI MMV项目将研究和行业合作伙伴聚集在一起，以开发和进行海上试验，以运行，集成且具有成本效益的地质CO ₂储存海洋监测系统。作为一个商业项目，这些结果以前从未发表过，本文分享了这项工作的首次见识。2020年2月，STEMM-CCS完成了对海洋CO ₂进行环境监测的测试技术的探索英国北海的储油场，进一步提高了近海床泄漏特征化能力，并提供了第一个海洋CCS示范水平的生态基线。本文旨在总结来自这三个项目的一些关键见解，并为感兴趣的读者提供参考。这三个项目的关键发现是，大规模存储中小至中型CO ₂泄漏的影响是有限的和局部的。存在用于集成船用CO _2的技术能力存储监视及其性能已在控制释放试验中进行了基准测试。即使在较大的目标区域中的未知位置，甚至可以检测到10-50 L / min的小泄漏。最后，迈出了迈向自动监测数据分析的重要第一步，包括从Side Scan Sonar数据自动检测泄漏信号（ETI MMV项目）和从海洋生物学图像自动识别物种（STEMM-CCS项目）。剩下的一些挑战包括由于背景信号的巨大差异而导致的错过/错误警报，长期监控大区域的成本以及基于大数据做出实时决策。