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Other literature type . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Project deliverable . 2024
License: CC BY
Data sources: Datacite
ZENODO
Project deliverable . 2024
License: CC BY
Data sources: Datacite
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D1.3 A framework for regional high-level technical screening of promising CCUS value chains

Authors: Lothe, Ane Elisabet; Gravaud, Isaline; Silva, Eirik Falck da; Skagestad, Ragnhild; Shogenova, Alla; Shogenov, Kazbulat; Sousa, Leandro-Henrique; +6 Authors

D1.3 A framework for regional high-level technical screening of promising CCUS value chains

Abstract

There is a need to accelerate the deployment of Carbon Capture, Utilization, and Storage (CCUS) throughout Europe. As a part of this, the CCUS ZEN project has looked at how to carry out high-level technical screening of promising value chains with technical mapping of emission sources, utilization options, transport infrastructure and storage sites. In this report challenges in the high-level technical CCUS value chain workflow is discussed. First, a screening of the emission sources in each geographical region led to the identification of promising sites for CO2 capture. The focus is on identifying clusters of emitters, where CO2 could be captured from different industrial plants and gathered at a hub before transport to the storage site. The emitters can also be identified as promising depending on their amount of emission, location, type of industry, etc. For each emission source, information about the facility was collected, along with information about the facility’s emissions. The screening of potential storage sites in the geographical region was carried out based on public and available data from projects. For each mapped storage site, information was gathered about the type of reservoir (deep saline aquifer or depleted hydrocarbon field), the onshore or offshore location, the capacity of the reservoir and the Storage Readiness Level indicating the maturity of the capacity evaluation. For the infrastructure screening, we looked at existing infrastructure relevant for CO2-transport with emphasis on pipelines, existing natural gas corridors, waterways and ports. If transport using pipelines or waterways were not an option, also railways and road (lorries) were evaluated. Thereafter, one or more chains were suggested, linking one or more emission clusters to one or more storage sites. The total emission volumes of the clusters and the storage capacities were considered. It was recognised that actual volumes should be treated with caution. On the one hand, the maturity level of the storage sites is often low, resulting in potential smaller capacities when the resource is further assessed. Also, crucial data can be missing or confidential, which makes detailed studies and dynamic reservoir modelling challenging. On the other hand, the total volumes of the clusters’ emission do not necessarily represent the amount of captured CO2, but rather a maximum, as not 100% of the emissions would necessarily be captured. The potential for carbon utilisation was assessed by identifying existing CO2 use projects and looking at the potential availability of renewable energy and biogenic CO2. Finally, lessons learned from CCUS value chains around the world were discussed and some recommendations are made. The main recommendations are: • Datasets for CO2 storage should be made more accessible • There should be open dialogue between stakeholders across the value chain • There should be open dialogue with societal stakeholders

Keywords

CCUS ZEN, Carbon Capture, CCUS, CCUS value chain, emission sources, CCU, Carbon Storage, value chain, ccuszen, CCS, Carbon Transport

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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