Webinar: Understanding the cost of retrofitting CO2 capture to integrated oil refineries – the ReCap project, 25/10/2017
Latest Publication: 4th Post Combustion Capture Summary Brochure 21/11/2017
Blog: New IEAGHG Report: CO2 Storage Efficiency in Deep Saline Formations – Stage, by James Craig, IEAGHG 11/01/2018
Report: 2018-02 CO2 Storage Efficiency in Deep Saline Formations – Stage 2 11/01/2018
Information Paper: 2017-IP63 CSLF Report on Offshore CO2-EOR & 2017-IP64 CSLF Report on Practical Regulations and Permitting Process for Geological CO2 Storage, by James Craig, IEAGHG & 2017-IP65 US Study on Approaches for International Collaboration and Financing of CCUS Pilot Projects, by John Gale, IEAGHG 14/12/2017
Greenhouse News: December 2017 22/12/2017
Staff Presentation: CCS Hubs and Clusters, John Gale 05/12/2017 & GHGT TCP Summary 2017, John Gale & Gunter Siddiqi, 17-19/12/17
Briefing papers: October 2017 GHG Mitigation and October 2017 CCS Technical Status 31/10/2017
GHGT-13 Conference Proceedings
Deep saline formations (DSFs) have the potential to store considerable amounts of CO2 over large areas. Estimating the storage capacity is an important aspect of CCS especially when geological conditions are evaluated over large areas. The objective of this second stage project, undertaken by the Energy and Environmental Resource Center, was to further develop and test a methodology for estimating storage efficiency of DSFs but, unlike the previous study completed in 2014, over a much more limited area of ~1000 km2. This current study also modelled storage capacity over a limited time-span of 50 years to reflect a realistic operational window for a large-scale storage project.
Two different formations from two contrasting basins were modelled: the Minnelusa Formation, within an onshore basin that spans the US states of Montana and Wyoming; and the Bunter Formation, an within an off-shore basin in the southern North Sea. These two systems were selected because they have contrasting geological characteristics. The Bunter is also a prospective candidate for CO2 storage. Although the Minnelusa Formation is classified as an open system with boundaries that are unconstrained it is less permeable by comparison with the Bunter. In both cases the volumetric capacity, in terms of available pore space, was modelled by building a 3D impression of each formation’s porosity by extrapolating information from wellbores, geophysical properties and known geological charateristics. The next stage of the project modelled the dynamic properties of each formation, that is simulating the effects of injecting CO2 from multiple wells. The storage efficiency, expressed as a percentage, is a comparison of the total pore volume with the amount of CO2 stored when dynamic conditions are applied. The study simulated a number of different conditions including variations in salinity, the effects of brine extraction and, in the case of the Bunter, how well configuration affects storage efficiency.
The results from this study showed that CO2 storage efficiency of the Minnelusa over a 50 year period raised the storage efficiency from 4.7% to 5.9%. This is equivalent to an estimated increase in storage capacity from 242 Mt to 302 Mt of CO2. Extending injectivity for a further 50 years would increase storage capacity to over 400 Mt of CO2. The impact of water extraction on the Bunter was profound raising storage efficiency from 4.7% to 7.4%. This is equivalent to raising the estimated storage capacity from 1,770 Mt to 2,806 Mt of CO2. The difference between these two formations in terms of storage capacity can be attributed to the highly favourable permeability across the Bunter compared with the Minnelusa.
The model simulations also showed that as the number of injection wells increases in a designated storage system, more of the wells become influenced by pressure interference from their neighbours and the injectivity rate per well declines. The closer a DSF approaches full development, the more its efficiency approaches that of a closed system, even if it has open boundaries.
This study also includes a cost development model to determine how the number of wells affects the cost-effectiveness of each storage site. In both cases 20% of all the wells in the cost model were able to deliver more than 60% of the total CO2 injected. In both modelled formations the number of wells was the primary variable in determining the cost factor. In other words delivering the amount of injected CO2 by increasing the number of wells becomes proportionately less cost-effective.
It is important to recognise in any model projection predictions can vary depending on the model grid cell size. Heterogeneity and different model projections can substantially influence the quantity of injected CO2. Consequently it is important to understand and separate the effects of the choices of simulation parameters from the physical effects in a storage formation.