RESUMO
Subsurface sandstone deposits represent globally ubiquitous reservoirs which can potentially provide the characteristics necessary for the effective geological storage of CO2. Geological carbon storage is widely agreed to be a key asset in tackling anthropogenic emissions and climate change to reach a sustainable 'net zero', despite the present financial challenges associated with it. Therefore, improved understanding of the characteristics of the materials in which we plan to store many gigatons of CO2 is critical. Developing cheaper characterisation techniques is therefore crucial to support the global push for net zero. In this work we use digital analysis of 3D microscale X-ray images of a range of sandstone samples to constrain the porosity-permeability relationship and the upper percolation threshold; the point at which near full pore structure connectivity is achieved. This is one of the most significant controls on the viability of carbon storage as a practical solution to achieving net zero. We find that the upper percolation threshold in sandstone occurs at ca. 14% total porosity whilst the relationship between porosity ([Formula: see text]) and permeability ([Formula: see text]) can be defined as [Formula: see text]. The investigation of the upper percolation threshold may allow a target criterion to be designated when assessing potential carbon storage reservoirs, whilst investigation of the porosity-permeability relationship allows for a greater understanding of the fluid flow regimes in the subsurface. By using a digital technique to assess carbon storage reservoir potentiality we show that initial characterisation of reservoirs can be carried out rapidly and relatively economically, prior to further full reservoir characterisation studies. This approach is also non-destructive, allowing samples to be reused and multiple analytical phases performed on the same materials.
RESUMO
An accurate and reliable description of the porosity-permeability relationship in geological materials is valuable in understanding subsurface fluid movement. This is important for reservoir characterisation, energy exploitation, geological carbon storage (GCS) and groundwater contamination and remediation. Whilst the relationship between pore characteristics and porosity and permeability are well examined, further investigation into the influence of grain characteristics on porosity and permeability would be beneficial due to the inherent relationship between grains and pores. This work aims to determine whether incorporation of grain characteristics into a porosity-permeability model is effective in constraining this relationship. Two fully digital approaches to individual 3D grain analysis based upon watershed segmentation are compared to determine the most effective, yet simple, workflow applicable to core plugs of significantly compacted grains. The identification of an effective segmentation workflow will facilitate future work on similarly complex materials, removing the need for traditional time-consuming and manual techniques. We use the most effective approach of measuring grain shape (sphericity) and size (Feret diameter) alongside an established fully digital workflow to measure porosity and permeability to investigate the impact of grain characteristics on porosity and permeability. We show that grain sphericity and porosity exhibit a positive relationship whereas no such relationship exists with grain size. Measurements of grain sphericity are applied to calculate a Kozeny-Carman (K-C) type porosity-permeability fit which was found to be unsatisfactory, compared to a simpler fit excluding any grain parameters. This is possibly due to the lower sphericity of the studied grains, deviating significantly from the K-C assumption that grains are entirely spherical. The simpler fit is most suitable for the studied materials, showing that inclusion of grain characteristics is not effective for better defining the porosity-permeability relationship in a K-C paradigm for these samples. This highlights the need for a model capable of considering a range of grain sphericities to further constrain the porosity-permeability relationship.
RESUMO
Over the past two decades, many decision support systems (DSSs) have been developed to support decision makers and facilitate the planning and redevelopment process of brownfields. Existing systems are however often siloed in their approach and do not fully capture the complexity of brownfield sites from a sustainable development point of view. This critical review provides an insight into the development and implementation of DSSs, published and emerging, together with assessment of their strengths, limitations and opportunities for future integration. Brownfields DSS applications include: remediation technology selection; and land use planning; and risk assessment. The results of this review lead the authors to identify four opportunities to improve brownfield DSSs: (i) increased use of qualitative socioeconomic criteria, particularly costs and economic variables, (ii) decision-support during the early stages of brownfield redevelopment, (iii) the integration of predictive modelling methods, and (iv) improvements of user interfaces and modern web-based functionalities.
