Shale reservoirs offer significant potential for CO₂ geological sequestration due to their extensive nanopore networks and heterogeneous pore systems. This study comparatively assessed the CO₂ storage potential of the Lower Silurian Longmaxi and Lower Cambrian Qiongzhusi shales through an integrated approach involving organic geochemical analysis, mineralogical characterization through X-ray diffraction (XRD), mercury intrusion capillary pressure (MICP), low-pressure nitrogen and carbon dioxide physisorption, field-emission scanning electron microscopy (FE-SEM), stochastic 3D microstructure reconstruction, multifractal analysis, and three-dimensional succolarity computation. The results demonstrate that mineral assemblages and diagenetic history govern pore preservation: Longmaxi shales, with moderate maturity and shallower burial, retain abundant organic-hosted mesopores, whereas overmature and deeply buried Qiongzhusi shales are strongly compacted and mineralized, reducing pore availability. Multifractal spectra and 3D reconstructions reveal that Longmaxi develops broader singularity spectra and higher succolarity values, reflecting more isotropic meso-/macropore connectivity at the SEM scale, while Qiongzhusi exhibits narrower spectra and lower succolarity, indicating micropore-dominated and anisotropic networks. Longmaxi has nanometer-scale throats (D₅₀ ≈ 10–25 nm) with high CO₂ breakthrough pressures (P₁₀ ≈ 0.57 MPa) and ultra-low RGPZ permeability (mean ≈ 1.5 × 10⁻² nD); Qiongzhusi has micrometer-scale throats (D₅₀ ≈ 1–3 μm), very low breakthrough pressures (P₁₀ ≈ 0.018 MPa), and much higher permeability (mean ≈ 4.63 × 10³ nD). Storage partitioning further differs: Longmaxi’s median total capacity is ≈15.6 kg m⁻³ with adsorption ≈ 93%, whereas Qiongzhusi’s median is ≈12.8 kg m⁻³ with adsorption ≈ 70%. We infer Longmaxi favors secure adsorption-dominated retention but suffers from injectivity limits; Qiongzhusi favors injectivity but requires reliable seals.

Digital Rock Technology comprises a set of image-based modelling techniques that analyse rocks at the pore level, gaining insight essential in industries including geological carbon dioxide sequestration, underground hydrogen storage, oil and gas, or geothermal energy. Pore Network Modelling simulates flow through a network of pores and throats representing a rock sample's void space, which results in improved efficiency and scalability compared to conventional simulation methods. We propose using semantic segmentation to identify the location and geometry of complex features such as fractures and vugs. Convolutional Neural Networks, a form of deep learning technology, can assist in automating the time-consuming process of handling extensive X-ray micro-computed tomography (micro-CT) data. //However, a solely accuracy-focused approach neglects the importance of efficiency and interpretability. This study explores the effect of deep-learning architecture choices on micro-CT semantic segmentation performance, considering computational resource efficiency and carbon footprint under the Green AI principles. The proposed novel objective function, incorporating topology and curvature information via Minkowski functionals, shows a 4% improvement in the accuracy of pre-trained models, and the study presents a strategy for selecting the optimal configuration. To address the lack of available labelled datasets, an algorithm was developed for generating semi-synthetic image-label pairs using features from publicly available data.

The rapid escalation of global warming, driven by anthropogenic carbon dioxide (CO₂) emissions, underscores the necessity of carbon capture and storage (CCS) technologies as a critical strategy for mitigating atmospheric CO₂ levels. Shale reservoirs, characterized by their extensive nanopore networks and heterogeneous pore structures, hold significant promise for CO₂ sequestration. This study investigates the storage and sequestration potential of shales from two distinct formations: the Lower Silurian Longmaxi Formation (TY1 group) and the Lower Cambrian Niutitang Formation (N206 group). A comprehensive suite of experiments, including XRD analysis, mercury intrusion porosimetry (MIP), low-pressure gas adsorption (N₂ and CO₂), field-emission scanning electron microscopy (FE-SEM), and mineralogical analysis, was employed to characterize pore structure, adsorption behaviour, and mineralogical controls on CO₂ storage. Moreover, a novel fractal parameter, succolarity along with conventional mass and surface fractal dimensions were used to depict the pore systems of the two groups.Results reveal that the TY1 samples exhibit higher total organic carbon (TOC; up to 7.58%), greater microporosity, and stronger CO₂ adsorption energies (up to 34 kJ/mol) compared to the N206 samples, which display a more mesopore-dominated system and lower adsorption energies (28–30 kJ/mol). The Longmaxi Formation demonstrates superior pore connectivity and pore size distribution (PSD) homogeneity, enhancing both CO₂ retention and transport. Its higher carbonate content also suggests potential for mineral trapping through carbonation reactions. In contrast, the Niutitang Formation is characterized by higher total porosity (up to 2.4%) and mesoporous contributions, favouring rapid injection but limiting long-term retention. Meanwhile, the FE-SEM observations revealed that many authogenic minerals such as quartz, pyrite and rutile occupy the pore space in organic matters. It is much more prevalent in the N206 samples, which may be responsible for its lower microporosity.Key findings include a strong correlation between TOC and micropore volume, as well as between clay minerals and mesopore-macropore attributes. These correlations highlight the dual role of organic matter and mineral content in determining gas adsorption capacity and flow dynamics. The TY1 group’s balanced micropore and mesopore contributions make it ideal for long-term CO₂ sequestration, while the N206 group’s larger pore sizes enhance its suitability for rapid injection and enhanced gas recovery (EGR) applications.This study provides critical insights into the interplay of organic matter, mineral composition, and pore structure in controlling CO₂ storage potential in shale reservoirs. The findings emphasize the Longmaxi Formation's superior suitability for CO₂ storage and EGR, with implications for optimizing CCS strategies in similar shale systems globally.