Kejian Wu

Understanding plume migration andF trapping mechanisms within heterogeneous reservoirs is critical for optimizing carbon storage performance and ensuring long term containment. Predicting plume behaviour in heterogeneous reservoirs requires pore scale descriptors that preserve capillary physics and connectivity. We develop a lognormal Gaussian decomposition of microCT‑derived pore volume probability density functions (PDFs) for eight North Sea reservoir plugs. Each PDF is resolved into three subpopulations: micropores, mesopores and macropores characterised by modal weight , mean radius and spread Band cut‑offs are anchored in drainage capillary thresholds extracted from reference data (largest entry and percolation pressures), ensuring classes correspond to hydraulically meaningful activation pressures. Model fits are excellent (R² > 0.90). The grouped parameters show strong, physically consistent Spearman correlations with the laboratory petrophysics: permeability increases with and and decreases with . Consistent trends in sonic transit time, bulk density, and electrical resistivity corroborate these relationships. Image‑derived bands predict the shape of -water relative permeability curves and, in compositional -brine simulations (2,500-day injection plus 2,000 years shut‑in), reproduce plume stability, vertical rise, capillary trapping and dissolution signatures. A ternary projection of () provides an operational classification into micro-constrained, meso-dominated, and macro-connected pore systems. This classification anticipates the mobility ratio, fingering propensity, and sweep efficiency. The framework provides a compact, transport‑aware bridge from 3D pore geometry to reservoir scale behaviour, enabling quantitative screening, design and monitoring of geologic storage.

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.

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.

Characterising the complexity of spatial patterns and their underlying physics using a nonlinear approach is growing in many fields. Many features in geomaterials, such as pore and fracture systems, exhibit scaling behaviour, allowing their properties to be characterised using fractal theory.The widely used fractal dimension is a ratio that compares how the level of detail in a structure varies with its size, measuring its space-filling ability. Lacunarity, derived from the Latin word "lacuna," meaning "gap," quantifies the “voidness” of a texture. Nevertheless, neither fractal dimension nor lacunarity can characterise the percolating properties of a fractal. Mandelbrot coined the concept of succolarity. Given that "percolare" in Latin translates to "to flow through," the term "succolare" (sub-colare) aptly conveys the concept of "to nearly flow through" in neo-Latin. A succolating fractal is characterised by almost containing the connecting paths that permit percolation, i.e., one below the percolation threshold. However, it remains a less known notion than the other two fractal counterparts. In the last ten years, succolarity has evolved from an idea to a computable parameter. It has characterised many patterns in different scales and fields, such as medical objects, material surfaces, and networks from nano-micropores to rivers.In this contribution, we aim (i) to understand the physical meaning of succolarity and how it relates to pore networks and other petrophysical properties across different scales, and (ii) to provide new approaches to succolarity calculation. We implemented the succolarity algorithm using the gliding box-counting method. We then re-examined the published datasets for validation and comparison. The succolarity for 2/3D images of rock samples and synthetic models with various porous structures was also calculated for deeper understanding. Finally, we correlated the succolarity results with porosity, permeability, and other petrophysical parameters.Our findings reveal that (i) succolarity contains information about a structure's anisotropy, phase fraction (e.g., porosity in the case of pore space), and percolation information. (ii) It is susceptible to connectedness. As we cut out smaller pores of a structure, succolarity decreases linearly until a pore size (porosity) threshold is reached; it drops significantly and follows a power law. (iii) Succolarity (Su) and permeability are fitted to an exponential relation: k = aebSu. The computation of succolarity excludes isolated pores for a given flooding direction, allowing it to reflect the flow properties better than porosity alone. (iv) Moreover, it is worth noting that using pressure or velocity field in the succolarity calculation algorithm would endow it with a clearer physical meaning than being a proxy for porosity.

One of the main challenges in soil science lies in the passage from heterogeneous soil structure to a quantified multi-scale 3D model. Here a new approach to quantify the microbial distribution relating to soil pore structure is presented. Characterising 3D microbial soil structural in digital porous media is not found and most soil process models tend to assume a homogenous spatial distribution of microbes. We measured the in situ spatial distribution of bacteria in arable soils across scales from sub-micrometers to metres and here we describe further progress to quantify and explicitly model the 3D microbial distributions, based on a stochastic Bayesian approach to predict spatial variation in the underlying bacterial intensity measure. A 3D higher order Multi-Markov chain model is introduced to model complex geometry of real soil structure and associated microbial distribution. In this study, Markov random fields are used to construct multiscale 3D Pore Architecture Models (PAM). The binary structure of PAM has been successfully used to predict multiphase flow behaviour in porous media such as hydrocarbon bearing reservoir rocks, we explore further to use such a new multi-components scheme in modelling pore structure incorporating with microbial spatial distribution, the multicomponent Markov chain model, which is a stationary multiple higher order Markov chain. The models parameterisation is based on high resolution SEM images of soil that have been prepared in a manner that preserves the microbial community information in situ. Based on the quantified 3D multiscale soil structure associated with microbial distribution components, the accurate reactive flow of microbial degradation can be simulated to predict environmental impact of microbial activates in the field. A variety of examples of structures and bacterial distribution created by the models are presented.

The ultrasound method is a low-cost, environmentally safe technology that may be utilized in the petroleum industry to boost oil recovery from the underground reservoir via enhanced oil recovery or well stimulation campaigns. The method uses a downhole instrument to propagate waves into the formation, enhancing oil recovery and/or removing formation damage around the wellbore that has caused oil flow constraints. Ultrasonic technology has piqued the interest of the petroleum industry, and as a result, research efforts are ongoing to fill up the gaps in its application. This paper discusses the most recent research on the investigation of ultrasound’s applicability in underground petroleum reservoirs for improved oil recovery and formation damage remediation. New study areas and scopes were identified, and future investigations were proposed.

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.