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.

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.

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.

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.