Search Results (629)

Traps are central to petroleum exploration, where hydrocarbons accumulate during migration. Reservoirs are likewise an essential petroleum system element and serve as the primary medium for hydrocarbon storage. The buried hill is a geological formation highly favorable for reservoir development. However, the factors influencing hydrocarbon accumulation in buried hill reservoirs are highly diverse, especially in areas with complex, active fault systems. Fault systems play a dual role, both in the formation of reservoirs and in the migration of hydrocarbons. Therefore, understanding the impact of complex fault systems helps enhance the exploration success rate of buried hill traps and guide drilling deployment. In the Beibuwan Basin in the South China Sea, buried hill traps are key targets for deep-buried hydrocarbon exploration in this faulted basin. The low level of exploration and research in buried hills globally limits the understanding of hydrocarbon accumulation conditions, thereby hindering large-scale hydrocarbon exploration. By using drilling data, logging data, and seismic data, stress fields and tectonic faults were restored. There are two types of buried hills developed in the Beibuwan Basin, which were formed during the Late Ordovician-Silurian period and Permian-Triassic period, respectively. The tectonic genesis of the Late Ordovician-Silurian period buried hills belongs to magma diapirism activity, while the tectonic genesis of the Permian-Triassic period buried hills belongs to reverse thrust activity. The fault systems formed by two periods of tectonic activity were respectively altered into basement buried hills and limestone buried hills. The negative structural inversion controls the distribution and interior stratigraphic framework of the deformed Carboniferous strata in the limestone buried hill. The faults and derived fractures of the Late Ordovician-Silurian period and Permian-Triassic period promoted the diagenesis and erosion of these buried hills. The faults formed after the Permian-Triassic period are not conducive to calcite cementation, thus facilitating the preservation of the reservoir space formed earlier. The control of hydrocarbon accumulation by the fault system is reflected in two aspects: on the one hand, the early to mid-Eocene extensional faulting activity directly controlled the depositional process of lacustrine source rocks; on the other hand, the Late Eocene-Oligocene, which is closest to the hydrocarbon expulsion period, is the most effective fault activity period for connecting Eocene source rocks and buried hill reservoirs. This study contributes to understanding of the role of complex fault activity in the formation of buried hill traps within hydrocarbon-bearing basins. Full article

Geological storage is an integral element of the green energy transition. Geological formations, such as aquifers, depleted reservoirs, and hard rock caverns, are used mainly for the storage of hydrocarbons, carbon dioxide and increasingly hydrogen. However, potential adverse effects such as ground movements, leakage, seismic activity, and environmental pollution are observed. Existing research focuses on monitoring subsurface elements of the storage, while on the surface it is limited to ground movement observations. The review was carried out based on 191 research contributions related to geological storage. It emphasizes the importance of monitoring underground gas storage (UGS) sites and their surroundings to ensure sustainable and safe operation. It details surface monitoring methods, distinguishing geodetic surveys and remote sensing techniques. Remote sensing, including active methods such as InSAR and LiDAR, and passive methods of multispectral and hyperspectral imaging, provide valuable spatiotemporal information on UGS sites on a large scale. The review covers modelling and prediction methods used to analyze the environmental impacts of UGS, with data-driven models employing geostatistical tools and machine learning algorithms. The limited number of contributions treating geological storage sites holistically opens perspectives for the development of complex approaches capable of monitoring and modelling its environmental impacts. Full article

The growing demand for sustainable, functional ingredients in the food industry has driven interest in marine-derived biopolymers. Among marine sources, microalgae represent a promising yet underexplored reservoir of bioactive gel-forming compounds, particularly extracellular polysaccharides (EPSs), both sulfated and non-sulfated, as well as proteins that exhibit unique gelling, emulsifying, and stabilizing properties. This study focuses on microalgal species with demonstrated potential to produce viscoelastic, shear-thinning gels, making them suitable for applications in food stabilization, texture modification, and nutraceutical delivery. Recent advances in biotechnology and cultivation methods have improved access to high-value strains, which exhibit promising physicochemical properties for the development of novel food textures, structured formulations, and sustainable food packaging materials. Furthermore, these microalgae-derived gels offer additional health benefits, such as antioxidant and prebiotic activities, aligning with current trends toward functional foods containing prebiotic materials. Key challenges in large-scale production, including low EPS productivity, high processing costs, and lack of regulatory frameworks, are critically discussed. Despite these barriers, advances in cultivation technologies and biorefinery approaches offer new avenues for commercial application. Overall, microalgal gels hold significant promise as sustainable, multifunctional ingredients for clean-label food formulations. Full article

Hepatitis E virus (HEV) is a pathogen that has caused various epidemics and sporadic localized cases. It is considered to be a public health problem worldwide. HEV is a small RNA virus with a significant genetic diversity, a broad host range, and a heterogeneous geographical distribution. HEV is mainly transmitted via the faecal–oral route. However, some animals are considered to be natural or potential reservoirs of HEV, thus elucidating the zoonotic route of transmission via the environment through contact with these animals or consumption of their by-products. Other routes of human-to-human transmission are not negligible. The various human–animal–environment entities, taken under one health approach, show the circulation and involvement of the different species (mainly Paslahepevirus balayani and Rocahepevirus ratti) and genotypes in the spreading of HEV infection. Regarding P. balayani, eight genotypes have been described, of which five genotypes (HEV-1 to 4 and HEV-7) are known to infect humans, while six have been reported to infect animals (HEV-3 to HEV-8). Furthermore, the C1 genotype of the rat HEV strain (HEV-C1) is known to be more frequently involved in human infections than the HEV-C2 genotype, which is known to infect mainly ferrets and minks. Contamination can occur during run-off, flooding, and poor sanitation, resulting in all of these genotypes being disseminated in the environment, contaminating both humans and animals. This systematic review followed the PRISMA guidelines and was registered in PROSPERO 2025 CRD420251071192. This research highlights the importance of investigating the transmission routes and major circulating HEV genotypes in order to adopt a holistic approach for controlling its emergence and preventing future outbreaks. In addition, this article outlines the knowledge of HEV in Africa, underlining the absence of large-scale studies at the environmental, human, and animal levels, which could improve HEV surveillance on the continent. Full article

Shale and coal in the transitional marine–continental facies of the Ordos Basin serve as unconventional natural gas reservoirs, with their pore structures controlling gas adsorption characteristics and occurrence states. To quantitatively characterize the pore structure features and differences between these two reservoirs, this study takes the Shanxi Formation shale and coal in the Daning–Jixian area on the eastern margin of the Ordos Basin as examples. Field-emission scanning electron microscopy (FE-SEM), high-pressure mercury intrusion, low-temperature N₂ adsorption, and low-pressure CO₂ adsorption experiments were employed to analyze and compare the full-scale pore structures of the shale and coal reservoirs. Combined with methane isothermal adsorption experiments, the gas adsorption capacity and its differences in these reservoirs were investigated. The results indicate that the average total organic carbon (TOC) content of shale is 2.66%, with well-developed organic pores, inorganic pores, and microfractures. Organic pores are the most common, typically occurring densely and in clusters. The average TOC content of coal is 74.22%, with organic gas pores being the dominant pore type, significantly larger in diameter than those in transitional marine–continental facies shale and marine shale. In coal, micropores contribute the most to pore volume, while mesopores and macropores contribute less. In shale, mesopores dominate, followed by micropores, with macropores being underdeveloped. Both coal and shale exhibit a high SSA primarily contributed by micropores, with organic matter serving as the material basis for micropore development. The methane adsorption capacity of coal is 8–29 times higher than that of shale. Coal contains abundant organic micropores, providing a large SSA and numerous adsorption sites for methane, facilitating gas adsorption and storage. This study comprehensively reveals the similarities and differences in pore structures between transitional marine–continental facies shale and coal reservoirs in the Ordos Basin at the microscale, providing a scientific basis for the precise evaluation and development of unconventional oil and gas resources. Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article

Fractures play a critical role in the storage and migration of hydrocarbons within subsurface reservoirs, and their characteristics can be effectively studied through core sample analysis. This study proposes an automated fracture extraction method for full-diameter core Computed Tomography (CT) images based on a deep learning framework. A semantic segmentation network called SCTNet is employed to perform high-precision semantic segmentation, while a sliding window strategy is introduced to address the challenges associated with large-scale image processing during training and inference. The proposed method achieves a mean Intersection over Union (mIoU) of 72.14% and a pixel-level segmentation accuracy of 97% on the test dataset, outperforming traditional thresholding techniques and several state-of-the-art deep learning models. Besides fracture detection, the method enables quantitative characterization of fracture-related parameters, including fracture proportion, dip angle, strike, and aperture. Experimental results indicate that the proposed approach provides a reliable and efficient solution for the interpretation of large-volume CT data. Compared to manual evaluation, the method significantly accelerates the analysis process—reducing time from hours to minutes—and demonstrates strong potential to enhance intelligent workflows for geological core fracture analysis. Full article

Based on drilling, core and seismic data, combined with the regional tectonic sedimentary evolution background, the sedimentary environment of the Triassic carbonate rocks in the Mangeshlak Basin was studied. A sedimentary facies model of this set of carbonate rocks was established. Research has shown that the Mangeshlak Basin underwent a complete large-scale marine transgression–regression sedimentary evolution process during the Triassic. During the early to middle Triassic, seawater gradually invaded the northwest region of the basin from northwest to southeast and gradually regressed in the late Middle Triassic. In the lower part of the Triassic carbonate rocks, the primary components are developed granular limestone or dolomite with oolitic structures, interspersed with a small amount of thin mudstone, which is a good reservoir; the upper part of the Triassic is mainly composed of sedimentary mudstone and mudstone, which can form good sealings. The hill-shaped reflections of the platform edge facies, along with the high-frequency, strong-amplitude, and moderately continuous reflections within the restricted platform interior, are clearly visible on the seismic profile. These features are consistent with the sedimentary environment and lithofacies characteristics revealed by drilling data along the profile. Drilling and seismic data revealed that the sedimentary environment of the early and middle Triassic in the basin is mainly composed of shallow water platform edges and restricted platforms, as well as carbonate rock slopes and open non-marine shelves in deep water areas. A sedimentary facies model of the Triassic carbonate rock segment in the basin was established, comprising restricted platforms, platform edges, carbonate rock slopes, and non-marine shelves. Unlike the modified Wilson marginal carbonate rock platform model, the carbonate rock platform edge in the Mangeshlak Basin does not develop reef facies. Instead, it is mainly composed of oolitic beach (dam) sediments, making it the most favorable sedimentary facies zone for the Triassic reservoir development in the basin. Full article

Accurate oil production forecasting is essential for optimizing field development and supporting efficient decision-making. However, traditional methods often struggle to capture the complex dynamics of reservoirs, and existing machine learning models rely on large parameter sets, resulting in high computational costs and limited scalability. To address these limitations, we propose the Kolmogorov–Arnold Network (KAN) for oil production forecasting, which replaces traditional weights with spline-based learnable activation functions to enhance nonlinear modeling capabilities without large-scale parameter expansion. This design reduces training costs and enables adaptive scaling. The KAN model was applied to forecast oil production from wells 15/9-F-11 and 15/9-F-14 in the Volve field, Norway. The experimental results demonstrate that, compared to the best-performing baseline model, the KAN reduces the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by 78.5% and 89.5% for well 15/9-F-11 and by 80.1% and 91.8% for well 15/9-F-14, respectively. These findings suggest that the KAN is a robust and efficient multivariate forecasting method capable of capturing complex dependencies in oil production data, with strong potential for practical applications in reservoir management and production optimization. Full article

Unconventional hydrocarbon reservoirs with low matrix permeability (<0.3 mD), high temperatures, and sour conditions present significant challenges for stimulation and production enhancement. This study examines field trials for a large oil and gas operator in the UAE, focusing on tight carbonate deposits with reservoir temperatures above 93 °C and high sour gas content. A novel multi-stage chemical stimulation workflow was created, beginning with a pre-flush phase that alters rock wettability and reduces interfacial tension at the micro-scale. This was followed by a second phase that increased near-wellbore permeability and ensured proper acid placement. The treatment’s core used a thermally stable, corrosion-resistant retarded acid system designed to slow reaction rates, allow deeper acid penetration, and build prolonged conductive wormholes. Simulations revealed considerable acid penetration of the formation beyond the near-wellbore zone. The post-treatment field data showed a tenfold improvement in injectivity, which corresponded closely to the acid penetration profiles predicted by modeling. Furthermore, oil production demonstrated sustained, high oil production of 515 bpd on average for several months after the treatment, in contrast to the previously unstable and low-rate production. Finally, the findings support a reproducible and technologically advanced stimulation technique for boosting recovery in ultra-tight carbonate reservoirs using the acid retardation effect where traditional stimulation fails. Full article

Faults and fracture sets in carbonate reservoirs are key geological features that govern hydrocarbon migration, accumulation, and wellbore stability. Their accurate detection is essential for structural interpretation, reservoir modeling, and drilling risk assessment. In this study, we propose an Optimally Oriented Coherence Attribute (OOCA) method that integrates geological guidance with multi-frequency structural analysis to achieve enhanced sensitivity to faults and fractures across multiple scales. The method is guided by depositional and tectonic principles, constructing model traces along directions with maximal structural variation to amplify responses at geological boundaries. A distance-weighted computation and extended directional model trace strategy are adopted to further enhance the detection of fine-scale discontinuities, overcoming the limitations of traditional attributes in resolving subtle structural features. A Gabor-based multi-frequency fusion framework is employed to simultaneously preserve large-scale continuity and fine-scale detail. Validation using physical modeling and field seismic data confirms the method’s ability to enhance weak fault imaging. Compared to traditional attributes such as C3 coherence, curvature, and instantaneous phase, OOCA delivers significantly improved spatial resolution. In zones with documented lost circulation, the identified structural features align well with drilling observations, demonstrating strong geological adaptability and engineering relevance. Overall, the OOCA method offers a geologically consistent and computationally efficient solution for high-resolution fault interpretation and drilling risk prediction in structurally complex carbonate reservoirs. Full article

Seismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional machine learning methods. To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). A case study in the Ordos Basin further demonstrated that UMAP-CNN offers a high degree of accuracy in predicting coal seam thickness. Furthermore, our framework exhibited superior computational efficiency and robustness in handling large-scale datasets. Full article

In this study, a comprehensive experimental framework was developed to quantitatively characterize the pore structure of a deep coal-rock (DCR; reservoirs below [3000 m]) gas reservoir. Experimentally, petrological and mineral characteristics were determined by performing proximate analysis and scanning electron microscopy (SEM) as well as by measuring vitrinite reflectance and maceral components. Additionally, physisorption and high-pressure mercury injection (HPMI) tests were conducted to quantitatively characterize the nano- to micron-scale pores in the DCR gas reservoir at multiple scales. The DCR in the NQ Block is predominantly composed of vitrinite, accounting for approximately 77.75%, followed by inertinite. The pore space is predominantly characterized by cellular pores, but porosity development is relatively limited as most of such pores are extensively filled with clay minerals. The isothermal adsorption curves of CO₂ and N₂ in the NQ Block and the DJ Block exhibit very similar variation patterns. The pore types and morphologies of the DCR reservoir are relatively consistent, with a significant development of nanoscale pores in both blocks. Notably, micropore metrics per unit mass (pore volume (PV): 0.0242 cm³/g; and specific surface area (SSA): 77.7545 m²/g) indicate 50% lower gas adsorption potential in the DJ Block. In contrast, the PV and SSA of the mesopores per unit mass in the NQ Block are relatively consistent with those in the DJ and SF Blocks. Additionally, the peak mercury intake in the NQ Block occurs within the pore diameter < 20 nm, with nearly 60% of the mercury beginning to enter in large quantities only when the pore size exceeds 20 nm. This indicates that nanoscale pores are predominantly developed in the DCR of the NQ block, which aligns with the findings from physical adsorption experiments and SEM analyses. Overall, the development characteristics of multi-scale pores in the DCR formations of the NQ Block and the eastern part of the Basin are relatively similar, with both total PV and total SSA showing an L-shaped distribution. Due to the disparity in micropore SSA, however, the total SSA of the DJ Block is approximately twice that of the NQ Block. This discovery has established a robust foundation for the subsequent exploitation of natural gas resources in DCR formations within the NQ Block. Full article

Complex carbonate reservoirs feature large-scale karst cavern structures, exhibiting complex pore and bottom water energy distributions, which increase the difficulty of reservoir development and require targeted research. This paper proposes a new method for dynamic reserves calculation in these reservoirs based on the Differential Processing Method (DPM) and aimed at optimizing the development of complex reservoirs. The AD22 unit of the Tarim Oilfield in Xinjiang is taken as the research object, and this reservoir features complex karst and fault characteristics, which traditional reserves calculation methods cannot effectively capture due to its complex heterogeneous distribution. This study constructs a refined reservoir numerical model through 3D geological modeling and impedance inversion techniques, calculates dynamic reserves using the DPM, and compares the result with traditional material balance and production data analysis methods. The results indicate that the DPM has an advantage in estimating the petrophysical parameters and reserve utilization in such reservoirs. The error between the constructed reservoir numerical model and the actual reservoir development historical data is only 2.04%, demonstrating a good reference value. The model shows that more than 60% of the recoverable reserves in the target unit are located in areas shallower than 160 m underground, while the current development degree is only 12.6%. The model shows that the recovery rate is low in the strong bottom water energy areas of the unit, while the recovery potential is high in the weak bottom water areas. Therefore, a differentiated development strategy based on varying bottom water energy is required to enhance development efficiency. The model indicates that this strategy can improve the comprehensive development benefits of the reservoir by 81.66% over the existing baseline, demonstrating significant potential. This study provides new ideas and methods for dynamic reserve estimation and development strategy optimization for complex carbonate reservoirs, verifies the effectiveness of the DPM in evaluating the development of complex bottom water energy reservoirs, and offers data references for related research and field applications. Full article