Search Results (29)

The article presents an analysis of the complex mechanism of abrasion of shorelines built of non-lithified sediments as a result of rising water levels in the reservoir, along with its quantitative assessment. It allows forecasting the actual risks of coastal areas intendent for urbanization with similar morphology and geological structure. The task of the article is also to point out that for proper assessment of abrasion it is necessary to take into account the greater complexity of the mechanism in which abrasion is the result of co-occurring processes of erosion and landslides. During the analysis, the classic Kachugin method of abrasion assessment was combined with an analysis of the stability of the abraded slope, taking into account the circular slip surface (Bishop and Morgenster–Price methods) and the breaking slip surface (Sarma method). This approach required the assessment of the geotechnical properties of the soil using, among other things, advanced in situ methods such as static sounding. The results indicate that the cliff edge is in limit equilibrium or even in danger of immediate landslide. At the same time, it was possible to determine the horizontal extent of a single landslide at 1.2 to 5.8 m. In the specific cases of reservoir filling, the consideration of the simultaneous action of both failure mechanisms definitely worsens the prediction of shoreline sustainability and indicates the need to restrict construction development in the coastal zone. Full article

Accurate and efficient modeling of subsurface flow in reservoir simulations is essential for optimizing hydrocarbon recovery, enhancing water management strategies, and informing critical decision-making processes. However, traditional numerical simulation methods face significant challenges due to their high computational cost and limited scalability in handling large-scale models with uncertain geological parameters, such as permeability distributions. To address these limitations, we propose a novel deep learning-based framework leveraging a conditional U-Net architecture with time embedding to improve the efficiency and accuracy of reservoir data assimilation. The U-Net is designed to train on permeability maps, which encode the uncertainty in geological properties, and is trained to predict high-resolution saturation and pressure maps at each time step. By utilizing the saturation and pressure maps from the previous time step as inputs, the model dynamically captures the spatiotemporal dependencies governing multiphase flow processes in reservoirs. The incorporation of time embeddings enables the model to maintain temporal consistency and adapt to the sequential nature of reservoir evolution over simulation periods. The proposed framework can be integrated into a data assimilation loop, enabling efficient generation of reservoir forecasts with reduced computational overhead while maintaining high accuracy. By bridging the gap between computational efficiency and physical accuracy, this study contributes to advancing the state of the art in reservoir simulation. The model’s ability to generalize across diverse geological scenarios and its potential for real-time reservoir management applications, such as optimizing production strategies and history matching, underscores its practical relevance in the oil and gas industry. Full article

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 10⁶ m³, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 10⁶ m³ and 1 × 10⁶ m³) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management. Full article

Over the last 25 years, research on geoparks has moved from basic research to comprehensive multidisciplinary studies related to the creation and development of geoparks, integrating the principle of sustainability. This research focuses on exploring geoparks as the core subject. The aim of this study is to synthesize the heterogeneous body of knowledge about geoparks in an exhaustive way by leveraging a multi-database bibliometric approach. The methodology applied is based on quantitative bibliometric analysis using R, including its application for non-coders and ensuring reliability with the PRISMA Statement framework. Ten databases were taken as the sources of research papers: Web of Science, Scopus, PubMed, Nature Journals, SpringerLink, Taylor & Francis, Wiley Journals, IEEE Xplore, and CABI. The method we used has limitations, providing a restricted number of trends aligned and scaled to the database boundary conditions used in analysis. The main goals of quantitative bibliometric analysis are as follows: (1) The impact of data integration—Evaluating how merging the data from the ten databases improves research coverage. (2) Global research trends—Identifying the evolution of geopark-related studies over time. (3) Three-year forecast—Predicting the upcoming research directions using a polynomial regression model. (4) Academic performance—Assessing geographical distribution, citation impact, and productivity using bibliometric laws. (5) Conceptual contribution—Identifying the key research themes that drive future studies and potential areas for exploration. Among these, we highlighted the key elements. The integration of the ten databases provides 63% greater insight into scientific research compared to that of the Web of Science (WoS) database. Geographically, the scientific output spans 102 countries, with China leading in production over the last two decades. The most impactful paper has accumulated 768 citations, while Ruben D.A. and Wu Fandong emerge as the most prolific authors. According to the bibliometric law, the core source of scientific output is Geoheritage. The future research directions are expected to address global challenges, particularly natural disasters in alignment with the Sustainable Development Goals (SDGs). Additionally, GIS-based subtopics leveraging advanced technologies for analyzing, mapping, and promoting geological resources represent a promising area for further exploration. The projections indicate that by the end of 2026, scientific production in this field could reach 5226 published papers, underscoring the growing significance of geopark research and interdisciplinary advancements. Full article

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters. Full article

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development. Full article

With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. Full article

This study provides a theoretical foundation for optimizing tunnel boring machine (TBM) excavation parameters under diverse geological conditions, offering significant engineering value by enhancing construction efficiency and reducing costs. As the development of underground spaces advances, TBMs play a pivotal role in tunnel excavation. TBMs enhance safety in excavation by mechanically breaking rock, reducing the reliance on explosives, and the associated risks of blasts. The shield support minimizes surrounding rock collapse, advanced geological forecasting mitigates risks posed by complex geologies, and intelligent monitoring systems improve operational safety. To enhance TBM efficiency and safety, this study developed a 3D simulation model of rock breaking by disc cutters using the discrete element method. This study systematically examined the effects of excavation parameters, including disc-cutter diameter, cutter spacing, and penetration, on rock-breaking performance. The findings reveal, that as the disc-cutter diameter increases, the rolling force also increases, while the rock-breaking specific energy initially rises and then declines. The 19-inch disc cutter demonstrated a superior rock-breaking efficiency in conventional excavation operations. At a cutter spacing of 60 mm, the rock-breaking specific energy reached its lowest value, representing optimal efficiency. Furthermore, as the penetration increased, both the rolling force and rock fragmentation volume grew, whereas the specific energy decreased, further improving the rock-breaking efficiency. Full article

This document introduces a novel equivalent algorithm for forecasting gas content within deep coal seams, which is subject to constraints stemming from the advancements and precision achieved in well and roadway engineering endeavors. This algorithm meticulously acknowledges that coal seam gas content comprises three fundamental components: the inherent gas emission rate of the equivalent stratum, the residual gas content retained within the coal seam itself, and the influence imparted by the gas content within the coal seam. Furthermore, the approach thoroughly considers variations in the level of porosity development within the coal seam and its surrounding rock formations, as well as the occurrence of gas within these structures. The equivalent layer is classified into two distinct groups: the sandstone zone and the clay zone. The sandstone zone utilizes pertinent parameters pertaining to fine sandstone, whereas the clay zone distinguishes between clay rock and thick mudstone. The influencing factor considerations solely encompass natural elements, such as the coal seam’s occurrence and geological structure. The residual gas content employs either existing measured parameters or acknowledged experimental parameters specific to the coal seam. Based on this predictive approach, an intelligent auxiliary software (V1.0) for mine gas forecasting was devised. The software calculates the gas content of deep coal seams within the mine at intervals of 100 m × 100 m, subsequently fitting the contour lines of gas content across the entire area. The gas content predictions derived from this equivalent algorithm demonstrate robust adaptability to variations in gas content caused by construction activities, and the prediction results exhibit an acceptable level of error on-site. Notably, the prediction process is not constrained by the progress of tunnel engineering, ensuring that the prediction outcomes can accurately represent the distribution characteristics of deep coal seam gas content. After a year of application, the prediction results have consistently met on-site requirements, providing a scientific foundation for the implementation of effective gas prevention and control measures in the mining area. Furthermore, this approach can effectively guide the formulation of medium- and long-term gas prevention and control plans for mines. Full article

Optimizing fracturing parameters is crucial for enhancing production and reducing costs in oil and gas exploration and development. Effectively integrating geological and engineering parameters for the automated optimization of fracturing design continues to pose challenges. This study utilizes the cluster-based local outlier factor method for anomaly detection and removal from the dataset, significantly enhancing data quality. By integrating diverse models, including tree-based models and neural networks, an ensemble model for production prediction was developed. This approach successfully addresses the limitations of relying on a single model and achieves high-precision production forecasting. Furthermore, a Covariance Matrix Adaptation Evolution Strategy (CMA-ES)-based framework was established to comprehensively optimize the design parameters of fracturing projects. Optimization practices for two selected wells resulted in a 168.54% increase in production and identified the optimal design parameter configuration for all cases studied. The results of this study demonstrate the feasibility and effectiveness of the proposed ensemble prediction model and optimization framework in practical applications. Data-driven optimization strategies are expected to play a larger role in future oil and gas development, driving technological innovation and advancement in the field. Full article

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. This study analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, this method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Utilizing numerical simulation, a model for the advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space, and the velocity structure of the model is retrieved through this velocity analysis method. In the engineering example, the fracture property, water content, and weathering degree of the surrounding rock are predicted accurately. Full article

In order to ensure the safety of the tunnel approaching the fault and prevent water inrush disasters, and then take reasonable protective measures, a fault-tunnel-surrounding rock is established by using a three-dimensional (3D) discrete element numerical analysis method, which takes into account the fluid-structure coupling effect. Based on the method of control variables, the catastrophe information characteristics of displacement and water pressure of the surrounding rock of the tunnel face and the corresponding characteristics of changes before the occurrence of water inrush disasters were studied under different fault water pressures during the excavation of the tunnel approaching the water-rich fault. The results show that, during excavation at the same step, displacement and its magnitude in the surrounding rock escalate as fault water pressure increases. The maximum pressure of the water in the surrounding rock is also constantly increasing. As tunnel excavation progresses, at constant fault water pressure, longer excavation distances result in greater axial displacement of the surrounding rock mass and increased water pressure at corresponding positions within the surrounding rock, leading to higher magnitude increases. As excavation proceeds, the displacement and water pressure in the surrounding rock and the increase of its amplitude continue to increase. Pre-reinforcement grouting techniques and pipe umbrella support systems that are very effective protective measures can be determined by a comprehensive approach integrating advanced geological forecasting methods, real-time water pressure detection, and the analysis of stress-strain and seepage pressure field variations in the surrounding rock mass. Full article

Carbonate reservoirs, influenced by depositional and diagenetic processes and characterized by features like faults and vugs that impact storage capacity, require more than traditional Borehole Imaging logs ($BHIs$) for accurate porosity data. These data are essential for geological assessments, production forecasting, and reservoir simulations. This work aims to address this limitation by developing methods to measure and monitor the sustainability of carbonate reservoirs and exploring the application of sustainability principles to their management. The study integrates $BHIs$ and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation ($BVF$) in the Santos Basin. The methodology involves four steps: (i) analyzing conventional logs; (ii) segmenting BHI logs; (iii) integrating conventional and segmented BHI logs using Self-Organizing Maps ($SOM$); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies: (A to D). The results of this research indicate the following: Facies A has high non-matrix porosity with 14,560 small megapores, 5419 large megapores, and 271 gigapores (71.9%, 26.76%, and 1.34% of the 20,250 pores, respectively). Facies B shows moderate non-matrix porosity with 8,669 small megapores, 2642 large megapores, and 33 gigapores (76.42%, 23.29%, and 0.29% of the 11,344 pores, respectively) and medium matrix porosity. Facies C exhibits low non-matrix porosity with 7749 small megapores, 2132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9355 small megapores, 2346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The results of this research reveal the effectiveness of a semiautomatic methodology that combines $BHI$ and conventional well logs to distinguish between matrix and non-matrix-related pore spaces, thus enabling a preliminary classification of reservoir facies based on porosity. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, thus offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes. The novelty of this work lies in integrating data from two sources to classify porosity across the presalt reservoir interval, thus serving as a proxy for preliminary lithofacies identification without core data. Full article

Investigating the construction mechanics of a ventilation tunnel using the TBM (Tunnel Boring Machine) pilot and enlargement method with reliable rock mechanics parameters ensures the safety of on-site excavation operations. Leveraging the construction project of the ventilation tunnel at the Wuhai Pumped Storage Power Station, TGP sidewall forecasting was employed to explore the geological conditions within a 50 m range of the tunnel’s side. A systematic study of the construction mechanics of the TBM pilot and enlargement method was conducted, along with corresponding construction recommendations and engineering applications. This research indicates that sidewall forecasting can supplement the deficiencies in geological exploration reports, with excavation revealing conditions consistent with the forecast. Deformation at the interface, including the arch crown and sidewall, mainly concentrates during the construction phase from the completion of full-section excavation to the beginning of expansion. As the working face advances, the upper rock mass within the ventilation tunnel outline experiences tension, with stress concentration in the shoulder and bottom corner rock masses. The plastic zone before expansion primarily concentrates within the ventilation tunnel outline, shifting to the sidewall after expansion, with the left shoulder’s plastic zone depth slightly exceeding that of the right. The proposed method effectively ensures construction safety, and the research findings have valuable implications for similar projects. Full article

Herein, we propose new methods for interpreting the debris flow phenomena affecting the Sibillini Mountains (central Apennines, Italy), based on the main characteristics and mechanisms of these hazards and their associated risk, as studied by applying advanced GIS tools to a geodatabase including the morphometric parameters of many surveyed debris flows as well as topographic and climatic information. The study area is characterized by mainly calcareous lithologies belonging to the Umbria–Marche Succession, which are frequently covered by Quaternary continental deposits. Slopes and deep transversal valleys are strongly influenced by Pliocene–Quaternary tectonics. Our main objectives were (a) to provide a comprehensive survey of the local morphologies and dynamics of debris flows and localize, catalog, sample and implement them in a geodatabase, as well as monitor them; (b) to forecast potential future debris flows in the study area based upon their evolutionary processes (e.g., dynamic evolution of debris flows, time of recurrence, removed volume of materials) and, in doing so, evaluate hazards and risks for human activities, as well as possibly apply this prediction method to other areas with similar geological and morpho-climatic characteristics; and (c) to share scientific information with society, with the goal of involving citizens in a new and sustainable method of territorial management. Full article