Search Results (135)

Blast-induced airblast poses a significant environmental and operational issue for surface mining, affecting safety, regulatory adherence, and the well-being of surrounding communities. Despite advancements in machine learning methods for predicting airblast, present studies neglect essential geomechanical characteristics, specifically rock mass strength (RMS), which is vital for energy transmission and pressure-wave attenuation. This paper presents a capuchin search algorithm-optimized multilayer perceptron (CapSA-MLP) that incorporates RMS, hole depth (HD), maximum charge per delay (MCPD), monitoring distance (D), total explosive mass (TEM), and number of holes (NH). Blast datasets from a granite quarry were utilized to train and test the model in comparison to benchmark approaches, such as particle swarm optimized artificial neural network (PSO-ANN), multivariate regression analysis (MVRA), and the United States Bureau of Mines (USBM) equation. CapSA-MLP outperformed PSO-ANN (RMSE = 1.120, R² = 0.904 compared to RMSE = 1.284, R² = 0.846), whereas MVRA and USBM exhibited lower accuracy. Sensitivity analysis indicated RMS as the main input factor. This study is the first to use CapSA-MLP with RMS for airblast prediction. The findings illustrate the significance of metaheuristic optimization in developing adaptable, generalizable models for various rock types, thereby improving blast design and environmental management in mining activities. Full article

Cone Penetration Tests with pore pressure measurements (CPTu) are widely used in geotechnical site investigations due to their high-resolution profiling capabilities. However, traditional interpretation methods—such as the Soil Behavior Type Index ($I_c$)—often fail to capture the internal heterogeneity typical of mining tailings deposits. This study presents a machine learning-based approach to enhance stratigraphic interpretation from CPTu data. Four unsupervised clustering algorithms—k-means, DBSCAN, MeanShift, and Affinity Propagation—were evaluated using a dataset of 12 CPTu soundings collected over a 19-year period from an iron tailings dam in Brazil. Clustering performance was assessed through visual inspection, stratigraphic consistency, and comparison with $I_c$-based profiles. k-means and MeanShift produced the most consistent stratigraphic segmentation, clearly delineating depositional layers, consolidated zones, and transitions linked to dam raising. In contrast, DBSCAN and Affinity Propagation either over-fragmented or failed to identify meaningful structures. The results demonstrate that clustering methods can reveal behavioral trends not detected by $I_c$ alone, offering a complementary perspective for understanding depositional and mechanical evolution in tailings. Integrating clustering outputs with conventional geotechnical indices improves the interpretability of CPTu profiles, supporting more informed geomechanical modeling, dam monitoring, and design. The approach provides a replicable methodology for data-rich environments with high spatial and temporal variability. Full article

With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article

Empirical and numerical methodologies for the geomechanical assessment of underground excavations have evolved in recent years to adapt to the geotechnical and structural conditions of natural caves, enabling stability evaluation and ensuring safe conditions for speleological exploration. This study analyzes the evolution of the state of the art of these techniques worldwide, assessing their reliability and application context, and identifying the most suitable methodologies for determining the stability of the Al-Badia lava tube. The research was conducted through bibliographic analysis and rock mass characterization using empirical geomechanical classifications. Subsequently, the numerical boundary element method (BEM) was applied to compare the obtained results and model the stress–strain behavior of the cavity. The results allowed the classification of the Al-Badia lava tube into stable, transition, and unstable zones, using empirical support charts and determining the safety factors of the surrounding rock mass. The study site highlights that empirical methods are rather conservative, and numerical results align better with observed conditions. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

This paper presents the procedures and results of the numerical modelling and simulations performed to analyse an innovative method of advanced methane drainage employing underground long-reach directional drilling (LRDD) technology. The analysis involved the implementation of geomechanical and dynamic reservoir models to simulate processes in coal seams and the surrounding rocks during coal mining and concurrent methane drainage, in accordance with the proposed technology. The analysis aimed to quantitatively assess the effectiveness of the technology, evaluate its sensitivity to the geological and geomechanical properties of the rocks, and identify the potential for optimisation of its technological and operational parameters in the proposed strategy. The works presented in this paper include the following key tasks: the construction of a system of geological, geomechanical, and dynamic simulation models; the analysis of the geomechanical effects of various types and regions of occurrence; the implementation of the correlation between the geomechanical states of the rocks and their transport properties; and the performance of the effectively coupled geomechanical and reservoir fluid flow simulations. The proposed approach was applied to the specific conditions of the multi-seam Murcki–Staszic Coal Mine operated by Jastrzębska Spółka Węglowa, Poland. Full article

Geological storage of CO₂ in offshore deep saline aquifers is widely recognized as an effective strategy for large-scale carbon emission reduction. This study aims to assess the mechanical integrity and storage efficiency of reservoirs using a multi-layer CO₂ injection method in the Enping 15-1 Oilfield CO₂ storage project which is the China’s first offshore carbon capture, utilization, and storage (CCUS) demonstration. A coupled Hydro–Mechanical (H–M) model is constructed using the TOUGH-FLAC simulator to simulate a 10-year CO₂ injection scenario, incorporating six vertically distributed reservoir layers. A sensitivity analysis of 14 key geological and geomechanical parameters is performed to identify the dominant factors influencing injection safety and storage capacity. The results show that a total injection rate of 30 kg/s can be sustained over a 10-year period without exceeding mechanical failure thresholds. Reservoirs 3 and 4 exhibit the greatest lateral CO₂ migration distances over the 10-year injection period, indicating that they are the most suitable target layers for CO₂ storage. The sensitivity analysis further reveals that the permeability of the reservoirs and the friction angle of the reservoirs and caprocks are the most critical parameters governing injection performance and mechanical stability. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article

Enhanced or engineered geothermal systems (EGSs), or non-hydrothermal resources, are highly notable among sustainable energy resources because of their abundance and cleanness. The EGS concept has received worldwide attention and undergone intensive studies in the last decade in the US and around the world. In comparison, hydrothermal reservoir resources, the ‘low-hanging fruit’ of geothermal energy, are very limited in amount or availability, while EGSs are extensive and have great potential to supply the entire world with the needed energy almost permanently. The EGS, in essence, is an engineered subsurface heat mining concept, where water or another suitable heat exchange fluid is injected into hot formations to extract heat from the hot dry rock (HDR). Specifically, the EGS relies on the principle that injected water, or another working fluid, penetrates deep into reservoirs through fractures or high-permeability channels to absorb large quantities of thermal energy by contact with the host hot rock. Finally, the heated fluid is produced through production wells for electricity generation or other usages. Heat mining from fractured EGS reservoirs is subject to complex interactions within the reservoir rock, involving high-temperature heat exchange, multi-phase flow, rock deformation, and chemical reactions under thermal-hydrological-mechanical (THM) processes or thermal-hydrological-mechanical-chemical (THMC) interactions. In this paper, we will present a THM model and reservoir simulator and its application for simulation of hydrothermal geothermal systems and EGS reservoirs as well as a methodology of coupling thermal, hydrological, and mechanical processes. A numerical approach, based on discretizing the thermo-poro-elastic Navier equation using an integral finite difference method, is discussed. This method provides a rigorous, accurate, and efficient fully coupled methodology for the three (THM) strongly interacted processes. Several programs based on this methodology are demonstrated in the simulation cases of geothermal reservoirs, including fracture aperture enhancement, thermal stress impact, and tracer transport in a field-scale reservoir. Results are displayed to show geomechanics’ impact on fluid and heat flow in geothermal reservoirs. Full article

Deep coalbed methane (CBM) resources represent a significant opportunity for future exploration and development. The combination of horizontal well drilling and hydraulic fracturing technology has emerged as the most efficient method for extracting deep CBM. By optimizing the fracturing parameters for horizontal wells, we can improve the effectiveness of reservoir stimulation even further. In this paper, taking the deep coalbed methane in the Daniudi gas field in the Ordos Basin as the research object, using Numerical simulation software such as Petrel, comprehensively considering the field logging, logging data and laboratory experimental data of rock mechanical parameters, the three-dimensional geomechanical and stress field model of deep coalbed methane is established, and on this basis, the numerical simulation research on the fracture network expansion and construction parameter optimization of single well and well group is carried out. Through the qualitative evaluation of fracture network morphology, the change of in situ stress field, the quantitative evaluation of post-pressure conductivity and fracture volume, the section spacing, construction fluid volume, and construction displacement under the conditions of single well and well group were optimized. The results show that under the condition of a certain well spacing, the fracture propagation of the well group is affected by stress shadowing and channeling, and the fracture pattern is more complex, and the construction scale is smaller than that of a single well. These findings provide critical insights for improving the efficiency of deep CBM recovery. Full article

Compared with traditional gas reservoirs, ultra-deep and ultra-high-pressure tight sandstone gas reservoirs are characterized by well-developed faults and fractures, strong heterogeneity and stress sensitivity, and complex in situ stress distribution. Traditional three-dimensional geological models and numerical models ignore the variation characteristics of reservoir in situ stress during the production process, it affects the accuracy of the subsequent fracturing modification design and development plan formulation. Therefore, based on the integrated method of geological engineering, this article first carried out high-temperature and high-pressure stress sensitivity tests on reservoir rock samples and fitted the stress-sensitive mathematical model to clarify the influence of high temperature and high pressure on permeability. Then, aiming at the problem of four-dimensional in situ stress variation caused by the coupling of the seepage field and stress field during the exploitation of tight sandstone gas reservoirs, combined with the results of well logging interpretation, rock physical property analysis, and mechanical experiments, based on the three-dimensional geological model and geomechanical model of the gas reservoir and coupled with the stress-sensitive characteristics of the reservoir, a four-dimensional in situ stress model for the reservoir of tight sandstone gas reservoirs was established. The prediction of the variation law of four-dimensional in situ stress during the production process was carried out. Finally, the influence of considering stress sensitivity on reservoir production was simulated. The results show the following: ① The production process has a significant impact on the magnitude and distribution of four-dimensional in situ stress. With the decrease in pore pressure, both the maximum horizontal principal stress and the minimum horizontal principal stress decrease. ② In the area near the production well, the direction of in situ stress will significantly deflect over time. ③ In an ultra-deep and ultra-high-pressure environment, the gas reservoir is affected by the stress-sensitive effect. The stable production time of the gas well is reduced by two years, and the cumulative gas production decreases by 5.01 × 10⁸ m³. The research results provide the temporal stress field distribution results for the simulation and prediction of the secondary fracturing of old wells and the commissioning fracturing of new wells in the target well area. Full article

Understanding the in situ stress state and mechanical properties of rock masses is essential for ensuring the stability and safety of quarrying operations. This study aims to estimate the natural stress state of rock using the CSIRO HI (Hollow Inclusion) triaxial overcoring method; we also conducted numerical modelling by applying the Distinct Element Method (DEM) for stability assessments in quarry environments. The investigation provided comprehensive insights into the geomechanical properties of the rock mass and the stability of quarry fronts. Precise measurements and analyses of in situ stress contributed to a detailed understanding of stress distribution within the rock. Additionally, biaxial compression tests further characterized the mechanical behavior of the rock, which was essential for accurate modelling and simulation. Numerical modelling using DEM facilitated an in-depth stability analysis, allowing evaluation of potential failure mechanisms and proposal of effective mitigation strategies. The 3D numerical model was calibrated using in situ measurements from CSIRO HI data and was employed to simulate future excavations. DEM modelling was particularly crucial because of the fractured nature of the rock mass, which necessitated thorough stability verification in excavation design simulations. This research advances the scientific understanding of stress distribution and mechanical behavior in jointed rock masses, ultimately contributing to the development of safer and more efficient quarrying practices. Full article

Conventional rock mechanical testing approaches encounter significant limitations when applied to deeply buried fractured formations, constrained by formidable sampling difficulties, prohibitive costs, and intricate specimen preparation demands. This investigation pioneers an innovative nanoindentation-based multiscale methodology (XRD–ED–SEM integration) that revolutionizes the mechanical characterization of dolostone through drill cuttings analysis, effectively bypassing conventional coring requirements. Our integrated approach combines precision surface polishing with advanced indenter calibration protocols, enabling the continuous stiffness method to achieve unprecedented measurement accuracy in determining micromechanical properties—notably an elastic modulus of 119.47 GPa and hardness of 5.88 GPa—while simultaneously resolving complex indentation size effect mechanisms. The methodology reveals three critical advancements: remarkable 92.7% dolomite homogeneity establishes statistically significant elastic modulus–hardness correlations (R² > 0.89), while residual imprint analysis uncovers a unique brittle–plastic interaction mechanism through predominant rhomboid plasticity (84% occurrence) accompanied by microscale radial cracking (2.1–4.8 μm). Particularly noteworthy is the identification of load-dependent property variations, where surface hardening effects and defect interactions cause 28.7% parameter dispersion below 50 mN loads, progressively stabilizing to <8% variance at higher loading regimes. By developing a micro–macro bridging model that correlates nanoindentation results with triaxial test data within a 12% deviation, this work establishes a groundbreaking protocol for carbonate reservoir evaluation using minimal drill cutting material. The demonstrated methodology not only provides crucial insights for optimizing hydraulic fracture designs and wellbore stability assessments, but it also fundamentally transforms microstructural analysis paradigms in geomechanics through its successful application of nanoindentation technology to complex geological systems. Full article