Search Results (295)

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering. Full article

This study presents an expert system for assessing the stability of underground mine workings and automatically selecting rock bolt support schemes. The system integrates physically based calculations of roof compressive strength (Rc) and expected maximum displacement (Um) with rule-based decision logic grounded in engineering practice. Unlike empirical classifications and black-box AI models, the proposed approach ensures interpretable, reproducible, and context-aware engineering decisions. The architecture includes a numerical solver that computes Rc and Um based on excavation geometry, geomechanical properties, and mining conditions. The support scheme is selected using a knowledge base of formalized rules, while the specific support parameters are calculated within the solver. The system was validated across 51 underground excavations, with approximately 85% of its recommendations matching field-proven support solutions, and 12% suggesting reinforced schemes that could have prevented failures. The expert system is suitable for integration into digital mine management platforms and offers a foundation for developing digital twin solutions in geomechanically variable environments. Full article

The applicability of similar materials is a key factor affecting the results of geomechanical model tests. In order to investigate the multi-physical field evolution mechanism of surrounding rocks during water inrush disasters in tunnels crossing fault zones, based on the similarity theory of geomechanical model tests, the physical–mechanical parameters of a prototype rock’s mass were first analyzed for similarity, and the target values of similar materials were determined. Secondly, using sand as coarse aggregate, talcum powder as fine aggregate, gypsum and clay as binders, and Vaseline as a regulator, a fault-simulating material suitable for model tests was developed through extensive laboratory experiments. Finally, with material deformation characteristics and strength failure characteristics as key control indicators, parameters such as uniaxial compressive strength, permeability coefficient, unit weight, and elastic modulus are synergistically regulated to determine the influence of different component ratios on material properties. The experimental results show that the uniaxial compressive strength and permeability coefficient of similar materials are mainly controlled by gypsum and Vaseline. Cohesion is mainly controlled by clay and Vaseline. The application of this similar material in the model test of the tunnel fault water inrush disaster successfully reproduced the disaster evolution process of fault water inrush, meeting the requirements of the model test for similar materials of faults. Furthermore, it provides valuable guidance for the selection of similar materials and the optimization of mix proportions for fault disaster model tests involving similar characteristics. Full article

Underground hydrogen storage (UHS) in shale and tight reservoirs offers a promising solution for large-scale energy storage, playing a critical role in the transition to a hydrogen-based economy. However, the successful deployment of UHS in these low-permeability formations depends on a thorough understanding of the geomechanical and geochemical factors that affect storage integrity, injectivity, and long-term stability. This review critically examines the geomechanical aspects, including stress distribution, rock deformation, fracture propagation, and caprock integrity, which govern hydrogen containment under subsurface conditions. Additionally, it explores key geochemical challenges such as hydrogen-induced mineral alterations, adsorption effects, microbial activity, and potential reactivity with formation fluids, to evaluate their impact on storage feasibility. A comprehensive analysis of experimental studies, numerical modeling approaches, and field applications is presented to identify knowledge gaps and future research directions. Full article

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

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. 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

The development of mining operations in high-altitude regions is associated with a number of geomechanical challenges caused by increased rock fracturing, adverse climatic conditions, and high seismic activity. These issues are particularly relevant for the exploitation of gold ore deposits, where the stability of open-pit slopes directly affects both safety and extraction efficiency. The aim of this study is to develop and practically substantiate a comprehensive approach to assessing and ensuring slope stability, using the Bozymchak gold ore deposit—located in a high-altitude and seismically active zone—as a case study. The research involves the laboratory testing of rock samples obtained from engineering–geological boreholes, field shear tests on rock prisms, laser scanning of pit slopes, and digital geomechanical modeling. The developed calculation schemes take into account the structural features of the rock mass, geological conditions, and the design contours of the pit. In addition, special bench excavation technologies with pre-shear slotting and automated GeoMoS monitoring are implemented for real-time slope condition tracking. The results of the study make it possible to reliably determine the strength characteristics of the rocks under natural conditions, identify critical zones of potential collapse, and develop recommendations for optimizing slope parameters and mining technologies. The implemented approach ensures the required level of safety. Full article

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

This study investigates the stability risks associated with a substandard-thickness (42 m) backfill isolation layer in the open-underground coordinated mining system of the Yongping Copper Mine’s eastern panel at the −150 m level. A numerical simulation based on FLAC3D 3.00 was conducted to evaluate the impacts of four mining sequences (south-to-north, north-to-south, center-to-flank, and flank-to-center) on stress redistribution and displacement evolution. A three-dimensional geomechanical model incorporating lithological parameters was established, with 23 monitoring points tracking stress and displacement dynamics. Results indicate that the mining sequence significantly influences the stability of both the isolation layer and the slope. No abrupt displacement occurred during mining, with incremental isolation layer settlement controlled within 3 mm. Post-mining maximum displacement increased to 10–12 mm. The “north-to-south” sequence emerged as the theoretically optimal solution, reducing cumulative displacements in pillars and stopes by 9.1% and 7.8%, respectively, compared to the suboptimal scheme. However, considering the engineering continuity of the existing “south-to-north” sequence at the −100 m level, maintaining consistent directional mining at the −150 m level is recommended to ensure synergistic disturbance control, ventilation system stability, and operational management coherence. Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. 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