Search Results (419)

Deep coalbed methane reservoirs exhibit strong heterogeneity and complex stress environments, making accurate prediction of casing loads during multi-stage hydraulic fracturing challenging. This study developed a coupled prediction framework integrating three-dimensional geomechanical modeling, modified induced stress calculation, and numerical simulation. The geomechanical model was constructed using logging, drilling, rock mechanics, and in situ stress data and validated against fracture monitoring results. The simulated fracture half-length and stimulated area differed from the monitored values by less than 10% and 8%, respectively, with a spatial matching degree exceeding 92%. A modified analytical model was then established by introducing correction coefficients for fracture net pressure and stress propagation. Among the results obtained using five parameter inversion methods, the sparrow search algorithm achieved the highest fitting accuracy, with an (R²) of 0.9371 and an RMSE of 0.3761, yielding (A = 0.7330) and (B = 0.9238). These coefficients indicate an approximately 26.7% reduction in effective net pressure and enhanced attenuation of induced stress in heterogeneous, cleat-developed coal seams. Furthermore, a multi-parameter casing stress model was developed by coupling treatment scale, injection rate, fracture spacing, and stage number. Sensitivity analysis showed that the number of fracturing stages and injection rate were the dominant factors, followed by fracture spacing and treatment scale. The proposed framework quantitatively characterizes casing stress evolution and facilitates casing load assessment under different multi-stage fracturing conditions. Full article

Numerical modelling is a powerful tool used in geomechanics; however, its reliability depends on proper validation. This study demonstrates the use of intelligent rock bolt measurements to validate the numerical model of underground deformation, providing a practical and applicable approach even with all numerical modelling simplifications. A numerical model of a selected Case Study Location was developed using geomechanical data from laboratory tests (uniaxial compressive strength, triaxial test, and Brazilian test). The numerical model was validated using deformation data collected by intelligent rock bolts installed in the underground mine. Applying statistical data correction methods, the model data accuracy was further improved. Applying Kalman filtering improved the correlation between measured and modelled deformations from 0.90 to 0.98, demonstrating the effectiveness of statistical methods. The novelty of this work lies in the combined use of intelligent rock bolts, FEM simulations, and statistical data correction to achieve a practical and reproducible validation framework, even when simplified geological assumptions are used. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

Underground storage of green hydrogen is a strategic enabler of large-scale renewable deployment, but its feasibility rests on a hard problem: keeping a small, highly mobile molecule confined underground for decades without safety or environmental risk. This critical review examines the containment mechanisms of hydrogen across underground storage types, focusing on geological barriers, well integrity and sealing materials. We evaluate the containment capabilities of salt cavities, deep aquifers and depleted reservoirs, with particular attention to the viscoplastic, self-healing properties of salt that promote confinement, and to the vulnerabilities of well infrastructure and salt–cement interfaces. Emerging alternatives, including lined rock caverns and repurposed abandoned mines, are assessed alongside their distinct operating configurations and use cases. Leakage mechanisms including diffusion, advection, microcracking, cement degradation and hydrogen–material interactions are analysed alongside geomechanical modelling, microbial activity, monitoring strategies, regulatory frameworks, and techno-economic and environmental considerations, including the integration of carbon capture, utilisation and storage (CCUS) with underground hydrogen storage. Well integrity emerges as the dominant risk factor across storage types. The review concludes with design criteria, monitoring priorities and research needs to guide the safe, sustainable deployment of underground hydrogen storage, providing a scientific foundation for future numerical and experimental work on storage tightness. Full article

Coal-bearing tight gas and deep coalbed methane (CBM) widely co-occur in China, and integrated commingled production outperforms separate development. Conventional separated simulation fails to capture coupled reservoir–wellbore gas–water flow. This study develops an integrated geomechanical coupled numerical model with multi-scale fractures and multi-phase wellbore flow: tight gas reservoirs use a stress-sensitive single-porosity model, deep CBM adopts a dual-porosity model for matrix desorption, and EDFM characterizes non-Darcy flow in hydraulic fractures. The Gray gas column and liquid column methods calculate layered bottomhole pressure according to reservoir vertical distribution, and matrix bordering solves the whole coupled system. Validated by field data of Well C-1 in Shanxi, the model yields average relative errors of 8.76% for daily gas output and 2.92% for daily water output. Sensitivity analysis on Well C-2 indicates vertical reservoir stacking controls interlayer pressure difference, and commingled gas curves show dual peaks with shifting dominant gas sources over production stages. A 3.9% rise in deep coalbed methane gas content significantly boosts mid-term peak production and cumulative gas output, making reservoir gas content the dominant geological factor governing commingled production performance. A 120.0% increase in tight gas saturation only delivers a slight uplift in cumulative production under low-porosity conditions. Elevated reservoir stress sensitivity triggers a cumulative gas production reduction of over 50%. Cumulative gas output varies proportionally with hydraulic fracture length, while fracture network width brings mismatched production improvement due to pressure drawdown funnel effects. Therefore, hydraulic fracturing operations should prioritize extending artificial fractures to expand the drainage area of commingled wells. Schemes with constant bottomhole flowing pressure and constant gas rate exert marginal influences on ultimate cumulative production and can be flexibly switched on site. To stabilize daily gas deliverability throughout the early, middle and late production stages, a bottomhole pressure drawdown rate of 0.05 MPa/d or a fixed daily gas rate of 4000 m³/d is recommended. This work provides theoretical support for optimizing commingled development of superimposed tight gas and deep CBM reservoirs. Full article

Efficient blasting design is increasingly regarded not only as a geomechanical problem but also as a managerial challenge related to energy use, fragmentation quality, downstream comminution costs, and environmental performance. This study develops a shock-adiabat-based analytical model for predicting the radius of the crushing zone around borehole explosive charges and demonstrates its applicability as a decision support tool for energy- and cost-oriented blasting management. The model integrates shock wave propagation parameters, particle velocity behind the shock front, and the physical and mechanical properties of limestone, sandstone, and granite. The calculated crushing zone radiation was compared with a previously developed analytical model based on borehole pressure and validated using finite element simulations in SolidWorks Simulation. The discrepancy between the proposed shock adiabat model and the reference analytical solution did not exceed 6%, while the difference between analytical estimates and numerical simulations remained below 5%. The results show that borehole diameter, compressive strength, and explosive–rock interface pressure significantly affect the crushing zone radius and, consequently, the volume of rock effectively fragmented during blasting. A scenario-based assessment further indicates that improved prediction and management of the crushing zone may reduce downstream crushing and grinding energy demand by approximately 10–20%, generating potential cost savings and indirect CO₂ emission reductions. The proposed method therefore supports the management of blasting energy efficiency, fragmentation quality, operational costs, and sustainability performance in mineral extraction systems. Full article

Ultra-deep Ordovician carbonates in the Tarim Basin are a major target for oil and gas exploration in China. Localized overpressure, however, creates substantial well-control risks and impairs drilling safety and exploration performance. This study investigates the Fudong Block of the Fuman Oilfield using drilling, seismic, and well-test data. We develop a high-resolution, layer-specific formation-pressure prediction workflow that integrates well and seismic data through a stress–fracture-pressure framework. The workflow combines geomechanical modeling, prediction of the in situ stress field and fracture distribution, stress-fracture matching, Biot-theory-based pressure prediction, and iterative calibration against drilling observations. The results show that: (1) overpressure is concentrated near secondary faults, branch faults, and NW-trending faults. It is jointly controlled by tectonic compression, pressure retention within fracture–vug bodies, and fluid charging. Multiple vertically separated pressure systems are common, and their marked heterogeneity is closely related to secondary-fault development and fracture–vug connectivity; (2) drilling disturbance can generate apparent overpressure and lead to erroneous pressure interpretation. Overpressured wells commonly exhibit a kick followed by lost circulation or simultaneous kick and loss. Drilling-fluid invasion into confined fracture–vug bodies causes pressure buildup; and (3) formation pressure is a key parameter in integrated geological and engineering sweet-spot evaluation and is closely linked to wellbore stability. Field applications confirm the accuracy of the proposed workflow. The method strengthens integrated geology-engineering evaluation and provides a practical basis for the safe and efficient development of ultra-deep carbonate reservoirs. Full article

The collapse pressure equivalent density is a key parameter for determining the safe drilling fluid density window and evaluating wellbore stability. To address the limited number of drilled wells, the lack of continuous geomechanical labels, and the complex relationship between seismic responses and collapse pressure, this study proposes a collapse pressure prediction method based on virtual-well constraints and deep sequence learning. Virtual-well samples were constructed from the acoustic impedance distributions of drilled wells, synthetic seismic records were generated through actual seismic wavelet extraction and reflection-coefficient convolution, and collapse-pressure equivalent-density labels were calculated using a rock-mechanics model, thereby forming a large-scale training dataset. Random Forest, Polynomial Regression, Support Vector Regression, and CNN-MultiLSTM models were compared. The CNN-MultiLSTM framework was further optimized in terms of input range, feature channels, and sequence structure, resulting in a whole-well sequence model combining multichannel inputs with residual dilated convolutions. The optimized model achieved an MAE of 0.00262 g/cm³, an RMSE of 0.00352 g/cm³, a MAPE of 0.241%, and an R² of 0.9774 on the validation set. In independent validation using an actual well not involved in training, the model achieved an MAE of 0.0182 g/cm³, an RMSE of 0.0278 g/cm³, a MAPE of 1.640%, and an R² of 0.7356 within the primary target interval of 7900.000–7994.100 m. The predicted profile reproduced the main depth-dependent variation of the calculated collapse-pressure equivalent density, although larger deviations occurred in locally abrupt intervals. Analysis outside the primary target interval further showed that the model could respond to high-collapse-pressure anomalies. Overall, integrating virtual-well constraints, rock-mechanics-based labeling, and whole-well sequence learning provides a feasible approach for collapse-pressure prediction in undrilled areas and drilling fluid density design, while further multi-well validation is required to assess cross-well generalization. Full article

The prediction of shear strength in seasonally frozen silty clay is restricted by complex physical mechanisms and sparse experimental data. A self-supervised contrastive physics-informed neural network is proposed to overcome these limitations. Robust latent features are extracted from limited datasets via contrastive pretraining. Time-dependent constitutive equations and physical boundary conditions are simultaneously embedded into the loss function. This mathematical constraint ensures strict physical consistency during the modeling process. The proposed framework was validated using 100 independent laboratory samples prepared under controlled moisture content, freezing temperature, and thawing duration. The experimental results demonstrate the superior predictive accuracy of the proposed model. A coefficient of determination ($R^2$) of 0.988 was achieved on the test set, accompanied by minimized error metrics compared to conventional data-driven approaches. Consequently, a highly accurate and reliable methodology is established by this architecture for evaluating soil stability and supporting infrastructure design in cold regions. Full article

Rockfall hazards are frequently observed within mountainous road networks. Significant uncertainties regarding the optimal selection of evaluation units and spatial modeling scales are still being identified in this field. A comprehensive comparative framework for rockfall susceptibility mapping is presented in this study, using Wufeng County as the empirical study area. Five evaluation scenarios were constructed to systematically isolate the independent predictive contributions of the spatial domain, the mapping unit morphology, and the physics-informed engineering proxy. These scenarios included a whole-county macro-scale raster; three multi-scale road buffers with widths of 1 km, 2 km, and 3 km; and an object-oriented vector road evaluation unit (REU) framework. To parameterize localized engineering-induced risks, a physics-informed feature defined as the theoretical slope-cutting height ($H_{\mathrm{cut}}$) was structurally introduced into the vector-based assessment. Thirteen representative machine learning, deep learning, and statistical algorithms—including Random Forest, LightGBM, and TabNet—were systematically cross-examined under both unconstrained splits and strict Leave-One-Road-Corridor-Out Validation (LORCOV) protocols. The empirical multi-metric sensitivity analysis explicitly decouples the three structural effects. First, isolating the effect of the spatial domain reveals that restricting the validation extent from a broad countywide area to a narrow road corridor purges unperturbed background terrain noise, shifting the focus from easy negatives to geomorphological hard negatives. Second, evaluating the independent effect of the evaluation unit demonstrates that transitioning from continuous raster pixels to homogeneous vector REUs successfully resolves the terrain smoothing effect, precisely characterizing sharp geomechanical gradients adjacent to cut slopes. Third, isolating the effect of adding $H_{\mathrm{cut}}$ proves that this engineering indicator drives the primary descriptive gain, enabling tree-based ensembles to achieve a peak baseline AUC of 0.7763 and maintain a robust spatial validation AUC of 0.6129 under strict geographic block constraints, whereas legacy deep learning architectures exhibit an inductive bias mismatch on small-scale tabular records. Rather than asserting a single optimal paradigm, this coordinated feature–unit matching framework provides transport authorities with a highly calibrated, target-tiered decision matrix to optimize localized public works safety budgets and protect critical linear infrastructure assets. Full article

Traditional rock mass evaluation methods have three main limitations: (1) their application depends largely on specialist judgment; (2) their discrete classification approach, such as RMR (Rock Mass Rating) and SMR (Slope Mass Rating), leads to abrupt transitions between categories; and (3) the interaction among geomechancial parameters is limited; these aspects reduce their ability to represent slope behavior in a gradual manner. The main objective of this research was to develop a model capable of representing gradual transitions between geomechanical conditions and the interaction among parameters related to susceptibility to instability. The model uses a hierarchical modular framework based on the Mamdani fuzzy inference mechanism, allowing the incorporation of expert knowledge through linguistic rules. It is implemented in a graphical environment that allows users to directly use geomechanical parameters obtained through conventional characterization or from three-dimensional digital models derived from UAV (Unmanned Aerial Vehicle) photogrammetry. Model consistency was evaluated through a sensitivity analysis, which verified the model’s response coherence across variations in input parameters. The graphical evaluation tool was then applied to three real case studies with different geomechanical configurations, and the results were compared with those from traditional methods (RMR and SMR). The results showed differences between traditional and fuzzy approaches, as our proposal links recommendations to specific geomechanical conditions across different evaluation levels, identifying conditions for potential intervention measures. In addition, the model enables the zonification of instability susceptibility, facilitating its use in future risk analyses. Our model is intended for application under normal slope conditions, without accounting for extreme events or external dynamic loads, such as seismic activity, groundwater level variations, infiltration, or high-mountain conditions. Full article

Reservoir stimulation is essential in enhanced geothermal systems and unconventional reservoirs where low permeability, inadequate fracture connectivity, or near-wellbore damage restricts commercial injection or production. This review evaluates hydraulic fracturing, thermal stimulation, plasma-pulse stimulation, and selected dynamic stimulation technologies. It compares their physical mechanisms, fracture-network development, reservoir applications, permeability enhancement, operational maturity, deployment challenges, and future perspectives. Hydraulic fracturing remains the most mature method for reservoir-scale fracture creation, fracture conductivity, and reservoir connectivity. In enhanced geothermal systems, however, performance depends on the heat-exchange area, distributed flow, thermal sweep, long-term energy recovery, and induced-seismicity control rather than permeability enhancement alone. Thermal stimulation is integral to geothermal reservoir development. Cold-fluid injection generates thermoelastic stress redistribution, enlarges the fracture aperture, activates natural fractures, promotes thermally assisted fracture propagation, and influences thermal breakthrough. Plasma-pulse stimulation, also termed pulsed-power plasma, electrohydraulic, or shock-wave stimulation, provides a low-water method for near-wellbore permeability enhancement, damage bypass, fracture reactivation, and restimulation. Its broader deployment remains constrained by the limited treatment radius, scale-up uncertainty, energy-transfer efficiency, tool durability, completion integrity, and insufficient field validation. Liquid CO₂ phase-transition, propellant, and explosive stimulation provide additional dynamic-loading options with distinct fracture responses, controllability, safety, and technology readiness. Stimulation technologies should therefore be selected according to the dominant reservoir limitation and evaluated using sustained injectivity or productivity, effective reservoir contact, distributed flow, delayed thermal breakthrough, treatment durability, wellbore integrity, and a controlled geomechanical response. Future progress requires hybrid stimulation, coupled thermal–hydraulic–mechanical–chemical (THMC) modeling, integrated monitoring, adaptive control, physics-informed artificial intelligence, digital twins, standardized field validation, and techno-economic and life-cycle assessments. Full article

Geothermal energy extraction using existing wellbore systems provides a promising approach for sustainable heat utilization; however, the long-term thermo-hydro-mechanical (THM) responses associated with different working fluids remain insufficiently understood. In this study, a three-dimensional coupled THM model was developed to compare geothermal heat extraction using water and pressurized CO₂ under identical geological and operational conditions. The model integrates Darcy flow, heat transfer, and linear elastic deformation to investigate the evolution of hydraulic, thermal, and mechanical fields over a 100-year operation period. The results show that the hydraulic fields rapidly reach quasi-steady states, whereas thermal responses continuously evolve due to cold-front propagation from the injection well. Compared with water, pressurized CO₂ exhibits stronger fluid mobility and produces a larger thermal influence region, resulting in different heat extraction characteristics under the same mass-flow-rate condition. Thermal cooling induces reservoir contraction and stress redistribution; however, the calculated stress and displacement variations remain within a stable range throughout the simulation period. The comparison demonstrates that pressurized CO₂ can enhance long-term thermal utilization while maintaining acceptable geomechanical stability under the investigated conditions. These findings provide insights into the selection of working fluids for wellbore-based geothermal systems and highlight the importance of coupled THM evaluation for long-term reservoir performance assessment. Full article

Wellbore instability poses a significant challenge to the safe and long-term production of natural gas hydrates (NGHs). Characterizing the geometry of the wellbore-adjacent plastic zone is critical for evaluating geomechanical risks during hydrate exploitation. In this paper, an elastic–plastic analytical model incorporating the Mohr–Coulomb failure criterion is developed to describe the stress distribution around the borehole under non-uniform in situ stress conditions. Particular attention is paid to the role of hydrate saturation, which is integrated into the constitutive framework to reflect the cementation effect of NGH-bearing sediments (GHBS). Analytical solutions for the stress fields in both the elastic and plastic regions are derived, which are accompanied by a computational scheme for determining the plastic zone radius. Using site-specific mechanical parameters from the Shenhu area in the South China Sea, a parametric analysis is conducted to quantify the influences of hydrate saturation, reservoir depressurization, and stress anisotropy on the evolution of the plastic zone shape. The results indicate that elevated hydrate saturation enhances the load-bearing capacity of the formation, whereas depressurization significantly expands the plastic region, leading to severe wellbore instability. These findings provide theoretical insights for optimizing drilling strategies in deep-water hydrate reservoirs. Full article

Underground gas storage converted from depleted oil and gas reservoirs requires reliable long-term sealing of caprocks, faults, and other geological barriers during cyclic injection and withdrawal. Conventional evaluations mainly focus on static geological parameters, whereas the effects of stress evolution during operation are often insufficiently addressed. In contrast, four-dimensional geomechanical simulations based on fluid–solid coupling can capture the dynamic evolution of geological sealing behavior under injection and withdrawal conditions. The review summarizes progress in heterogeneous geomechanical model construction, stress-field evolution under cyclic loading, dynamic sealing assessment of caprocks and faults, and determination of safe operating pressure limits. Geological sealing evaluation has evolved from a static assessment based on geological characteristics to a dynamic assessment controlled by mechanical criteria. Geostress inversion has developed from three-dimensional heterogeneous mechanical models to four-dimensional geomechanical dynamic coupling analyses that account for seepage, stress, temperature, and other factors. Safe pressure evaluation has also progressed from conventional gravity-driven storage construction to the assessment of critical pressure evolution during the safe operation stage. Existing studies indicate that heterogeneous parameter characterization, coupled flow-stress simulation, and dynamic pressure management strongly affect the reliability of sealing evaluation in reservoir-type UGS. The results further show that pressure history, stress redistribution, and creep effects should be considered together when assessing long-term storage safety. The engineering cases listed at the end of this paper verify some of the research findings. The results presented above are of great significance for the construction and safe operation of reservoir-type gas storage facilities. Full article