Search Results (791)

Shale gas exploration in the Shixi block, Guizhou, faces significant challenges due to complex geological structures and normal pressure. To reduce exploration risk, we propose an integrated “Four-in-One” evaluation workflow that combines geological sweet spots, engineering feasibility, preservation conditions, and paleogeomorphology. The workflow features a ‘cap-constraint’ velocity model to reduce structural uncertainty and a tiered multi-scale discontinuity detection strategy for low-SNR seismic data. Application of this workflow in the Shixi block delineated two Class I favorable zones (42.61 km²) with estimated resources of 8.33 billion cubic meters. Drilling results from 56 horizontal wells validate the accuracy of our prediction model, confirming that preservation condition is the primary controlling factor for gas accumulation in this normally pressured setting. This study provides a practical reference for shale gas assessment in structurally complex, normally pressured regions. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

The stability of underground building foundations in fractured rock masses is a critical concern in geotechnical engineering, particularly for urban projects situated in complex geological settings. In such environments, the interaction between weak planes, groundwater seepage, and in situ stress plays a decisive role in controlling deformation and failure mechanisms. This study presents a novel weak plane–seepage–stress coupling model specifically developed to evaluate the stability of underground excavations and foundation walls under these challenging conditions. Unlike conventional approaches that often assume isotropy or consider isolated factors, the proposed model integrates multiple interacting variables—including weak plane orientation, seepage coefficient, and excavation direction—to systematically assess their combined influence on stress redistribution and failure pressure. A key innovation lies in the quantitative evaluation of the permeability-sealing coefficient, which reflects the effectiveness of waterproofing measures, and its coupling with weak plane characteristics. The results demonstrate that weak planes significantly alter the surrounding stress field, inducing directional instability. The optimal excavation orientation for minimizing instability is identified within the range of 200° to 280°. Moreover, increasing δ from 0 to 1 leads to a substantial reduction in the required supporting pressure, underscoring the critical role of effective sealing and waterproofing in enhancing foundation stability. While the current model is based on a single weak plane assumption and focuses on short-term mechanical responses, it provides a foundational framework for understanding coupled instability mechanisms. Future work will extend the model to incorporate multi-set weak planes, time-dependent degradation, and dynamic excavation processes. This research offers both theoretical insights and practical guidance for optimizing geotechnical design in fractured rock environments, contributing to more resilient and sustainable underground construction. Full article

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration. Full article

Although trenchless technology is widely recognized for its low-carbon potential, existing assessment models often overlook the significant impact of regional geological variations on energy consumption. Based on the EN 15804 standard and the Input–Process–Output (IPO) model, this study establishes a high-resolution carbon emission assessment framework focusing on the “Upfront Carbon” stages (Modules A1–A5) of public works. An empirical study was conducted on a sewage microtunneling project in Hualien, Taiwan, characterized by a deep burial depth of 12 m and challenging gravel formations (SPT N-value > 50). Life Cycle Assessment (LCA) principles were adopted to quantify the carbon footprint and benchmark the results against international guidelines from the UK (PJA) and Japan (JSWA). The Life Cycle Inventory (LCI) reveals a unit emission intensity of 349 kgCO₂e/m, significantly higher than international benchmarks. Critical findings indicate that this discrepancy is primarily driven by environmental variables—specifically, geological resistance and grid emission factors. Crucially, the sensitivity analysis demonstrates that the physical resistance of the hard gravel layer increased machinery energy intensity by 18.7% compared to baseline soil conditions. This study officially defines this phenomenon as the “Geological Premium.” Additionally, carbon efficiency was found to be profoundly influenced by the regional grid emission factor (Taiwan: 0.495 vs. UK: 0.193 kgCO₂/kWh). This research establishes a localized empirical database and validates the necessity of expanding assessment boundaries to include auxiliary works in geologically complex regions. The developed framework provides a scalable solution for optimizing embodied carbon in urban infrastructure, offering policymakers a robust scientific basis for implementing precise “Green Public Procurement” and carbon budgeting strategies. Full article

Unlined pressure tunnels and shafts are widely employed in hydropower projects where the surrounding rock mass is required to sustain the internal water pressure. Their hydraulic stability is governed by complex interactions among the three-dimensional in situ stress state, discontinuity geometry, rock mass properties, and operational water pressure. Conventional deterministic design approaches address these factors implicitly and provide limited information on the likelihood of hydraulic failure mechanisms, such as hydraulic jacking, hydraulic fracturing, and shear slip of discontinuities. This paper presents a probabilistic framework for assessing the hydraulic stability of unlined pressure tunnels and shafts, in which the governing failure mechanisms are explicitly formulated as limit states and key sources of uncertainty are systematically represented. The full three-dimensional stress tensor is rotated onto potential discontinuity planes to evaluate effective normal and shear stresses, and reliability-based methods are employed to quantify probabilities of failure. The methodology is demonstrated through a representative case study of a failed unlined pressure tunnel reflecting typical geological and stress conditions encountered in hydropower projects. The results show that variability in stress orientation and discontinuity characteristics has a strong influence on hydraulic stability and that commonly used deterministic criteria may not fully capture the associated failure risk. Full article

The anomalous logging responses caused by complex geological and downhole engineering conditions, which can be the expansion of a borehole, the formation of fractures, and the mud intrusion, usually result in the absence of some important curves and undermine the accuracy of the reservoir evaluation. The strong nonlinearity and non-stationarity of the log curves remain problematic to conventional interpolation and statistical techniques; the traditional models do not take into account any sequential relationship between points along the depth axis, whereas the deep sequence models can only regress on the points, which limits their capability of ensuring the overall geological consistency. In order to resolve these difficulties, this paper introduces a Geology-Balanced Time Series Conditional Generative Adversarial Network (GC-TSGAN) in which the lithological data is converted into an initial state in the form of prior conditions and is input into both the generator and the discriminator. The model uses LSTM to learn depth-sequential dependencies and a BCE GAN-based adversarial loss to achieve distributional consistency and local morphological fidelity. Hyperparameter tuning is used with the help of random search and Bayesian optimization. The logging data of 41 wells in the B Basin, Chad, are experimented using GC-TSGAN alongside baseline models such as RF, XGBoost, LSTM and ANN; GC-TSGAN is proven to be much better than baseline models in terms of the RMSE, MAE, and squares of predicate and value. The findings confirm that the proposed model can effectively reconstruct log curves with high precision even in a complicated geological environment, thereby providing quality data for performing geological modeling and evaluating the reservoirs. Full article

To improve the blasting performance of tunnel wedge cutting while mitigating vibration effects, this study proposes a precise delayed blasting method and evaluates its effectiveness through a three-dimensional numerical simulation, similarity model test, and field application. The proposed method divides the cut holes into initial and secondary groups and uses electronic detonators to control the delay time. The numerical results show that delayed blasting reduces the peak stress in the surrounding rock, accelerates stress-wave attenuation, improves cavity integrity, and lowers the peak particle velocity (PPV), while maintaining sufficient rock breaking capacity. Model tests conducted under different delay times indicate that the delayed scheme increases the pull efficiency, decreases the ratio of large fragments, and reduces the PPV, with an optimal delay time range of 4~8 ms for moderately weathered limestone. Field tests in the Da Balai Tunnel further verify the effectiveness of the proposed method. Compared with conventional blasting, delayed blasting increases the pull efficiency from 77.8% to 97.3%, reduces the large fragment ratio from 30.6% to 11.4%, decreases the PPV by 52.5%, and increases the dominant vibration frequency by 48.7%. These results demonstrate that the proposed method can simultaneously enhance the rock-breaking quality and vibration control, providing practical guidance for tunnel blasting excavation under complex geological conditions. Full article

The construction of suspension bridges in mountainous expressways often involves tunnel-type anchorages in close proximity to shallow-buried bifurcated tunnels, particularly in soft rock strata with dense overlying structures. This proximity poses significant challenges to construction safety and stability. This study aims to investigate the influence of tunnel-type anchorage construction on the ground surface, surrounding rock, and adjacent bifurcated tunnels under such complex conditions. It was hypothesized that the anchorage load transfer and deformation mechanisms would significantly affect the adjacent tunnel, with potential cumulative effects due to the twin-anchor configuration. To address this, a combined approach of in situ scaled model testing (1:10 scale) and three-dimensional numerical simulation was employed. The model test incorporated monitoring of deformation and stress at key locations (anchor plug, rock mass, and anchor–rock interface) under incremental cable loads. Quantitative results from the model test indicate that at the design load (1P, equivalent to 2016.84 kN per anchor), deformations were minimal (e.g., maximum anchor displacement 0.35 mm). The anchor–rock interface exhibited limited slip (max 0.06 mm at 1P), and contact stresses were highest in the rear part of the anchor plug, indicating a non-uniform load transfer. Under overload conditions, the system reached yield at 7P and peak strength at 10.5P, with measured ground surface cracks up to 5 mm. Numerical simulations, calibrated against the experimental data, revealed that under increasing load (up to 10P), the plastic zones around the two anchors progressively expanded and eventually coalesced, leading to a characteristic “inverted trapezoid” failure pattern propagating to the surface, accompanied by shear failure along the 14° bedding plane. The combined results quantify the progressive interaction between the twin anchorages and the surrounding rock, highlighting the critical role of the anchor–rock interface and the cumulative effect of twin anchors on ground deformation and potential failure mechanisms. This research provides a scientific basis for the design and construction of tunnel-type anchorages in similar challenging geological and spatial settings. Full article

The coupling between the key parameters of rock and soil particle composition and slurry diffusion paths exerts a significant influence on actual grouting effectiveness. Based on the spherical penetration grouting model for Bingham cement grout, this study optimizes the fractal permeability model by coupling the characteristic particle size, porosity, and tortuosity, overcoming the deficiency of single-factor porosity consideration in existing permeability models. Unlike existing studies that only use experimentally measured permeability coefficients, this study employs a physically meaningful permeability model that realizes the synergistic coupling of soil particle composition, pore microstructure, and macroscopic permeability, and further establishes a penetration grouting mechanism that integrates the actual slurry diffusion path tortuosity into the classical spherical diffusion framework. A novel high-precision volume measurement method for grouting stone bodies based on point cloud 3D reconstruction is proposed, and a COMSOL-based visual numerical simulation program is developed by embedding the above coupling permeability model. The accuracy of the optimized mechanism is verified by a combination of model tests, numerical simulations, and theoretical analysis, which makes up for the existing grouting mechanism for loose gravelly soil failing to consider the synergistic influence of rock–soil particle composition parameters and the actual diffusion path. The research results indicate the following: (1) Adopting loose gravelly soil—which is more consistent with actual field conditions—as the grouted medium can effectively predict the reinforcement effect of heterogeneous media in grouting engineering. (2) Compared with theoretical values calculated by mechanisms that ignore the effect of the diffusion paths, those derived from the grouting mechanism that couples the rock and soil characteristic particle size with the Bingham cement grout diffusion path are closer to the experimental values. (3) The visual simulation results exhibit high morphological consistency with the actual grouting stone bodies, and the vast majority of the grout diffusion range falls within the numerical simulation domain. The findings of this study provide targeted theoretical and technical guidance for grouting design under complex geological conditions of loose gravelly soil layers. Full article

Marine electric-source time domain electromagnetic (TDEM) surveys typically involve the simultaneous movement of transmitters and receivers, which generates a large number of transmitter–receiver pairs. This acquisition geometry creates notable challenges for 3D inversion, mainly because of the large data volume and high computational cost. However, the electromagnetic “sensitive region” for each transmitter–receiver pair is much smaller than the full survey area. Based on this feature, we propose an efficient 3D inversion approach using the footprint technique. By clearly defining the sensitivity region, referred to as the footprint domain, for each pair, the method builds the sensitivity matrix only within localized subsurface regions that significantly affect the observed response. This approach greatly reduces both forward modeling cost and memory requirements. The forward modeling adopts an integral equation method combined with cosine transforms for fast 3D field computation, while the inversion framework uses a regularized conjugate-gradient algorithm, further accelerated by parallel computing under footprint domain constraints. Numerical simulations also examine the effects of offset, time channel, seawater thickness, and resistivity on the footprint domain, helping clarify the spatiotemporal diffusion behavior of TDEM fields in shallow marine environments. Tests on representative models show that the proposed method remains stable and accurate under complex geological conditions while significantly improving computational efficiency. In particular, the footprint domain technique improves inversion speed by about 55% compared with full domain inversion. These results indicate that the proposed approach provides a reliable and scalable option for large-scale 3D inversion of marine TDEM data. Full article

Deep shale gas reservoirs in the southern Sichuan Basin (Weiyuan area) exhibit strong heterogeneity and complex pore-fracture networks. Traditional reservoir evaluation methods struggle to accurately capture their microscale pore characteristics and fracability, thereby restricting efficient development and precise sweet spot prediction. Therefore, integrating digital core technology with geological analysis is essential to systematically quantify key reservoir parameters, including microscale pore structure, mineral composition, and brittleness characteristics. To clarify the controlling factors of high-quality deep shale gas reservoirs in the Weiyuan area and assess their exploration and development potential, we performed digital core analysis at micron to nanometer scales. Three-dimensional digital core models of representative deep shale gas wells were constructed. Integrating mineral composition, geochemical characteristics, and pore space features, we discuss the geological conditions for deep shale gas accumulation and the fracability of horizontal wells, and we delineate favorable shale reservoir zones. The results show that digital core technology enables quantitative and visual characterization of each sublayer of the Longmaxi Formation shale reservoir, including mineral types, laminae types, pore-throat structures, and organic matter distribution. From the Long 11-1 sublayer to the Long 11-4 sublayer, the pore-throat radius, total pore volume, total throat volume, connected pore-throat percentage, and coordination number all gradually decrease. In the eastern Weiyuan area, the siliceous components in deep shale gas reservoirs at the base of the Longmaxi Formation are primarily of both biogenic and terrigenous origin. Due to local variations in the sedimentary environment, terrigenous input contributes significantly to the total siliceous content in this region. Although the Long 11-1 sublayer of the Longmaxi Formation is lithologically classified as mud shale, its particle size and mineral composition more closely resemble those of clayey siltstone or argillaceous sandstone, suggesting considerable potential for reservoir space development. Typical wells in the eastern Weiyuan area exhibit distinct lithological characteristics, including coarser grain sizes, stronger hydrodynamic conditions during deposition, and abundant terrigenous clastic supply. The rigid framework formed by silt- to sand-sized particles effectively mitigates compaction, thereby facilitating the preservation of intergranular pores and microfractures. High organic matter abundance, appropriate thermal maturity, and a considerable thickness of high-quality shale ensured sufficient hydrocarbon supply. The main types of natural fractures are intergranular and grain-edge fractures formed by differences in sedimentary grain size, and bedding-parallel fractures generated by hydrocarbon generation overpressure. Based on reservoir mineral composition, pore characteristics, areal porosity, and pore size distribution identified via digital core analysis, the bottom 0–3 m of the Long 11-1 sublayer is determined to be the optimal target interval. By delineating the microscopic characteristics of the shale reservoir and predicting rock mechanical parameters, a fracability evaluation index was established from digital core simulations. This guides the selection of target layers in deep shale gas reservoirs and optimizes hydraulic fracturing design. Full article

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

To address the challenges of low accuracy and complex influencing factors in predicting horizontal well fracturing productivity during the development of unconventional oil and gas resources such as tight oil, this paper proposes a productivity prediction framework based on an improved feature selection method and an ensemble learning model. This study employs a fusion analysis using the entropy weight method to combine Pearson correlation analysis and improved gray relational analysis (IGRA) for feature selection. Thirteen machine learning models were tested with six distinct parameter combinations to construct a Stacking-based ensemble learning model, with base models including Random Forest (RF), Ridge Regression (RR), and Artificial Neural Network (ANN). Particle Swarm Optimization (PSO) was employed to optimize hyperparameters, followed by interpretability analysis using SHapley Additive exPlanations (SHAP). The results indicate that the model with fused weights demonstrated optimal performance. The Stacking model achieved significantly improved accuracy after PSO optimization, with the coefficient of determination increasing by 4.9%, outperforming all comparison models. Engineering guidance is provided: Under current geological conditions, sand ratio and displacement fluid volume require fine-tuning to prevent over-treatment. Fracturing design should implement differentiated strategies based on the target sand body thickness. This study not only delivers a high-precision production prediction tool but also offers decision support for efficient unconventional oil and gas field development through its exceptional interpretability. Full article