Search Results (74)

To address core challenges involving severe reservoir heterogeneity, complex fracture systems, and rapid energy depletion encountered in the development of tight oil reservoirs in the G5 block of the Nanpu Sag, this study performs a systematic analysis of geological characteristics and optimizes an integrated geology–engineering development strategy. Through the integration of 3D seismic and well-logging data, the “sandwich-style” superposition architecture of sand bodies in the Es₃⁴ sub-member is quantitatively characterized. It reveals that productivity is co-controlled by high-quality main channel sand bodies (permeability: 0.5–1 mD) and high-density fracture zones (linear density: 3.2 fractures·m⁻¹) along structural ridges. Consequently, a comprehensive technical system is established, incorporating trajectory optimization for high-angle wells, differential stimulated reservoir volume (SRV) fracturing based on the Reservoir Quality Index (RQI), and CO₂ huff-n-puff for energy supplementation. Field applications demonstrate that optimized well placement increased the drilling encounter rate of high-quality reservoirs from 42% to 78%, while CO₂ huff-n-puff technology successfully restored the formation pressure coefficient from 0.65 to 0.82. The implementation of this integrated approach extended the stable production period of typical wells to 18 months, significantly mitigating production decline and increasing the ultimate recovery factor of the block to 14.5%, which provides a favorable recovery level for a complex fault-block tight oil reservoir compared with the generally low primary-recovery performance reported for analogous tight oil systems in rift-basin settings. This study confirms that the coupling zone of fracture systems along structural ridges and high-quality sand bodies represents the optimal target for economic development. The proposed geology–engineering synergy model provides a transferable technical paradigm for the efficient development of similar complex fault-block tight oil reservoirs. Full article

Ultra-deep carbonate reservoirs are increasingly critical to the global energy supply, representing a major frontier in hydrocarbon exploration. While these reservoirs are predominantly controlled by strike-slip faults, hydrocarbon enrichment exhibits considerable spatial variability along these faults, resulting in persistently high exploration risks in the Tarim Basin, China. This paper proposes a quantitative evaluation framework integrating source connectivity, transport capacity, and reservoir quality of strike-slip faults. This multi-parameter quantitative evaluation of the main controlling factors aims to provide a geological basis and an objective reference for hydrocarbon exploration in ultra-deep carbonate reservoirs within the Shunbei area. Utilizing high-precision 3D seismic data and drilling data from 15 exploration wells along the F4 strike-slip fault in the Shunbei area, we identified five distinct kinematic segment types of the strike-slip fault. Subsequently, a comprehensive characterization of source connectivity, transport capacity, and reservoir quality was achieved based on a series of geological parameters, including stratal deformation intensity, gypsum–salt layer thickness, the average value of gradient structure tensor attributes, and the cross-sectional area of fracture–cavity bodies. Principal component analysis was then employed to integrate these geological parameters into a hydrocarbon enrichment index F, quantifying the synergistic coupling effects of multiple geological factors. The results demonstrate a good positive correlation (R² = 0.78) between the F index and the normalized daily oil equivalent production of each well. To assess predictive performance, a randomized cross-validation with 10 independent trials was conducted. The blind test sets yielded an average predictive coefficient of determination (Q²) of 0.76 and a mean relative error (MRE) of 9.65%, indicating stable predictive performance without major deviations. The spatial configuration of the fundamental parameters for source connectivity, transport capacity, and reservoir quality ultimately determines the enrichment degree of ultra-deep carbonate reservoirs, which is specifically manifested as differential hydrocarbon enrichment models associated with distinct kinematic segment types. Specifically, the high-enrichment model correlates primarily with offset and flexural pull-apart segments; the medium-enrichment model is associated with the flexural pull-apart, transpressional uplift, and weakly transpressive strike-slip segments; whereas the low-enrichment model is confined to the weakly transpressive strike-slip and pure strike-slip segments. This study elucidates fault-controlled hydrocarbon accumulation mechanisms within ultra-deep carbonate reservoirs, providing novel insights for the predictive exploration and quantitative evaluation of ultra-deep energy resources in the Shunbei area. Full article

Transmission tower-line systems spanning active faults are simultaneously subjected to the “dual characteristic seismic actions” of permanent ground displacement (PGD) and spatially varying near-fault ground motions, rendering their failure mechanisms far more complex than those under conventional site-specific seismic actions. This paper investigates a 500 kV double-circuit “two-tower, three-line” coupled system by establishing a high-fidelity finite element model. An analytical framework is proposed, centered on indexing seismic action and structural response by key parameters: “Permanent Ground Displacement–Peak Differential Displacement–Velocity Pulse Period” (“PGD–Δmax–Tp”). By employing synthesized ground motions, the displacement time history is decomposed into three components—a velocity pulse, high-frequency background noise, and permanent displacement—thereby achieving a strict decoupling of these three control variables. Based on this methodology, three sets of controlled-variable scenarios were constructed to systematically reveal the independent influence of ground motion spectral characteristics, permanent displacement, and peak differential displacement on the system’s response. The research indicates that: spectral characteristics modulate the failure mode (the whiplash effect is triggered when the period ratio μ is approximately 1–2, whereas tower leg buckling occurs when μ ≫ 1); a threshold PGD value exists that triggers a shift in the structural force-resisting mechanism; and the peak differential displacement (Δmax) causes the system’s response to transition from being dominated by conductor slackening and unloading to being governed by inertia and P-Δ effects. The insights gained into the asymmetric response characteristics of towers on opposite sides of the fault provide a quantitative reference for the revision of seismic design codes for cross-fault power transmission projects. Full article

To address the challenges in intuitive feature discrimination and precise quantitative evaluation of cable defects, this paper proposes a diagnostic methodology utilizing the Symmetrized Dot Pattern (SDP) transform and reflection coefficient spectra. The Dung Beetle Optimizer (DBO) is introduced to adaptively optimize the SDP transform parameters, employing the Structural Similarity Index Measure (SSIM) as a fitness function to maximize discriminability between deterioration states. Three quantitative features, including the number of effective pixels, the degree of red–blue aliasing, and radial dispersion, are extracted to characterize the physical degradation processes of signal energy accumulation, angular evolution, and path divergence. By incorporating a self-reference calibration mechanism for structural differences, features are fused into a Comprehensive Deterioration Index (CDI). Experimental results on coaxial cables simulating shielding damage and thermal aging demonstrate that SDP images reveal continuous evolution patterns corresponding to defect severity. A regression model based on these patterns effectively characterizes deterioration trends. Compared to complex models, this study achieves intuitive fault identification and preliminary quantitative description of degradation trends through image feature fusion. Although the current sample size is limited, the results validate the feasibility of this method in evaluating cable deterioration severity, offering an efficient new data-processing perspective for cable condition monitoring. Full article

To address the challenge of surrounding rock instability in deep-buried tunnels crossing fractured fault zones, this study focuses on the Xigu Tunnel of the Lanzhou–Hezuo Railway. A combination of laboratory triaxial tests, an optimized multi-source advanced geological prediction workflow, and a site-specific parameter-weakened Mohr–Coulomb numerical simulation is employed to systematically reveal the physical–mechanical properties, spatial distribution, and deformation response of fractured rock masses under excavation-induced disturbance. The triaxial test results show that the average peak strength of the surrounding rock reaches 149.04 MPa; however, significant variability is observed among samples, and the failure mode exhibits a typical brittle–shear composite feature. The measured cohesion and internal friction angle are 20.57 MPa and 49.91°, respectively, indicating high intrinsic strength of individual rock blocks. Nevertheless, due to the presence of densely developed joints and crushed structures, the overall mass is loose and highly sensitive to dynamic disturbances such as blasting and excavation, revealing a unique mechanical paradox of high-strength rock blocks with low overall rock mass stability in deep-buried fractured zones. Joint TSP (Tunnel Seismic Prediction Ahead) and ground-penetrating radar (GPR) prediction reveals decreased P-wave velocity, increased Poisson’s ratio, and intensive seismic reflection interfaces; a quantitative index system for identifying the boundaries of narrow deep-buried fractured zones is proposed based on these geophysical characteristics. Combined with geological face mapping, these results confirm the existence of a highly fractured zone approximately 130 m in width, characterized by well-developed joints, heterogeneous mechanical properties, and localized risks of blockfall and groundwater ingress. The developed numerical model, with parameters weakened based on triaxial test and geological prediction data, effectively reproduces the deformation law of the fractured zone, and the simulation results agree well with field monitoring data, with peak displacement concentrated at section DK4 + 595, thus accurately identifying the center of the fractured belt as a key engineering validation result of the integrated technical framework. During construction, based on the identified spatial characteristics of the fractured zone and the proposed targeted support insight, enhanced dynamic monitoring and targeted support measures at the fractured zone center are required to ensure structural safety and long-term stability of the tunnel. This study develops an integrated engineering-oriented technical framework for deep-buried tunnels crossing narrow fractured zones, and provides novel mechanical insights and quantitative identification indices for such complex geological engineering scenarios. Full article

Generator faults are among the most expensive events in utility-scale wind turbines, and the remaining useful life (RUL) of a generator is strongly influenced by long-term thermal loading on windings and bearings. Although wind farms continuously log multi-point generator temperatures and operating variables via SCADA, these data are rarely converted into an actionable, quantitative RUL trajectory that can be used directly for maintenance planning. This study proposes a field-oriented RUL estimation framework that transforms multi-year SCADA records into degradation-focused indicators and converts them into a physically plausible, decision-ready RUL curve. First, SCADA data are cleaned and filtered by operating conditions, and temperature rises relative to ambient are extracted. Next, abnormal operation is detected and summarised using an abnormal operation index (AOI), and thermal severity indicators are aggregated into a health index (HI) that reflects both proximity to engineering limits and signal variability. The HI is then mapped to lifetime consumption to update an effective age relative to the generator’s designed lifetime, followed by smoothing and monotonicity enforcement to ensure a stable, non-increasing RUL trajectory. Field validation shows a highly smooth RUL profile (98.2%) and a near-linear long-term decreasing trend ($R^2=0.985$). The results demonstrate that SCADA temperature–operation data can support reliable online generator RUL prognostic monitoring without the need for additional sensors. Full article

Geochemical anomaly detection is a critical stage in mineral exploration, playing a key role in predicting potential mineral targets. Traditional methodologies often struggle to integrate the spatial structure of geochemical data with underlying geological constraints effectively. To address this limitation, we propose GeoCLA, a geochemical anomaly detection framework that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM). This integrated spatial-attention architecture captures complex correlations among multiple features to improve anomaly identification. The method constructs spatial sequential samples from geochemical data. The CNNs extract local spatial patterns, the BiLSTM models sequential dependencies, and the AM enhances the representation of critical features. Anomaly scores are computed using the reconstruction error between the model output and the original data. In addition, a fault-distance weighting factor is incorporated to build a comprehensive anomaly evaluation index. The proposed model was applied to the Hatu gold district in Xinjiang, China. Both visual analysis and quantitative evaluation demonstrate effectiveness, achieving a ROC-AUC of 0.86 and a mineral occurrence coverage rate of 97% within moderate-to-high anomaly prospective areas, significantly outperforming baseline methods. Full article

This study addresses the problem of robust actuator fault estimation for a class of critical linear continuous-time systems subject to state time-varying delays, external disturbances, and actuator faults. A learning observer is proposed to achieve the challenging task of simultaneously estimating both the system states and actuator faults, irrespective of whether the faults are constant or time-varying. A key theoretical contribution is the derivation of a less conservative delay-dependent condition for the existence of the proposed learning observer, which is expressed in terms of linear matrix inequalities (LMIs). The $H_{\infty }$ performance index is employed to attenuate the effects of disturbances to a prescribed level. The efficacy of the proposed strategy is rigorously validated through three illustrative examples, including quantitative performance metrics and a comparative analysis with existing methods. Full article

To address the periodic impacts and amplitude-modulated high-frequency resonance phenomena caused by bearing faults in rotating machinery, this paper proposes a detection method. The core innovation lies in: firstly, constructing a composite fault feature index (CFFI) that integrates normalized kurtosis and fuzzy entropy, which synchronously quantifies the fault impact intensity and periodic structure, and serves as an optimization objective; secondly, definining a spectral energy retention rate (SERR) that includes both the full spectrum and characteristic frequency bands to evaluate the denoising effect and fault feature retention, respectively. Based on this, the method adaptively determines the Variational Mode Decomposition (VMD) parameters through the Triangular Topology Aggregation Optimizer (TTAO), and uses Dynamic Weighted Center Clustering (DWCC) to screen key IMFs containing fault-envelope information. On the IMS bearing dataset, the SERR of the reconstructed signal is 0.21356, which is higher than the actual collected signal value of 0.22465, with a relative error of 4.9%, indicating a higher reconstruction accuracy. These quantitative results indicate that CFFI-guided optimization enhances impulsive and periodic fault components while maintaining stable feature-band retention. This approach is suitable for real-world equipment monitoring and exhibits strong engineering applicability. Full article

Rock fracture monitoring is crucial for the stability of rock engineering. Based on the four-component borehole strain (FCBS) theory, this study analyzes the response characteristics of FCBS through numerical simulations of large-scale local rock fracture. Drawing on linear elastic mechanics theory and combined with the Gaussian white noise model, three strain response indices (areal strain index $p_j^{\mathrm{a}}$ and shear strain indices $p_j^{13}$, $p_j^{24}$) are proposed to quantitatively characterize rock fracture events. A criterion is defined that if any of these indices is greater than 1, the rock fracture event can be reflected, and the larger the index, the better the effect of this index in reflecting rock fracture. The effects of the installation angle of the four-component borehole strain gauge (FCBSG), the distance between the borehole and the fracture zone, and the orientation of the borehole on these three indices are systematically investigated. The results show that for the same borehole, the areal strain index remains constant for different installation angles of the FCBSG, while the two shear strain indices exhibit a complementary variation trend—one shear strain index is always greater than or equal to the characteristic value of the borehole shear strain index, and the other is less than or equal to it; the larger values of the areal strain index and shear strain index decrease with the increase in the distance between the borehole and the fracture zone, following the variation law of the function y = ax^b with a negative exponent; there are significant differences in the larger values of the areal strain index and shear strain index among different orientation of the borehole, while those in the same orientation of the borehole relative to the fault fractured zone show a certain degree of complementarity, and the combined use of shear strain indices and areal strain index can better reflect rock fracture events; within the range of orientation of the borehole β = 0° to β = 90°, the minimum range of rock fracture that can be reflected by the three strain response indices is 55 m, the maximum range is 65 m, and the average range is 60.7 m. Full article

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

The growing global reliance on solar photovoltaic (PV) systems requires robust, automated inspection techniques to ensure reliability and efficiency. Thermal infrared (IR) imaging is widely used for detecting PV faults; however, accurate classification remains challenging due to severe class imbalance, low thermal contrast, and high inter-class visual similarity among fault types. This study proposes a hierarchical deep learning framework for thermal PV fault classification, integrating a multi-class dataset-balancing strategy to enhance representational efficiency. The proposed framework consists of two major components: (i) a hierarchical two-stage classification scheme that mitigates data imbalance and leverages limited labeled data for improved fault discrimination; and (ii) a contrast-preserving MixUp augmentation technique designed explicitly for low-contrast thermal imagery, improving minority fault class recognition and overall robustness. Comprehensive experiments were conducted on benchmark 8-class thermal PV datasets using nine deep network architectures. Dataset refactoring decisions are validated through quantitative inter-class distance analysis using multiple complementary metrics. Results demonstrate that the proposed hierarchical SlantNet model achieves the best trade-off between accuracy and computational efficiency, achieving an F1-Efficiency Index of 337.6 and processing 42,072 images per second on a GPU, over twice the efficiency of conventional approaches. Comparatively, the Swin-T Transformer attained the highest classification accuracy of 89.48% and F1 score of 80.50%, while SlantNet achieved 86.15% accuracy and 73.03% F1 score with substantially higher inference speed, highlighting its real-time potential. Ablation studies on augmentation and regularization strategies confirm that the proposed techniques significantly improve minority class detection without compromising overall performance, with detailed per-class precision, recall, and F1 analysis. The proposed framework delivers a high-accuracy, low-latency, and edge-deployable solution for automated PV inspection, facilitating seamless integration into operational PV plants for real-time fault diagnosis. Full article

The Korean aquaculture sector relies heavily on foreign workers, who face elevated risks due to language barriers and limited safety training. This disparity necessitates data-driven safety interventions addressing specific worker vulnerabilities to ensure sustainable industry growth. This study quantitatively investigated accident characteristics and economic losses by nationality in Korean aquaculture by integrating 325 approved cases (2018–2022) from Industrial Accident Compensation Insurance (268 Korean; 57 foreign) and field survey data into the Formal Safety Assessment and Fault Tree Analysis frameworks recommended by the International Maritime Organization (IMO). The study revealed that entanglement during machinery operations accounted for 63.5% of the total cost among foreign workers. For Korean workers, slip and fall accidents were most frequent, while falls from height were the most severe in terms of unit cost and fatality. Based on the importance index and Human Element analysis, four risk control options were proposed: guarding and interlocks retrofit, multilingual training for foreign workers, and fall-protection upgrades and permit-to-work systems with lockout/tagout for Korean workers. Scenario analysis demonstrated consistent cost-saving effects. Both direct and indirect costs were incorporated into total loss estimation, with indirect costs calculated as 0.5–1.0 times the direct costs following the Ministry of Employment and Labor (2021). Full article

CO₂ Geological Storage Leakage (CGSL) poses significant risks to environmental safety and the sustainability of Carbon Capture, Utilization, and Storage (CCUS) projects. While Fault Tree Analysis (FTA) and the Risk Matrix are established risk assessment tools, their combined application to CGSL remains underexplored, particularly in providing a structured, semi-quantitative framework for risk prioritization. This study addresses this gap by developing an integrated FTA-Risk Matrix methodology specifically tailored for CGSL. Firstly, an Analytic Hierarchy Process (AHP) was employed to establish and optimize a comprehensive risk assessment index system, resulting in 17 key indicators derived from expert questionnaires. Subsequently, a fault tree model for CGSL was constructed, identifying 14 basic risk events. By integrating the risk matrix, these factors were quantitatively assessed based on their probability and severity, enabling clear risk classification and the identification of critical vulnerable points. The practical application of this framework to the Jingbian CCUS project in the Ordos Basin demonstrates its efficacy, revealing legacy wells and fault activation as high-risk factors. This research provides a systematic and transferable tool for enterprises to conduct hierarchical risk management and offers a critical reference for enhancing the safety protocols of CCUS projects. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article