Search Results (700)

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments. Full article

The continuous prediction and uncertainty quantification of unconfined compressive strength (UCS) and the fracture-related index of anisotropy (FRIA) are essential for optimizing drilling operations and hydraulic fracturing design in shale gas development. However, machine-learning-based log inversion often suffers from (1) spatial information leakage caused by autocorrelation in well logs, (2) implicit target contamination during multi-source data fusion, and (3) biased evaluation under random data splitting, which can overestimate apparent performance and underestimate extrapolation risk in deep heterogeneous intervals. To address these limitations, we propose a leakage-free, physics-constrained framework for predicting UCS and FRIA in the Weiyuan shale gas reservoir. Using 18,440 quality-controlled, depth-aligned samples, we adopt a contiguous depth-based split that preserves stratigraphic continuity while isolating training, validation, and test intervals to block spatial leakage. Under a strict leakage-free protocol, we evaluate single-task ensemble trees (STL-RF/HGB), a multi-task neural network (MTL-MLP), and a physics-informed variant (PINN-MLP) for deep-interval stabilization. The best model is target-dependent: STL-RF achieves R² = 0.984 for FRIA, whereas MTL-MLP attains R² = 0.874 for UCS. For deep formations (>4800 m), PINN-MLP with a depth-continuity constraint reduces deep-interval prediction error by 47.5%. Multi-seed experiments with 95% Student’s t confidence intervals further confirm robustness. Overall, the framework provides a reproducible workflow for continuous geomechanical-parameter prediction and risk-aware deployment in deep unconventional reservoirs. Full article

The anchoring effect of drill-anchor equipment directly determines the support quality of roadways. Currently, hydraulic drill-anchor robots suffer from insufficient positioning control precision during operation, and drilling position deviations induce roadway collapse risks and serious safety hazards. Therefore, effectively improving the position control accuracy of the drill arm of drill-anchor robots is a critical prerequisite for ensuring roadway support safety. Aiming at the drill-arm position control system of drill-anchor robots, this study establishes a friction model for friction compensation based on the analysis of the motion mechanism of drill-anchor robots and then constructs mathematical models for the slewing and pitching systems respectively. To realize the precise position control of the drill arm, an active disturbance rejection predictive control scheme is proposed. An extended state observer (ESO) is adopted to observe the system states and unmodeled disturbances, and the particle swarm optimization (PSO) algorithm with an improved objective function is applied to optimize the parameters of the drill-arm position controller. Finally, simulation results demonstrate that the designed active disturbance rejection predictive control method for drill-arm positioning, based on friction compensation and PSO tuning, exhibits excellent control performance and achieves accurate trajectory tracking of the drill-arm position of drill-anchor robots. This research has important theoretical and practical significance for promoting the automatic control of drill-anchor robots in underground engineering. Full article

To address rapid tool wear and unstable hole surface quality during roughing and semi-finishing operations using indexable shallow-hole drills, an optimization study on the spatial mounting posture of the insert is conducted, aiming to improve tool life and machined surface quality. Considering that tool life and surface quality are significantly influenced by cutting force and cutting temperature, radial cutting force and cutting temperature are selected as the multi-objective optimization criteria. A mapping model between the insert mounting posture parameters and cutting performance metrics is established. An improved LO-NSGA-II algorithm is employed to perform multi-objective optimization, yielding a Pareto-optimal solution set, and the entropy weighted-TOPSIS method is subsequently applied to determine the optimal insert mounting posture. Experimental results demonstrate that the optimized spatial mounting posture significantly enhances the overall cutting performance of the tool. Compared with the non-optimized tool, the optimized configuration exhibits a significant extension in tool life and a notable improvement in machined hole surface quality. This study provides an effective methodology for the structural optimization design of indexable shallow-hole drills. Full article

The transition from pure to mixed-species forest stands is increasingly promoted to enhance ecosystem stability and multifunctionality. The growth conditions may influence the physical and mechanical properties of wood. This study evaluated wood density in pure and mixed stands of silver birch, Norway spruce, and Scots pine in Lithuania and analyzed its relationships with tree allometric parameters. Nine study plots representing pure (100%) and mixed (70/30%) stands were established under comparable site conditions. Wood density at breast height was assessed using resistance drilling (IML Resi PD500), and the increment core samples were analyzed with the LIGNOSTATION™ system. The mean values of wood density for silver birch differed by 11%, depending on the wood density determination method used. Differences between pure and mixed stands were insignificant and generally did not exceed 6%–10%. No consistent trend that was attributable to species mixing was identified. The combined data from pure and mixed stands indicate that the mean wood density, converted from microdrilling measurements, was highest in silver birch (546 kg m⁻³ ± 1.87 kg m⁻³), followed by Scots pine (476 kg m⁻³ ± 1.85 kg m⁻³) and Norway spruce (437 kg m⁻³ ± 1.66 kg m⁻³). Resistance drilling showed a moderate relationship with the core samples’ wood density (R² = 0.59), supporting its suitability as a semi-nondestructive method. Diameter at breast height was the only tree parameter that was consistently significant across all predictive models. The combined model for all species explained up to 43% of wood density variation, while species-specific models had lower explanatory power. Overall, the results indicate that species mixing has a limited effect on wood density under the studied conditions and is unlikely to substantially alter wood quality in terms of wood density. Full article

This study presents an integrated workflow for the multiobjective optimization of directional well trajectories in underground gas storage (UGS) reservoirs. A modular well-path construction model is developed, enabling flexible assembly of linear and curved segments in a local reference frame and their transformation into the reservoir. The optimization problem is formulated to simultaneously maximize working-gas capacity and minimize total drilling length for ten new directional wells. A calibrated UGS reservoir with more than 30 years of production history is used as the simulation environment, and solution quality is explored using the NSGA-II (non-dominated sorting genetic algorithm) evolutionary algorithm. The results reveal a diverse Pareto front of feasible designs. The best configurations achieve either an 8.6% reduction in total drilling length while still delivering a 2.12% capacity increase, or a 3.18% capacity enhancement at a modest drilling-length increase of 4%. These outcomes demonstrate that strategic redesign of well trajectories alone can deliver measurable improvements in UGS performance without modifying well controls or facility constraints. The proposed methodology provides a generalizable and computationally efficient framework for large-scale multiwell planning in UGS systems. Its modularity supports future extensions, including collision avoidance, perforation optimization, and adaptive well-control strategies. Full article

Large-scale post-marketing drug safety data from spontaneous reporting systems offer new opportunities to explore adverse drug events (ADEs). However, these datasets often contain high rates of missing and incomplete data, undermining the reliability and interpretability of pharmacovigilance analyses. Effective management of these data quality issues requires interactive tools to explore patterns of missingness across multiple dimensions. We present IRVINE (Interactive Visualization for Spontaneous Reporting Systems Databases Missing Values), an interactive visualization system designed to explore and compare missing data in spontaneous reporting systems. IRVINE integrates multiple coordinated components—including a global overview, detailed attribute-level breakdowns, a temporal analysis interface, and a cross-database comparison environment—allowing users to fluidly transition between global summaries and fine-grained diagnostic views. The system supports dynamic filtering, drill-down exploration, and interactive temporal analysis to examine changes in data completeness over time and across categories. Through three usage scenarios and a user study, we demonstrate how IRVINE supports effective exploration of reporting completeness. Results indicate that users perceived the system as easy to use and effective for identifying missingness patterns, with particular strengths in comparative and detail-level analysis. This work lays a foundation for improved transparency, interpretability, and data quality assessment in large-scale pharmacovigilance systems. Full article

Sandwich materials are increasingly used due to the possibility of improving their combined properties. However, some manufacturing operations become more complex, such as drilling, where it is more difficult to determine the optimal cutting conditions that provide the appropriate hole quality. In this context, the quality of the drilled holes of the carbon fiber plates with a foamed polyvinyl chloride core, a material used in marine environments at very low temperatures, among others, is analyzed. Due to the importance of surface quality in operations prior to the assembly of plates and the influence of delamination on the in-service behavior of materials, the objective is to determine the diameter deviation (∆D), circularity (CIR), and delamination (FD) at the entrance and exit of the hole after drilling plates of this material. This sandwich material has been drilled at low temperatures (−15, 0, and 15 °C) using compressed air as cooler. Different cutting conditions have been used regarding rotation speed and feed. An experimental and statistical study, including a response surface optimization for FD, and multiple response surface optimization for ∆D and CIR were used. Several ranges of suitable cutting conditions can be identified for each temperature. Full article

The Mabi Block is located in the southern Qinshui Basin, representing an underexplored region with high-rank coal seams that host significant Coalbed Methane (CBM) potential. Despite extensive CBM development in the nearby Anze and Zheng Zhuang blocks, the geological and geophysical controls on Coalbed Methane enrichment in Mabi remain insufficiently constrained. This study integrates the core data (63 samples) of isothermal adsorption tests, well-logging data from (13 wells), and 3D seismic attributes to systematically evaluate the key controlling factors, such as burial depth, roof and floor lithology, and sealing capacity, in the horizons of the No.3# and No.15# coal seams. Lithology is characterized using natural gamma ray (GR), acoustic (AC), deep resistivity (RD), compensated neutron log (CNL), and seismic wave impedance inversion. Coal quality parameters, ash content, and the Langmuir volume (V_L) are correlated with gas content, and structural controls are mapped using curvature, fault interpretation, and burial depth analysis. The results show that thick mudstone and limestone roofs, moderate burial depth (1100–1350 m), synclinal structural lows, and thicker coal seams (6–9 m) collectively enhance methane preservation. The ash content (%) exhibits a moderate negative correlation with the Langmuir volume (R² = 0.4) and gas content. Structural curvature (syncline) and fault intensity strongly govern lateral sealing integrity, where anticline zones and faulted regions display notable degassing. This integrated assessment contributes to a refined CBM optimization model for the Mabi Block and guides targeted future drilling, reservoir evaluation, and production optimization. Full article

To address the poor cutting stability and deterioration of hole quality caused by the inherent trade-off between chip evacuation performance and drill-body stiffness in U-drilling, a multi-objective optimization framework was established. The design variables were the core thicknesses ${\mathrm{L}}_1$ and ${\mathrm{L}}_2$ of the inner and outer chip flutes, the inner and outer offset angles ${\mathrm{\theta }}_1$ and ${\mathrm{\theta }}_2$, and the inner and outer helix angles ${\mathrm{\beta }}_1$ and ${\mathrm{\beta }}_2$. The objectives were to maximize the chip evacuation force and minimize the drill-body strain (which serves as an equivalent indicator of maximizing drill-body stiffness). The chip evacuation force was rapidly evaluated using a mechanistic chip evacuation force model derived from mechanism-based analysis. The drill-body strain was efficiently predicted using a GA–BP neural-network surrogate model. An NSGA-II algorithm combined with the entropy-weighted TOPSIS method was employed to solve the optimization problem, yielding the optimal parameter combination for the U-drill chip-flute geometry. The results show that drilling experiments on 42CrMo under the optimal structural parameter combination reduced the cutting forces in the $x$, $y$, and $z$ directions by approximately 11.2%, 13.1%, and 11.8%, respectively. The root-mean-square acceleration in the $x$ and $y$-directions decreased by about 17.3% and 22.9%, respectively. These improvements effectively enhanced the hole-wall surface roughness and hole diameter accuracy, and further improved chip evacuation smoothness and cutting stability of the U-drill. Full article

The Upper Triassic Xujiahe Formation (T₃x) represents a critical terrestrial source rock system in the Sichuan Basin, exhibiting pronounced vertical and lateral heterogeneity. Previous stratigraphic subdivisions relied primarily on lithological correlations rather than a systematic sequence stratigraphic framework. This approach has led to significant inconsistencies in source rock evaluation. Furthermore, recent discoveries of large gas fields, coupled with data from newly drilled wells, necessitate a comprehensive reassessment of this system. In this study, we re-evaluate the geochemical characteristics and spatial distribution of these source rocks within a newly established sequence stratigraphic framework. This assessment utilizes a robust dataset comprising total organic carbon (TOC) content, Rock-Eval pyrolysis, and vitrinite reflectance (Ro) measurements. The results indicate that the source rocks of the New Member 5 (T₃x⁵) in the slope belt of Central Sichuan exhibit the highest hydrocarbon generative potential. These rocks are characterized by high organic abundance (with 40% of samples showing TOC ≥ 2.0 wt.%), are dominated by Type III and II₂ kerogen (humic–sapropelic), and have reached the mature to high-maturity stage (Ro ranging from 1.0% to 1.7%). Notably, the cumulative thickness of these high-quality source rocks reaches 100~150 m. Specifically, the T₃x⁵ intervals in the Qiulin and Tianfu areas are identified as the most favorable hydrocarbon-generating centers. This reassessment under the new stratigraphic division provides a refined theoretical basis for future exploration targeting the Xujiahe Formation in the Sichuan Basin. Full article

With the advancement of high-end manufacturing, the application of difficult-to-machine materials such as titanium alloys and superalloys is becoming increasingly widespread. Their inherent material properties pose challenges during machining, including high cutting temperatures, rapid tool wear, and difficulty in controlling surface quality. Nanofluid minimum quantity lubrication (NFMQL) technology, as an advanced lubrication and cooling method, enhances the thermal conductivity and lubricating properties of fluids by uniformly dispersing nanoparticles in the base oil. This paper reviews the preparation methods, advanced atomization techniques, and core mechanisms of NFMQL technology. It focuses on analyzing the effectiveness of this technology in four major machining processes, turning, milling, grinding, and drilling, for typical materials such as titanium alloys, steel, and superalloys. Compared to dry cutting, conventional MQL, and poured cooling, NFMQL reduces cutting forces/torque, cutting temperatures, tool wear, and surface roughness while improving material removal rates, machining accuracy, and surface integrity. This paper concludes by summarizing the technology’s advantages, current challenges, and future research directions. Full article

Despite the promising exploration potential of the tight bioclastic limestone in the Carboniferous Shiqiantan Formation (Shiqiantan Sag, Junggar Basin), its reservoir characteristics remain poorly constrained. In particular, the macro and microscopic features and the key factors controlling reservoir development are still not well understood. We combined core observation, cast thin-section analysis, scanning electron microscopy (SEM), X-ray diffraction (XRD), high-pressure mercury intrusion, nitrogen adsorption, and nuclear magnetic resonance (NMR) to systematically characterize the Carboniferous bioclastic limestone reservoirs and identify the factors controlling their development in the Shiqiantan Sag. This study develops a multi-scale quantitative framework that integrates mercury intrusion–withdrawal behavior, nitrogen adsorption, and NMR T₂ spectra to constrain pore connectivity and the contribution of microfractures in tight carbonate reservoirs, providing a transferable approach for reservoir evaluation beyond the study area. The results categorize three rock microfacies in the study area: Bioclastic micrite, Micritic bioclastic limestone, and Sparry Bioclastic Limestone. The reservoir space is predominantly composed of secondary pores, including intragranular dissolution pores, intercrystalline clay pores, and microfractures. The pore structures exhibit a marked contrast across the lithofacies: the sparry bioclastic limestone develops the most optimal pore-fracture composite system, The pore structures exhibit a marked contrast across the lithofacies, directly correlating with hydrocarbon accumulation. Specifically, the sparry bioclastic limestone develops a pore-fracture composite system characterized by 25–100 nm pore throats, corresponding to the primary oil-bearing intervals observed in drilling. In contrast, the bioclastic micrite limestone and micritic bioclastic limestone, despite exhibiting localized nanoscale pores, lack effective connectivity (pore throats < 25 nm) and predominantly act as tight, dry layers with poor or no oil and gas shows, which endow them with the anomalous characteristic of relatively low porosity yet high permeability. This study reveals an integrated control on the development of tight bioclastic limestone reservoirs, in which sedimentary microenvironment and paleogeomorphology jointly determine the initial reservoir framework, while subsequent structural fractures and associated diagenetic dissolution play a critical role in modifying pore structures and enhancing reservoir quality. Sedimentary microfacies distribution, controlled by paleogeomorphologic variations, dictated the initial reservoir fabric. Subsequently, fracture systems generated by tectonic uplift, coupled with dissolution from meteoric freshwater leaching and organic acids, facilitated the development of secondary pores. Ultimately, the resulting optimization of the pore structure governs the final reservoir quality. The sparry bioclastic limestone is identified as the most promising exploration target in the study area. Its favorable reservoir quality is mainly attributed to its development on palaeogeomorphic and structural highs, where enhanced hydrodynamic energy and subsequent fracture-related dissolution significantly improved pore connectivity. These high-quality reservoirs are widely developed on gentle slope profiles and similar high-quality reservoirs may also locally occur at isolated palaeogeomorphic highs within steep-slope settings, as demonstrated by individual wells. Full article

This study focuses on the machinability optimization of bio-waste corn husk fiber–reinforced epoxy composites during drilling, with the objective of minimizing delamination and improving hole quality required for mechanical fastening applications. While natural fiber composites have been widely investigated, systematic statistical optimization of drilling parameters for corn husk fiber composites remains limited. The novelty of this work lies in identifying the dominant drilling parameter and establishing a clear damage-control strategy using a Taguchi L16 design coupled with ANOVA. Drilling experiments were conducted by varying spindle speed (1000, 1500, 2000, and 2500 rpm), drill diameter (6, 8, 10, and 12 mm), feed rate (00.05, 0.10, 0.15, and 0.20 mm/rev), and point angle (90°, 100°, 110°, and 120°). The results show that the drill diameter is the governing factor affecting delamination, contributing 73.52% of the total variation, followed by spindle speed (22.68%), whereas feed rate (3.14%) and point angle (0.38%) have minimal influence. The optimal condition (2500 rpm, 6 mm drill diameter, and 0.05 mm/rev feed rate) produced the lowest delamination and improved surface integrity. Microscopic observations confirmed reduced fiber pull-out and matrix cracking under these conditions. The main advantage of the proposed approach is the clear identification of parameter priority, enabling the industry to control drilling damage by primarily selecting appropriate drill diameter and spindle speed. The findings provide practical machining guidelines for the use of corn husk fiber composites in lightweight panels, automotive interior parts, and secondary structural components where reliable bolted joints are required. Full article

Continuous-wave mud pulsers enable real-time downhole communication during drilling; however, high actuation torque markedly increases energy consumption and limits deployment depth. In this study, we investigate valve plate geometry optimization for torque reduction through systematic CFD simulations using the SST k–ω turbulence model and analyzed the coupled effects of opening angle (20–30°) and chamfer height (4.0–6.0 mm) on hydraulic performance. The results reveal a previously uncharacterized torque-reversal phenomenon: introducing a chamfer shifts the torque zero-crossing point forward by up to 10°, fundamentally altering the torque–angle relationship. The main contribution is the establishment of quantitative correlations between geometric parameters and the torque–pressure decoupling mechanism, achieving a 45–60% reduction in peak torque while maintaining differential pressure within acceptable ranges for signal generation. Detailed flow-field analyses show that chamfers modify local velocity gradients and pressure distributions on valve surfaces, reducing flow resistance through improved momentum exchange. Dimensionless correlations between geometric parameters and performance metrics are developed, providing quantitative design guidelines for energy-efficient valve plates. Validation against baseline designs confirms that optimized geometries substantially reduce actuator power requirements without compromising signal quality. These findings provide practical design strategies for next-generation mud pulsers for deep well and extended-reach drilling, where energy efficiency is critical. The proposed optimization framework, based on the identified torque–pressure decoupling principle, is also applicable to other rotary valve systems requiring simultaneous optimization of actuation energy and functional performance. Full article