Search Results (20)

The transition to hybrid work has become a defining feature of the post-pandemic IT sector, yet organizations lack empirical benchmarks for balancing flexibility with performance and well-being. This study addresses this gap by identifying an optimal hybrid work structure and exposing systematic perception gaps between employees and managers. Grounded in Self-Determination Theory and the Job Demands–Resources model, our research analyses survey data from 1003 employees and 252 managers across 46 countries. The findings identify a hybrid “sweet spot” of 6–10 office days per month. Employees in this window report significantly higher perceived efficiency (Odds Ratio (OR) ≈ 2.12) and marginally lower office-related stress. Critically, the study uncovers a significant perception gap: contrary to the initial hypothesis, managers are nearly twice as likely as employees to rate hybrid work as most efficient (OR ≈ 1.95) and consistently evaluate remote-work resources more favourably (OR ≈ 2.64). This “supervisor-optimism bias” suggests a disconnect between policy design and frontline experience. The study concludes that while a light-to-moderate hybrid model offers clear benefits, organizations must actively address this perceptual divide and remedy resource shortages to realize the potential of hybrid work fully. This research provides data-driven guidelines for creating sustainable, high-performance work environments in the IT sector. Full article

In tight reservoirs deposited in diverse sedimentary settings, the pore structure governs tight oil enrichment features and sweet-spot distribution. Taking the tight sandstone reservoirs of the lower third member of the Shahejie Formation in the Linnan Subsag of Bohai Bay Basin in China as an example, this study employs XRD to delineate petrological characteristics, while porosity and permeability measurements are used to quantify physical properties. In addition, thin section, SEM, HPMI, NMR, fractal theory, and cathodoluminescence experiments are applied to investigate pore structure characteristics and influencing factors. The results reveal two sedimentary systems: turbidity current and delta front deposits. Turbidite reservoirs exhibit the coarse pore-coarse throats (Type A), medium pore-medium throats (Type B), and fine pore-medium throats (Type C) pore structures. Delta front reservoirs are characterized by medium-pore-coarse-throat (Type D), medium-pore-fine-throat (Type E), and fine-pore-fine-throat (Type F) pore structures. Turbidite reservoirs show more favorable pore structures for oil exploration compared to delta fronts, in which lithofacies and diagenetic facies are the key influences. A genetic model identifies the highest-quality Type A forms in fine sandstone lithofacies under medium compaction–medium cementation–strong dissolution, with pore diameters averaging 10.84 μm in turbidite reservoirs. Conversely, the poorest Type F forms in argillaceous layered siltstone lithofacies under strong compaction, cementation, and weak dissolution diagenetic facies in delta fronts, with pore diameters averaging 0.88 μm. Consequently, the control effect of the pore quality means that Type A has the highest and Type F has the lowest oil-bearing capacity. These findings provide valuable guidance for the classification, evaluation, and exploration of tight oil sweet spots. Full article

The Permian Lucaogou Formation (PLF) shale oil reservoirs in the Junggar Basin exhibit significant lithological heterogeneity, which limits the understanding of the relationship between macroscopic and microscopic reservoir characteristics, as well as insights into reservoir quality. To address this gap, thirty core samples, exhibiting typical sedimentary features, were selected from a 46 m section of the PLF for sedimentological analysis, thin section examination, high-performance microarea scanning, and scanning electron microscopy. Seven main lithofacies were identified, including massive bedding slitstone/fine-grained sandstone (LS1), cross to parallel bedding siltstone (LS2), climbing ripple laminated argillaceous siltstone (LS3), paired graded bedding argillaceous siltstone (LS4), irregular laminated argillaceous siltstone (LS5), irregular laminated silty mudstone (LM2), and horizontal laminated mudstone (LM2). The paired graded bedding sequences with internal erosion surfaces, massive bedding, and terrestrial plant fragments suggest a lacustrine hyperpycnal flow origin. The channel subfacies of hyperpycnal flow deposits, primarily consisting of LS1 and LS2, reflect strong hydrodynamic conditions, with a single-layer thickness ranging from 1.3 to 3.8 m (averaging 2.2 m) and porosity between 7.8 and 14.2% (averaging 12.5%), representing the primary sweet spot. The lobe subfacies, composed mainly of LS3, LS4, and LS5, reflect relatively strong hydrodynamic conditions, with a single-layer thickness ranging from 0.5 to 1.4 m (averaging 0.8 m) and porosity between 4.2 and 13.8% (averaging 9.6%), representing the secondary sweet spot. In conclusion, strong hydrodynamic conditions and depositional microfacies are key factors in the formation and distribution of sweet spots. The findings of this study are valuable for identifying sweet spots in the PLF and provide useful guidance for the exploration of lacustrine shale oil reservoirs in the context of hyperpycnal flow deposition globally. Full article

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction of shale reservoir lithofacies is crucial for identifying sweet spots for oil and gas development. However, obtaining shale lithofacies through core sampling during drilling is challenging, and the accuracy of traditional logging curve intersection methods is insufficient. To efficiently and accurately predict shale lithofacies, this study proposes a hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. The model employs the Grid Search Method to automatically search for optimal hyperparameters, using the four classifiers as base learners. The predictions from these base learners are then used as new features, and a Logistic Regression model serves as the final meta-classifier for prediction. A total of 3323 data points were collected from six wells to train and test the model, with the final performance evaluated on two blind wells that were not involved in the training process. The results indicate that the stacking model accurately predicts shale lithofacies, achieving an Accuracy, Recall, Precision, and F1 Score of 0.9587, 0.959, 0.9587, and 0.9587, respectively, on the training set. This achievement provides technical support for reservoir evaluation and sweet spot prediction in oil and gas exploration. Full article

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases. Full article

The exploration of continental shale oil in China has made a breakthrough in many basins, but the pure shale type has only been found in the Qingshankou Formation, Gulong Sag, Songliao Basin, and the evaluation of shale oil occurrence and sweet spot faces great challenges. Using information about the total organic carbon (TOC), Rock-Eval pyrolysis, vitrinite reflectance (Ro), kerogen elemental composition, carbon isotopes, gas chromatography (GC), bitumen extraction, and component separation, this paper systematically studies the organic geochemical characteristics and shale oil occurrence at the Qingshankou Formation. The G1 well, which was cored through the entire section of the Qingshankou Formation in the Gulong Sag, was the object of this study. On this basis, the favorable sweet spots for shale oil exploration are predicted. It is concluded that the shale of the Qingshankou Formation has high organic heterogeneity in terms of organic matter features. The TOC content of the source rocks in the Qingshankou Formation is enhanced with the increase in the burial depth, and the corresponding organic matter types gradually changed from Ⅱ₂ and Ⅱ₁ types to the Ⅰ type. The distribution of Ro ranges from 1.09% to 1.67%, and it is the mature to high-mature evolution stage that generates a large amount of normal crude oil and gas condensate. The high-quality source rocks of good to excellent grade are mainly distributed in the Qing 1 member and the lower part of the Qing 2 member. After the recovery of light hydrocarbons and the correction of pyrolytic heavy soluble hydrocarbons, it is concluded that the occurrence state of shale oil in the Qingshankou Formation is mainly the free-state form, with an average value of 6.9 mg/g, and there is four times as much free oil as adsorbed oil. The oil saturation index (OSI), mobile hydrocarbon content, Ro, and TOC were selected to establish the geochemical evaluation criteria for shale oil sweet spots in the Qingshankou Formation. The evaluation results show that interval 3 and interval 5 of the Qingshankou Formation in the G1 well are the most favorable sections for shale oil exploration. Full article

The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO₂ and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of a more suitable fuel injection system and the control of the combustion process are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated, allowing the identification of a “sweet spot” in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40%, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMSs) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allows targeting H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and aftertreatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains. Full article

The Ordovician Wulalike Formation in the northwestern Ordos Basin is a new prospect for exploring marine shale gas in China, facing prominent problems such as unclear reservoir conditions and the distribution of enrichment areas. The types of reservoir space, fracture development, porosity composition, and physical properties of the lower Wulalike Formation are discussed through the multi-method identification and quantitative evaluation of reservoir space for appraisal wells. The Wulalike Formation in the study area contained fractured shale reservoirs with matrix pores (mainly inorganic pores) and permeable fractures. The fracture system of the lower Wulalike Formation is dominated by open bed-parallel fractures that are intermittent or continuous individually, with a width of 0.1–0.2 mm and spacing of 0.5–14.0 cm. The fracture-developed intervals generally exhibit bimodal or multimodal features on NMR T2 spectra and have a dual-track feature with a positive amplitude difference in deep and shallow resistivity logs. The length and fracture porosity of fracture-developed intervals varied greatly in different parts of the study area. In the Majiatan-Gufengzhuang area in the southern part of the study area, the fracture development degree generally decreased from west to east. In the Shanghaimiao area in the central part of the study area, fractures were extremely developed, the continuous thickness of the fracture-developed interval was generally more than 20 m, and the average fracture porosity was higher than 1.3%. In the Tiekesumiao area in the northern part of the study area, the fracture development degree was generally lower than that in the central and southern parts of the study area and also showed a decreasing trend from west to east. The lower Wulalike Formation had a total porosity of 2.46–7.08% (avg. 4.71%), roughly similar to the Longmaxi Formation in the Sichuan Basin, of which matrix porosity accounts for 34.0–90.0% (avg. 61.1%) and fracture porosity accounts for 10.0–66.0% (avg. 38.9%). From this, it could be inferred that the shale gas accumulation type of the lower Wulalike Formation in the northwest margin of the basin is mainly a fractured shale gas reservoir controlled by structure, and its “sweet spot area” is mainly controlled by tectonic setting and preservation conditions. This indicates that the Wulalike Formation in the northwestern Ordos Basin has good shale gas exploration prospects, and a large number of fault anticlines or fault noses formed by reverse dipping faults have the potential of favorable exploration targets. Full article

There is a close relationship between the size and property of a reservoir and the production and capacity. Therefore, in the process of oil and gas field exploration and development, it is of great importance to study the macro distribution of oil–gas reservoirs, the inner structure, the distribution of reservoir parameters, and the dynamic variation of reservoir characteristics. A reservoir model is an important bridge between first-hand geologic data and other results such as ground stress models and fracture models, and the quality of the model can influence the evaluation of the sweet spots, the deployment of a horizontal well, and the optimization of the well network. Reservoir facies modeling and physical parameter modeling are the key points in reservoir characterization and modeling. Deep learning, as an artificial intelligence method, has been shown to be a powerful tool in many fields, such as data fusion, feature extraction, pattern recognition, and nonlinear fitting. Thus, deep learning can be used to characterize the reservoir features in 3D space. In recent years, there have been increasing attempts to apply deep learning in the oil and gas industry, and many scholars have made attempts in logging interpretation, seismic processing and interpretation, geological modeling, and petroleum engineering. Traditional training image construction methods have drawbacks such as low construction efficiency and limited types of sedimentary facies. For this purpose, some of the problems of the current reservoir facies modeling are solved in this paper. This study constructs a method that can quickly generate multiple types of sedimentary facies training images based on deep learning. Based on the features and merits of all kinds of deep learning methods, this paper makes some improvements and optimizations to the conventional reservoir facies modeling. The main outcomes of this thesis are as follows: (a) the construction of a training image library for reservoir facies modeling is realized. (b) the concept model of the typical sedimentary facies domain is used as a key constraint in the training image library. In order to construct a conditional convolutional adversarial network model, One-Hot and Distributed Representation is used to label the dataset. (c) The method is verified and tested with typical sedimentary facies types such as fluvial and delta. The results show that this method can generate six kinds of non-homogeneous and homogeneous training images that are almost identical to the target sedimentary facies in terms of generation quality. In terms of generating result formats, compared to the cDCGAN training image generation method, traditional methods took 31.5 and 9 times longer. In terms of generating result formats, cDCGAN can generate more formats than traditional methods. Furthermore, the method can store and rapidly generate the training image library of the typical sedimentary facies model of various types and styles in terms of generation efficiency. Full article

Different types of shale-oil sweet spots have developed and are vertically stacked in multiple layers of the Qingshankou Formation in the Changling Depression, southern Songliao Basin. Furthermore, this area lacks a classification standard in the optimization of its shale-oil sweet-spot area/layers. Through relevant tests of the region in question’s organic geochemistry, physical properties, oiliness, and pore structure, this paper investigates the formation elements of shale-oil sweet spots. In addition, summaries of its enrichment-controlling factors are given, and the classification standard and evaluation method for understanding the comprehensive sweet spots of the interbedded-type shale oil are then established. The interbedded-type shale oil is enriched in the Qingshankou I Member in the Changling Depression, and it has the features of medium-to-high maturity, the development of inorganic pores and micro-cracks, as well as higher oil saturation and better oil mobility. The sweet-spot enrichment is affected by lamina type, sedimentary facies, maturity, and sand–shale combinations. Both silty-laminated felsic shale and argillaceous-laminated felsic shale, which are developed in semi-deep lakes, are favorable shale lithofacies as they have excellent brittleness and oil mobility. The high maturity and the interbedded combination of sand and shale ensure the efficient production of shale oil, among which the pure-shale section issues a continuous contribution to the production process. Combined with oil testing, sweet-spot classification standards and a comprehensive evaluation of interbedded-type shale oil were established. An area of 639.2 km² for the interbedded-type shale-oil sweet spots was preferred, among which type I (193 km²) belonged to the combination of “good shale and good siltstone interlayers adjacent”, and type II belonged to “good shale and medium siltstone interlayers adjacent” combination (which have long-term low and stable production prospects). The research provides theoretical guidance on the effective exploration and development of the shale oil of the Qingshankou Formation in the Changling Depression. Full article

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model’s feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established. Full article

The Chang 7 member of the Upper Triassic Yanchang Formation in the Ordos Basin is considered to hold the main source rocks for conventional and unconventional oil and gas. The lamination or lithology alteration in vertical and lateral directions, even over a short distance, is a common feature in lacustrine source rocks. The differences in the geochemical characteristics of black shales, dark mudstones and interbedded sandstones have been scarcely reported, and their influences on the petroleum generation potential and shale oil potential are not clear. To this end, 22 core samples were collected from the Lower and Middle Chang 7 (C7-3 and C7-2) members of the Triassic from well CYX in the Qingcheng area. By conducting a series of geochemical analyses including TOC, Rock-Eval pyrolysis yields, bitumen extraction and quantification, and the separation and quantification of saturates, aromatics, resins and asphaltenes, along with biomarker analyses, several results were found. Firstly, the C7-3 and C7-2 source rocks are thermally mature and have entered into the stage of hydrocarbon generation. The C7-3 and C7-2 source rocks have a good to very good hydrocarbon generation potential especially the C7-3 black shales. Secondly, terrigenous source input is more abundant in C7-2, whereas the source input of phytoplankton, algae or microbial lipids is more abundant in C7-3. Moreover, a high TOC content basically corresponds to low wax indexes, terrigenous/aquatic ratios (TARs), and Pr/nC₁₇ and Ph/nC₁₈ ratios and high C₂₇/C₂₉ regular sterane ratios, which suggests that the source input of phytoplankton, algae or microbial lipids is favorable for OM accumulation. Third, analyses of the molecular composition of saturated fractions in shales and interbedded sandstones and the production index demonstrate the migration of petroleum from organic-rich source rocks to their organic-lean counterparts. The C7-2 dark mudstones could be considered as a potential “sweet spot” since their oil saturation index (OSI) was the highest among all the studied samples and they are more enriched in light aliphatic fractions. Full article

The sediment provenance influences the formation of the shale gas sweet-spot interval of the Upper Ordovician–Lower Silurian Wufeng–Longmaxi shale from the Yangtze Platform, South China. To identify the provenance, the mineralogy and geochemistry of the shale were investigated. The methods included optical microscopy analysis, X-ray diffraction testing, field-emission scanning electron imaging, and major and trace element analysis. The Wufeng–Longmaxi shale is mainly composed of quartz (avg. 39.94%), calcite (avg. 12.29%), dolomite (avg. 11.75%), and clay minerals (avg. 28.31%). The LM1 interval is the shale gas sweet-spot and has the highest contents of total quartz (avg. 62.1%, among which microcrystalline quartz accounts for 52.8% on average) and total organic carbon (avg. 4.6%). The relatively narrow range of TiO₂–Zr variation and the close correlation between Th/Sc and Zr/Sc signify no obvious sorting and recycling of the sediment source rocks. Sedimentary sorting has a limited impact on the geochemical features of the shale. The relatively high value of ICV (index of compositional variability) (1.03–3.86) and the low value of CIA (chemical index of alteration values) (50.62–74.48) indicate immature sediment source rocks, probably undergoing weak to moderate chemical weathering. All samples have patterns of moderately enriched light rare-earth elements and flat heavy rare-earth elements with negative Eu anomalies (Eu/Eu* = 0.35–0.92) in chondrite-normalized diagrams. According to Th/Sc, Zr/Sc, La/Th, Zr/Al₂O₃, TiO₂/Zr, Co/Th, SiO₂/Al₂O₃, K₂O/Na₂O, and La/Sc, it can be inferred that the major sediment source rocks were acidic igneous rocks derived from the active continental margin and continental island arc. A limited terrigenous supply caused by the inactive tectonic setting is an alternative interpretation of the formation of the sweet-spot interval. Full article

Member 1 of the Cretaceous Qingshankou Formation (K₂qn¹ Member) in the Southern Songliao Basin, composed of mainly semi-deep and deep lacustrine shale layers, is rich in shale oil. Previous studies on shale reservoir characteristics mainly focused on marine shale strata, but few studies have considered lacustrine shale strata, so the pore-throat features and differences between the lacustrine shale reservoir and marine shale reservoir need to be studied. Taking the Class-I and II sweet spot sections and Class-III non-sweet spot section of Da’an shale oil demonstration area as examples, SEM (scanning electron microscopy) was used to qualitatively and semi-quantitatively describe the morphology and occurrence characteristics of the shale. Full-scale pore size distributions of lacustrine shale samples were quantitatively measured by N₂GA (nitrogen absorption) combined with dominant pore size segments tested by experiments. Finally, the lacustrine shale reservoir was compared with classical marine shale reservoirs, and factors influencing semi-deep lacustrine and deep lacustrine shale oil in a large depression basin were analyzed by XRD (X-ray diffraction). The results show that Class-I and II sweet spots are rich in organic matter, quartz, and carbonate minerals, have mainly type H2 nitrogen adsorption hysteresis loops, and contain mainly inorganic pores, such as intergranular and intragranular pores in nano-scale, forming nano-scale reservoirs. Lacustrine shale is obviously different from marine shale in terms of pore structure, and the development characteristics of the lacustrine shale pore structure are more influenced by mineral components. Factors affecting the development of shale oil reservoirs in K₂qn¹ member include mineral components, TOC (total organic carbon), and diagenetic processes. Quartz and carbonate minerals are good for enhancing reservoir quality, while clay minerals are destructive to the development of reservoirs. TOC is the material foundation and main factor for forming organic pores, but the higher the TOC, the smaller the diameter of the organic pores will be. Compaction, cementation, and dissolution are the main diagenetic processes controlling the development of reservoir space. Full article

The porosity and inhomogeneity of 3D printed polymer samples were examined using terahertz time-domain spectroscopy, and the effects of 3D printer settings were analysed. A set of PETG samples were 3D printed by systematically varying the printer parameters, including layer thickness, nozzle diameter, filament (line) thickness, extrusion, and printing pattern. Their effective refractive indices and loss coefficients were measured and compared with those of solid PETG. Porosity was calculated from the refractive index. A diffraction feature was observed in the loss spectrum of all 3D printed samples and was used as an indication of inhomogeneity. A “sweet spot” of printer settings was found, where porosity and inhomogeneity were minimised. Full article