Search Results (1,792)

The complex fracture networks, multiphase flow behavior, and nonlinear flow mechanisms induced by hydraulic fracturing in horizontal wells of shale oil reservoirs pose significant challenges to production evaluation. In this study, a semi-empirical productivity evaluation method for multiphase shale oil systems is developed by integrating production dynamics with flow mechanisms. Three-phase productivity equations for oil, gas, and water are established, explicitly incorporating the underlying flow mechanisms. A nonlinear flow index is introduced to characterize both the stress sensitivity of fractures and the threshold pressure gradient in the matrix. Key unknown parameters, including oil saturation, water cut, stimulated reservoir volume, and nonlinear coefficients, are determined through history matching of production data. The impacts of geological properties, fracturing parameters, operating conditions, and nonlinear flow parameters on oil–gas productivity are systematically investigated using the proposed multiphase semi-empirical model. The model is validated against production data from fractured horizontal wells in a field case, demonstrating its accuracy and applicability. Furthermore, the model enables reliable production forecasting based on the derived productivity relationships. The proposed approach provides a practical and efficient tool for rapid post-fracturing productivity evaluation in shale oil reservoirs. Full article

This editorial synthesizes the key findings from 17 original research articles featured in the Special Issue on “Intelligent and Integrated Approaches for Efficient Oil and Gas Development.” The collection demonstrates a paradigm shift from purely data-driven methods toward physics-informed, interpretable, and operationally deployable intelligent systems across the upstream lifecycle. Advances span intelligent drilling with real-time model predictive control frameworks achieving sub-20 ms execution times and bottomhole pressure fluctuations below 0.30 MPa; AI-assisted reservoir characterization using multiscale convolutional neural networks, seismic waveform-constrained inversion, and geology-informed transformers that improve sandstone thickness prediction (R² = 0.895) and stratigraphic correlation (F1 = 0.886); production optimization through hybrid decomposition-ensemble models (R² = 0.954) and improved XGBoost (R² = 0.989); and enhanced oil recovery via self-assembled foam systems and polymer injector designs. Fundamental geochemical studies on the Qiongzhusi Formation shale and tight sandstone gas in the Ordos Basin provide critical geological constraints. The editorial identifies persistent challenges, including real-time performance versus physical fidelity, interpretability and uncertainty quantification, multi-scale integration, and generalizability across diverse geological settings. Future directions highlight reinforcement learning for autonomous operations, physics-informed digital twins, generative AI for subsurface scenario modelling, and integration with carbon capture, utilization, and storage. This Special Issue advances the convergence of petroleum engineering, artificial intelligence, and Earth sciences toward intelligent, secure, and sustainable hydrocarbon development. Full article

Accurate reservoir-fluid identification and water saturation prediction are essential for remaining-oil evaluation and water-flooding adjustment in heterogeneous oilfields. However, in the Youshashan Oilfield, southwestern Qaidam Basin, China, thin interbeds, strong reservoir heterogeneity, complex oil–water transitions, and inter-well logging-response differences make conventional single-task interpretation difficult. To address these problems, this study proposes a joint prediction method based on the Youshashan Fluid Prediction Network (YSF-Net) for six-class reservoir-fluid identification and continuous water saturation (S_w) prediction. A total of 200 wells were used and strictly divided by well into 140 training wells, 30 validation wells, and 30 independent test wells to avoid data leakage. Conventional logs were first processed through depth matching, outlier correction, robust standardization, and missing-value masking. Then, sliding-window logging sequences, regional stratigraphic embeddings, and reservoir-prior parameters, including shale volume, porosity, and permeability, were jointly input into the YSF-Net. The model uses a shared feature encoder with classification and regression branches to simultaneously identify oil layers, oil–water layers, water layers, and weakly, moderately, and strongly water-flooded layers, while predicting continuous S_w. A modified Simandoux-based physical consistency constraint was further introduced during training to improve the geological rationality of S_w prediction. Experimental results show that YSF-Net outperforms the CNN, BiLSTM, CNN-BiLSTM, and Transformer. It achieves an Accuracy of 0.926, Macro-F1 of 0.913, Macro-AUC of 0.968, S_w RMSE of 0.061, S_w MAE of 0.047, and S_w R² of 0.947. In direct cross-well testing without fine-tuning, YSF-Net obtains a Cross-well Accuracy of 0.918, Cross-well Macro-F1 of 0.904, and Cross-well S_w RMSE of 0.064. Ablation, transition-boundary, and typical well-interval analyses further demonstrate that regional constraints, reservoir-prior inputs, multi-task learning, and physical consistency improve class-boundary discrimination and S_w prediction reliability. The proposed method provides an accurate, consistent, and practical workflow for intelligent reservoir-fluid interpretation in heterogeneous reservoirs. Full article

Slump-type gravity-flow deposits are extensively developed in the Jiufotang Formation of the Naiman Sag, representing a core frontier for deep-water subtle hydrocarbon reservoir exploration. However, these deposits exhibit strong internal reservoir heterogeneity, while their diagenetic mechanisms are complex and their development pattern remains unclear. Integrating macroscopic and microscopic investigation of cores, scanning electron microscopy (SEM), micro-CT, and high-pressure mercury injection capillary pressure (MICP) data, a systematic study was conducted on the petrological characteristics and diagenesis of the gravity-flow reservoirs. The results indicate that the lithology is dominated by feldspathic lithic sandstones with low compositional maturity. The present-day reservoir quality is governed by the high spatiotemporal coupling between deposition and burial diagenesis. Compaction is the absolute dominant diagenetic factor driving the densification of these reservoirs. The strong compaction resistance, derived from a low argillaceous matrix content and a well-developed grain-supported framework, is the key to the formation of high-quality reservoirs. Furthermore, three distinct diagenetic pathways are revealed: the “high-energy freezing—rigid pore preservation” pathway controls the development of high-quality exploration sweet spots; the “shear mixing—plastic pore reduction” pathway forms low-permeability transitional reservoirs; and the “viscous suspension—compaction densification” pathway indicates widespread tight sandstone exploration targets. Full article

Background/Objectives: This study aimed to develop a novel tumor-associated macrophage (TAM)-targeting nanoplatform to improve the solubility and bioavailability of camptothecin (CPT) and achieve active targeted drug delivery for enhanced anti-tumor immunotherapy. Methods: We constructed a sialic acid-disulfide bond-camptothecin (SA-SS-CPT) nanowire system. Sialic acid was used as a targeting ligand to specifically recognize the overexpressed Siglec-E receptor on TAMs. Upon cellular internalization, the disulfide bond was designed to respond to intracellular glutathione (GSH), enabling controlled drug release. Results: The SA-SS-CPT nanowires significantly improved CPT solubility and enabled targeted delivery to TAMs. Following GSH-responsive cleavage and CPT release, the nanowires induced DNA damage in TAMs, activating the cGAS-STING signaling pathway. This promoted TAM polarization toward the M1 phenotype, enhanced pro-inflammatory and anti-tumor immune responses, and inhibited tumor immune escape. Furthermore, SA-SS-CPT synergistically improved the efficacy of PD-L1 blockade immunotherapy, remodeling the tumor immune microenvironment. Conclusions: The SA-SS-CPT nanoplatform effectively targets TAMs, repolarizes them to an anti-tumor M1 phenotype, and activates the cGAS-STING pathway. It shows strong potential for overcoming tumor immune escape and synergizing with PD-L1 checkpoint blockade to achieve significant tumor clearance. Full article

ASP flooding wastewater contains crude oil, suspended solids, anionic polymers and surfactants, with high viscosity, high zeta potential, difficult demulsification, flocculation and slow separation and sedimentation. In order to solve the problem of wastewater treatment of ASP flooding in oil fields, a magnetic branched core was prepared from ethyl silicate (TEOS), nano Fe₃O₄ and aminopropyl triethoxysilane (APTES), and then reacted with polyamine and methyl acrylate to synthesize the magnetic hyperbranched molecule FSNMN with demulsification ability. Using acrylamide (AM), acryloxyethyl trimethylammonium chloride (DAC) and maleic anhydride (MA) as raw materials, cationic polymer long chain (CAMHA) with flocculating properties was synthesized and grafted with hyperbranched molecules. The demulsification flocculation ability of the product regarding ASP flooding wastewater was evaluated, and the demulsification flocculation mechanism was summarized. The results showed that the average molecular weight of 3-FSNMN₄-C was 4.7 million, the cationic degree was 20.5%, and the saturation magnetization was 20 EMU/g. The removal rate of oil and suspended solids was 93.82% and 91.95% respectively when the simulated sewage was treated by magnetic field for 30 min. Magnetic hyperbranched star chain polymer provides a solution to the serious ecological environment problems caused by ASP flooding. Full article

SIRT2 (Sirtuin 2) is an NAD+-dependent deacetylase that exerts crucial regulatory effects on immune homeostasis and macrophage activation. While chronic cold exposure is a known predisposing factor for pulmonary dysfunction, the precise mechanisms by which SIRT2 potentially modulates lung macrophage polarization under cold stress remains poorly understood. In this study, we evaluated the protective capacity of SIRT2 using both wild-type (WT) and Sirt2-knockout (Sirt2−/−) murine models subjected to chronic cold exposure (4 °C for 3 h daily over 21 days). Our results demonstrated that Sirt2 deficiency significantly exacerbated cold-induced pulmonary histopathological damage and increased the secretion of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) (p < 0.05). Furthermore, chronic cold stress triggered a macrophage-centered inflammatory response, a process wherein SIRT2 was found to curtail M1 pro-inflammatory polarization. To further investigate these mechanisms, in vitro experiments were conducted using the mouse alveolar macrophage cell line MH-S. While LPS was utilized as a canonical inflammatory stimulus to mimic the injury environment, SIRT2 overexpression was found to reverse the LPS-induced increase in M1 markers and attenuate inflammatory cytokine secretion. These findings suggest that SIRT2 maintains intracellular homeostasis by modulating macrophage plasticity and plays a protective role in the development of chronic cold stimulus-induced lung injury. Consequently, SIRT2 activation may represent a potential therapeutic pathway for the treatment of environment-related respiratory diseases. Full article

Degraded network conditions and intermittent disconnections can impair solar greenhouse Internet of Things (IoT) systems by delaying cloud-to-field control, generating burst traffic after reconnection, and disrupting alarm feedback loops. This paper proposes a resilient end–edge–cloud collaborative framework for maintaining control continuity and closed-loop alarm reliability under unstable edge–cloud communication. The framework evaluates network quality using round-trip time, packet loss rate, and consecutive no-response duration, and combines hysteresis-based state switching, control leases, edge takeover, differential backfill, and locally persistent alarm-state synchronization. During disconnection, the edge gateway uses the latest valid configuration to execute fallback local control; after reconnection, high-priority events are uploaded first through a hierarchically rate-limited recovery strategy. In the scripted simulation experiments, the proposed method reduced peak backfill throughput from 2.16 ± 0.06 MB/s to 0.69 ± 0.01 MB/s, shortened high-priority event completion time from 17.3 ± 2.7 s to 2.0 ± 0.7 s, and increased the acknowledgment success rate at 20% packet loss from 76.5 ± 2.2% to 98.4 ± 0.8%. It also reduced the maximum temperature deviation during disconnection from 7.20 °C to 3.50 °C. These results suggest that the proposed framework can improve control continuity and alarm-loop completeness under the specified simulation settings. A supplementary trace-driven recovery evaluation using public 5G testbed measurements showed a similar qualitative trend. Broader validation with field-deployed greenhouse IoT platforms or hardware-in-the-loop testbeds is still needed. Full article

The exponential synchronization in mean square for complex networks by using two self-triggered impulsive control mechanisms under denial-of-service (DoS) and deception attacks is studied in this paper. These proposed control mechanisms include both static and dynamic forms. Meanwhile, under these control mechanisms, Zeno behavior is effectively avoided, which contributes to improved system stability. Additionally, two independent Bernoulli random sequences are introduced for integrating the denial-of-service (DoS) and deception attacks into a common modeling structure. Further, sufficient conditions for achieving exponential stability in mean square are derived by constructing the Lyapunov functional. Ultimately, the reliability and accuracy of the derived results are validated through simulation experiments. It is observed from the numerical experiments that dynamic self-triggered control enables more efficient utilization of the communication resources. Full article

To elucidate the wear evolution and failure mechanisms of VL-type composite seals in aviation hydraulic actuators under multiple operating conditions, a two-dimensional plane-strain finite element model was developed for a VL seal consisting of a PTFE L-ring and an NBR O-ring. The model incorporated the Mooney–Rivlin hyperelastic constitutive law and the Archard wear model. The effects of O-ring compression ratio, hydraulic pressure, sliding velocity, and temperature on cumulative wear, wear rate, and contact state were systematically investigated. The results show that the compression ratio has a nonlinear influence on wear. Within 8–16%, the peak wear increases approximately linearly with compression ratio; above 16%, the peak wear reaches a plateau and a secondary wear zone appears, indicating a transition from single-contact to multi-contact sealing. Hydraulic pressure promotes wear over the range of 4–28 MPa, and at 28 MPa the opposite lip edge of the L-ring comes into contact with the cylinder wall, weakening the sealing effectiveness. Within 0.1–0.3 m/s, wear increases approximately linearly with sliding velocity. However, under high velocity and insufficient hydraulic pressure, the L-ring may undergo inversion, resulting in complete seal failure. Temperature exhibits a non-monotonic effect: material softening reduces local contact stress and wear from −55 to 80 °C, whereas excessive softening at 135 °C causes the peak wear rate to increase again. A parametric analysis scheme involving an increased L-ring height and thickness, a reduced O-ring cross-section diameter, and reserved deformation space raises the critical compression ratio for stable single-contact sealing from 16% to above 20%. These findings clarify the contact-stress/contact-area competition mechanism governing VL seal wear and provide guidance for the design of aviation hydraulic actuator seals. Full article

According to an FAO report, the total area of saline-alkali land worldwide is approximately 954 million hectares, accounting for about 20% of global cultivated land. Saline-alkali stress significantly reduces soybean (Glycine max L.) yield and quality, and saline-alkali-tolerant plant growth-promoting bacteria (PGPB) have shown important application value for soybean planting in such farmlands. In this study, 15 strains of saline-alkali-tolerant bacteria were isolated from saline-alkali soil in Anda City, Heilongjiang Province, China, and identified morphologically, belonging to the genera Enterobacter, Bacillus, Chryseobacterium, Acinetobacter, Enterococcus, and Pseudomonas. Through tests for nitrogen fixation, phosphorus solubilization, potassium solubilization, hydrolase production (including pectinase, amylase, and protease), and germination promotion assays, Bacillus pumilus AD14 was identified as having the best growth-promoting effect on soybean seedlings. Pot experiments in saline-alkali soil showed that AD14 significantly promoted soybean seedling growth, increasing plant height by 5.63–6.37 cm and root length by 3.58–3.99 cm compared to the control. AD14 also enhanced saline-alkali tolerance by improving the activity of antioxidant enzymes including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) and increasing soluble sugar and protein contents. Meanwhile, soil pH decreased by 10.94–12.15% and soluble salt content decreased by 9.59–13.39% after planting, and soil enzyme activities (including urease, sucrase, and catalase) increased markedly. These results demonstrate the great potential of AD14 for soybean planting in saline-alkali soil. This study provides a relevant reference for enriching the resources of saline-alkali-tolerant PGPB and developing new biological agents suitable for soybean planting in saline-alkali soils. Full article

Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment. Full article

The cylindrical hydrocyclone can be regarded as a special-shaped hydrocyclone comprising entirely cylindrical sections without conical sections, featuring a unique flat-bottom design combined with central discharge, which promotes substantial particle circulation flow in the separation chamber, directly affecting separation performance. A validated TFM model is employed to investigate the flow field and particle motion behavior in the cylindrical hydrocyclone. The results indicate that the distributions of tangential velocity, radial velocity, pressure, and pressure gradient in the cylindrical hydrocyclone are consistent with patterns observed in the conventional hydrocyclone. The flat-bottom design combined with the central discharge configuration of the cylindrical hydrocyclone results in two distinct axial velocity transitions in the bottom region, forming downward axial velocity flow around the air core. Accordingly, particles moving toward the spigot must pass through the internal swirling flow region, facilitating the fine particles entrained by the coarse particles to enter the internal swirling flow, reducing the misplacement of fine particles in the underflow. Simultaneously, coarse particles entrained by the internal swirling flow return to the external swirling flow region under centrifugal force, forming a substantial coarse particle circulation flow. As a result, a mass of coarse particles accumulates in the separation chamber, hindering the centrifugal settling of medium particles and resulting in an enlarged cut size and severe coarse particle misplacement. Full article

Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland. Full article

Olfactory ensheathing cell (OEC) transplantation has been widely shown to support axonal regeneration, remyelination, and functional recovery after central nervous system injury; however, autologous approaches are limited by low cell yields from patient biopsies, which may be insufficient for large spinal cord lesions. This study evaluated whether cryopreservation could provide a scalable alternative by preserving the therapeutic potential of mucosa-derived OECs. Using a rat dorsal root injury model, cryopreserved mucosa-derived OECs (CmOECs) were thawed and assessed for viability, phenotype, and efficacy following transplantation. Although total viable cell yield was reduced compared with primary cultures, the relative proportion of OECs remained stable, and cells retained characteristic morphology and marker expression in vitro. In vivo, transplantation of CmOECs resulted in significant functional recovery in climbing and forepaw fault tasks compared with injured controls, with outcomes comparable to primary mucosal OEC transplantation. Immunohistochemical analysis confirmed the survival and integration of transplanted cells at the dorsal root entry zone, alongside evidence of axonal regeneration and astrocytic remodeling. These findings demonstrate that mucosa-derived OECs retain therapeutic efficacy following cryopreservation and support the development of standardized OEC biobanks as a scalable strategy for spinal cord repair. Full article