Search Results (546)

Horizontal wells in shale formations, such as those in the South Pars gas field (Iran) and the Bakken shale (Canada/USA), are essential for production from ultralow-permeability reservoirs but remain limited by poor hole cleaning, high torque, and unstable fluid transport. This study integrates real-time field data from South Pars with Drillbench simulations in the Bakken to develop practical strategies for improving drilling efficiency. A water-based mud system (9–10.2 ppg, 29–35 cP) supplemented with 2 wt.% sulphonated asphalt was applied to mitigate shale hydration, enhance cuttings transport, and preserve near-wellbore injectivity. Field implementation in South Pars demonstrated that adjusting drillstring rotation to 90 RPM and circulation rates to 1100 GPM reduced torque by ~70% (24 to 7 klbf·ft) and increased the rate of penetration (ROP) by ~25% (8 to 10 m/h) across a 230 m interval. Simulations in the Bakken confirmed these improvements, showing consistent torque and pressure trends, with cuttings transport efficiency above 95%. Inducing controlled synchronous whirl further improved sweep efficiency by ~15% and stabilized annular velocities at 0.7 m/s. Overall, these optimizations enhanced drilling efficiency by up to 25%, reduced operational risks, and created better well conditions for field development and EOR applications. The results provide clear, transferable guidelines for designing and drilling shale wells that balance immediate operational gains with long-term reservoir recovery. Full article

To address the challenges of accurately predicting and controlling the annular equivalent circulating density (ECD) in ultra-deepwater gas hydrate-bearing formations of the Qiongdongnan Basin, where joint production of hydrates and shallow gas through dual horizontal wells faces a narrow safe pressure window and hydrate decomposition effects, this study develops an ECD prediction model that incorporates riser drilling operations. The model couples four sub-models, including the static equivalent density of drilling fluid, annular pressure loss, wellbore temperature–pressure field, and hydrate decomposition rate, and is solved iteratively using MatlabR2024a. The results show that hydrate cuttings begin to decompose in the upper section of the riser (at a depth of approximately 600 m), causing a reduction of about 2 °C in wellhead temperature, a decrease of 0.15 MPa in bottomhole pressure, and an 8 kg/m³ reduction in ECD at the toe of the horizontal section. Furthermore, sensitivity analysis indicates that increasing the rate of penetration (ROP), drilling fluid density, and flow rate significantly elevates annular ECD. When ROP exceeds 28 m/h, the initial drilling fluid density is greater than 1064 kg/m³, or the drilling fluid flow rate is higher than 21 L/s, the risk of formation loss becomes considerable. The model was validated against field data from China’s first hydrate trial production, achieving a prediction accuracy of 93%. This study provides theoretical support and engineering guidance for safe drilling and hydraulic parameter optimization in ultra-deepwater hydrate-bearing formations. Full article

The ridged cutter, a highly representative non-planar PDC cutter known for its strong impact resistance and wear durability, has demonstrated significant effectiveness in enhancing the rate of penetration (ROP) in hard, highly abrasive, and interbedded soft–hard formations. Understanding the crack evolution is fundamental to revealing the rock-breaking mechanism of ridged PDC cutters. To date, studies on rock breaking with ridged cutters have largely focused on macroscopic experimental observations, lacking an in-depth understanding of the crack evolution characteristics during the rock fragmentation process. This study employs the Finite–Discrete Element Method (FDEM) to establish a three-dimensional numerical model for simulating the interaction between the ridged cutter and the rock. By analyzing crack propagation paths, stress distribution, and the stiffness degradation factor (SDEG), the initiation, propagation patterns, and sequence of cracks around the cutter are investigated. The results indicate the following outcomes: (1) The ridged cutter breaks rock mainly by tensioning and shearing, while the conventional planar cutter breaks the rock by shearing. (2) The rock-breaking process proceeds in three stages: compaction, micro-failure, and volumetric fragmentation. (3) Crack evolution around the cutter follows the rule of “tension-initiated and shear-propagation”; that is, tensile damage first generates at the front of the crack due to tensile stress concentration, whereas shear damage subsequently occurs at the rear under high shear stress. Finally, mixed tensile–shear macro-cracks are generated. (4) The spatial distribution of cracks exhibits strong regional heterogeneity. The zone ahead of the cutter contains mixed tensile–shear cracks, forming upward-concave cracks, horizontal cracks, and oblique cracks at 45°. The sub-cutter zone is dominated by tensile cracks; the zone on the flank side of the cutter consists of a radial stress field, accompanied by oblique 45° cracks. The synergistic evolution mechanism and distribution law of tensile–shear cracks revealed in this study elucidate the rock-breaking advantages of ridged cutters from a micro-crack perspective and provide a theoretical basis for optimizing non-planar cutter structures to achieve more efficient volumetric fracture. Full article

Salmonella spp. outbreaks associated with eggs have attracted widespread concerns about food safety. To provide necessary information for further pasteurization processes and computer simulations induced by radio frequency (RF) and microwave (MW) energy, the dielectric properties, penetration depth, and heating rates of egg white, yolk, and eggshell were measured, calculated, or fitted by regression models. The results demonstrated that both the dielectric constant and dielectric loss factor of egg white and yolk decreased dramatically with raised frequency within the RF range from 10 to 300 MHz, and then reduced slightly within the MW range from 300 to 3000 MHz. Dielectric constant, and loss factor of egg white, yolk, and eggshell increased with raised temperature. The penetration depth of egg white, yolk, and eggshell decreased with increasing of frequency. RF waves had a deeper penetration depth than that of MW waves at the same temperature. The fourth-order polynomial models provided a good fit to the experimental data with large coefficients of determination (R² > 0.902). The heating rate of the egg samples increased with increasing RF voltage and microwave power, and the heating rate of yolk was higher than that of egg white or eggshell at the same conditions. This study offers essential data and effective guidance in developing and optimizing RF and MW pasteurization techniques for ensuring the microbial safety of eggs, using both experiments and mathematical simulations. Full article

Shale oil and gas reservoirs are characterized by low porosity and low permeability. The development of ultra-long horizontal wells can significantly increase reservoir contact area and enhance single-well production. Shale formations exhibit distinct bedding structures, high formation pressure, high rock hardness, and strong anisotropy. These characteristics result in poor drillability, slow drilling rates, and high costs when drilling horizontally, severely restricting efficient development. Therefore, accurately predicting the drillability of shale gas wells has become a major challenge. Currently, most scholars rely on a single parameter to predict drillability, which overlooks the coupled effects of multiple factors and reduces prediction accuracy. To address this issue, this study employs drillability experiments, mineral composition analysis, positional analysis, and acoustic transit-time tests to evaluate the effects of mineral composition, acoustic transit time, bottom-hole confining pressure, and formation drilling angle on the drillability of horizontal well reservoirs, innovatively integrating multiple parameters to construct a nonlinear model and introducing three intelligent optimization algorithms (PSO, AOA-GA, and EBPSO) for the first time to improve prediction accuracy, thus breaking through the limitations of traditional single-parameter prediction. Based on these findings, a nonlinear regression prediction model integrating multiple parameters is developed and validated using field data. To further enhance prediction accuracy, the model is optimized using three intelligent optimization algorithms: PSO, AOA-GA, and EBPSO. The results indicate that the EBPSO algorithm performs the best, followed by AOA-GA, while the PSO algorithm shows the lowest performance. Furthermore, the model is applied to predict the drillability of Well D4, and the results exhibit a high degree of agreement with actual measurements, confirming the model’s effectiveness. The findings support optimization of drilling parameters and bit selection in shale oil and gas reservoirs, thereby improving drilling efficiency and mechanical penetration rates. Full article

Driven by the “double carbon” goals, the penetration rate of distributed photovoltaics (PV) in distribution networks has increased rapidly. However, the continuous growth of distributed PV installed capacity poses significant challenges to the carrying capacity of distribution networks. Reinforcement learning (RL), with its capability to handle high-dimensional nonlinear problems, plays a critical role in analyzing the carrying capacity of distribution networks. This study constructs an evaluation model for distributed PV carrying capacity and proposes a corresponding quantitative evaluation index system by analyzing the core factors influencing it. An optimization scheme based on deep reinforcement learning is adopted, introducing the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the evaluation model. Finally, simulations on the IEEE-33 bus system validate the good feasibility of the reinforcement learning approach for this problem. Full article

Background: Brain metastases (BMs) are a common and challenging complication of non-small cell lung cancer (NSCLC), historically associated with a poor prognosis. The development of targeted therapies, specifically tyrosine kinase inhibitors (TKIs) for epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) gene alterations, has significantly improved treatment outcomes. Methods: This article reports and evaluates the efficacy of different generations of TKIs for NSCLC with BMs. The primary endpoints assessed are intracranial objective response rates (IC-ORR), progression-free survival (PFS), and overall survival (OS). The analysis considers TKIs as monotherapy and in combination with radiotherapy. It also examines the impact of newer generation TKIs with enhanced blood–brain barrier (BBB) penetration on intracranial control. The report further discusses the integration of systemic therapy with local modalities like stereotactic radiosurgery (SRS) and the safety profiles of these agents, including central nervous system (CNS) and metabolic adverse events. Results: Newer generation TKIs demonstrate significantly enhanced BBB penetration, resulting in superior intracranial control compared to older generations. These agents show remarkable intracranial activity, contributing to improved IC-ORR, PFS, and OS. The optimal integration of systemic therapy with local modalities, such as SRS, is still under investigation. Treatment with these TKIs is associated with distinct safety profiles, including novel CNS and metabolic adverse events, which require careful management due to prolonged treatment durations. Conclusions: The management of CNS metastases in NSCLC is evolving towards more proactive and personalized therapeutic strategies. Newer generation TKIs have profoundly reshaped the treatment landscape by offering superior intracranial control. Further research is needed to determine the optimal integration of these systemic therapies with local modalities and to effectively manage the associated adverse events. Full article

The global generation of industrial waste is increasing rapidly, with much of it either landfilled or discharged into marine environments, resulting in severe environmental pollution. To address this issue, extensive research has been conducted on utilizing waste materials as partial replacements for cement. Although concrete incorporating industrial by-products offers environmental advantages—such as reducing pollution and lowering CO₂ emissions—its application has been limited by poor early-age performance. In South Korea, the annual production of ferronickel slag (FNS) now exceeds 2,000,000 tons, yet its usage remains minimal. To improve this early-age performance, researchers have applied steam curing (SC), a method widely used in precast concrete, which can enhance the utilization of FNS-containing concrete. Some studies have individually evaluated the mechanical or microstructural properties of SC effects, but the combined effects of FNS and SC replacement in precast concrete have rarely been addressed. This study applied SC, a method widely used in precast concrete production, to improve the performance of FNS concrete and conducted a comprehensive evaluation to promote its practical application. For this purpose, ordinary Portland cement (OPC) was partially replaced with FNS at rates of 10%, 20%, and 30%. To assess the effects, tests were conducted on hydration heat, SEM, and XRD, along with evaluations of compressive and splitting tensile strength. Results identified 20% as the optimal replacement ratio. At this ratio, chloride penetration resistance and freeze–thaw durability were also assessed. Furthermore, FNS concrete was evaluated under both natural curing (NC, 28 days) and SC conditions to simulate precast production. Under NC, mechanical properties declined as the FNS content increased, whereas under SC, the performance of the 20% replacement mixture was comparable to that of the control. In addition, the chloride diffusion coefficient and freeze–thaw resistance were improved by 11% and 2%, respectively, under SC compared to NC. This study evaluated the feasibility of FNS-containing concrete, and further studies should be conducted to investigate the structural performance of FNS-containing reinforced concrete via methods such as flexural, shear, splicing, and debonding experiments. Full article

Ground-Penetrating Radar is a non-destructive tool for detecting subsurface structures. However, traditional image interpretation is often limited by medium complexity and noise. To improve detection efficiency and accuracy, this study combines deep learning techniques to develop an automatic embankment cavity identification system based on the YOLOv10 model. The research first constructs a training dataset containing GPR images of embankment cavities and expands the dataset through data augmentation strategies to enhance model adaptability. Subsequently, cross-validation is employed to fine-tune the hyperparameters of the YOLOv10 model, seeking optimal performance. The experimental results demonstrate that the YOLOv10 model successfully identifies cavities in radar images, achieving accuracy rates of nearly 90% and 97%. This study proves the potential of deep learning in GPR image analysis, effectively improving detection efficiency, accuracy, and automation levels, providing more reliable technical support for embankment safety inspection. Full article

In order to enhance the performance of 20# steel, this study successfully fabricated AlCoCrFeNi high-entropy alloy coatings with different WC contents (x = 0, 10, 20, 30 wt%) on its surface using plasma cladding technology. The effects of WC content on the microstructure, mechanical properties, and corrosion resistance of the coatings were systematically investigated. The results indicate that without WC addition, the coating consists of a dual-phase structure comprising BCC and FCC phases. With the incorporation of WC, the FCC phase disappears, and the coating evolves into a composite structure based on the BCC matrix, embedded with multiple carbide phases such as W₂C, M₇C₃, M_xC_γ, and Co₆W₆C. These carbides are predominantly distributed along grain boundaries. As the WC content increases, significant grain refinement occurs and the volume fraction of carbides rises. The coating exhibits a mixed microstructure of equiaxed and columnar crystals, with excellent metallurgical bonding to the substrate. The microhardness of the coating increases markedly with higher WC content; however, the rate of enhancement slows when WC exceeds 20 wt%. The hardness of 1066.36 HV is achieved at 30 wt% WC. Wear test results show that both the friction coefficient and wear rate first decrease and then increase with increasing WC content. The optimal wear resistance is observed at 20 wt% WC, with a friction coefficient of 0.549 and a wear mass loss of only 0.25 mg, representing an approximately 40% reduction compared to the WC-free coating. Electrochemical tests demonstrate that the coating with 20 wt% WC facilitates the formation of a dense and stable passive film in NaCl solution, effectively inhibiting Cl⁻ ion penetration. This coating exhibits the best corrosion resistance, characterized by the lowest corrosion current density of 1.349 × 10⁻⁶ A·cm⁻² and the highest passive film resistance of 2764 Ω·cm². Full article

As oil and gas exploration extends to deep, ultra-deep, and unconventional reservoirs, high drilling costs persist. Drill bit performance, as the critical rock-breaking component, directly governs efficiency and economics. While optimal bit selection boosts rate of penetration (ROP) and cuts costs, traditional expert-dependent methods struggle to address complex formation bit parameter interactions, suffering from low accuracy and poor adaptability. With artificial intelligence gaining traction in petroleum engineering, machine learning-based bit selection has emerged as a key solution. This study focuses on polycrystalline diamond compact (PDC) bits and proposes an intelligent bit selection method based on graph neural networks (GNNs), utilizing drilling records from over 100 wells encompassing 40 multidimensional features. Through comparative analysis of four intelligent models—random forest, gradient boosting (XGBoost), gated recurrent unit (GRU), and the GNN, the results demonstrate that the GNN achieves superior performance with an R² (coefficient of determination) of 0.932 and MAPE (mean absolute percentage error) of 6.88%. The GNN significantly outperforms conventional models in rock-breaking performance prediction. By establishing this GNN model for ROP and footage per run prediction, this study achieves intelligent bit selection that substantially enhances drilling efficiency, reduces operational costs, and provides scientifically reliable technical support for drilling operations in complex formation conditions. Full article

To achieve deep decarbonization in the transportation sector, this study employs life cycle assessment (LCA) and the GREET model to construct baseline and low-carbon scenarios. It simulates the evolution of emissions and energy consumption within Inner Mongolia’s public transportation energy system (including diesel buses (DBs), electric buses (EBs), and hydrogen fuel cell buses (HFCBs)) from 2022 to 2035, while exploring synergistic pathways for its low-carbon transition. Results reveal that under the baseline scenario, reliance on industrial by-product hydrogen causes fuel cell bus emissions to increase by 3.64% in 2025 compared to 2022, with system energy savings below 10%, and decarbonization potential will be constrained by scale limitations and storage/transportation losses in cold regions. Under the low-carbon scenario, deep grid decarbonization, vehicle structure optimization, and green hydrogen integration reduced system emissions and energy consumption by 66.86% and 40.44%, respectively, compared to 2022. The study identifies a 15% green hydrogen penetration rate as the critical threshold for resource misallocation and confirms grid decarbonization as the top-priority policy tool, yielding marginal benefits 1.43 times greater than standalone hydrogen policies. This study underscores the importance of multi-policy coordination and ‘technology-supply chain’ synergy, particularly highlighting the critical threshold of green hydrogen penetration and the primacy of grid decarbonization, offering insights for similar coal-dominated, cold-region transportation energy transitions. Full article

The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time. Full article

To effectively address challenges posed by high-penetration renewable energy to power system operation and reserves, this paper proposes a novel research framework. The framework considers transmission–distribution coordinated dispatch and optimizes reserve capacity. First, the framework addresses the volatility and uncertainty of wind and solar power output. It constructs a three-dimensional objective function incorporating generation cost, spinning reserve cost, and linear wind/solar curtailment penalties as core components. The study uses the IEEE 30-bus system as the transmission network and the IEEE 33-bus system as the distribution network to build a transmission–distribution coordinated optimization model. Power dynamic mutual support across voltage levels is achieved through tie transformers. Second, the framework designs three typical scenarios for comparative analysis. These include separate dispatch of transmission and distribution networks, coordinated dispatch of transmission and distribution networks, and a fixed reserve ratio mode. The approach breaks through the limitations of traditional fixed reserve allocation. It optimizes the coordinated mechanism between reserve capacity spatiotemporal allocation and renewable energy accommodation. Case study results demonstrate that the proposed coordinated optimization scheme reduces total system operating costs and wind/solar curtailment rates. This is achieved by exploiting the potential of regulation resources on both the transmission and distribution sides. The results verify the significant advantages of transmission–distribution coordination in improving reserve resource allocation efficiency and promoting renewable energy accommodation. The approach helps enhance power grid operational economics and reliability. Full article

As China advances toward its 2060 carbon neutrality goal, the electrification of inland waterway shipping has emerged as a strategic pathway for reducing emissions. This study constructs a 2025–2060 dynamic material flow analysis framework that integrates three core dimensions: (1) all-electric ships (AES) diffusion, estimated via a GDP-elasticity model and carbon emission accounting; (2) battery technology evolution, including lithium iron phosphate and solid-state batteries; and (3) recycling system improvements, incorporating direct recycling, cascade utilization, and metallurgical processes. The research sets up three AES penetration scenarios, two battery technologies, and three recycling technology improvement scenarios, resulting in seven combination scenarios for analysis. Through multi-scenario simulations, it reveals synergistic pathways for resource security and decarbonization goals. Key findings include that to meet carbon reduction targets, AES penetration in inland shipping must reach 25.36% by 2060, corresponding to cumulative new ship constructions of 51.5–79.9k units, with total lithium demand ranging from 49.1–95.9 kt, and recycling potential reaching 5.4–25.2 kt. Results also reveal that under current allocation assumptions, the AES sector may face lithium shortages between 2047 and 2057 unless recycling rates improve or electrification pathways are optimized. The work innovatively links battery tech dynamics and recycling optimization for China’s inland shipping and provides actionable guidance for balancing decarbonization and lithium resource security. Full article