Search Results (427)

Conventional depletion development and waterflooding are often ineffective in tight oil reservoirs because of their ultra-low permeability, complex fracture–matrix architecture, and limited fluid mobility. Although inter-fracture CO₂ flooding has demonstrated considerable potential for enhanced oil recovery (EOR), the coupled effects of key operational parameters on reservoir pressure evolution, fracture–matrix mass transfer, and oil mobilization remain inadequately understood. In this study, a multi-component compositional simulation model, constrained by detailed geological characterization and calibrated through production history matching of the Yuan 284 block in the Changqing Oilfield, was developed to systematically evaluate the effects of CO₂ injection rate, injection–production time ratio, and shut-in duration on recovery performance and reservoir response. The results show that increasing the CO₂ injection rate from 1000 to 50,000 m³/d improves the recovery factor from 40.49% to 49.90%; however, the incremental recovery gain decreases markedly beyond 30,000 m³/d, which is aggravated by enhanced gas channeling through high-conductivity fracture pathways. Analysis of the injection–production time ratio indicates that an optimal ratio of 0.50 provides the best balance between reservoir energy replenishment and oil displacement efficiency, whereas excessively small ratios result in insufficient pressure support and reduced recovery. In contrast, extending the shut-in duration consistently lowers recovery performance by weakening fracture–matrix mass transfer and promoting pressure dissipation, demonstrating that immediate production following injection is more effective than prolonged soaking under the investigated conditions. The optimized operating scheme yields a recovery factor of 48.87%, substantially exceeding the representative waterflooding recovery level of 35.20%. These findings clarify the mechanisms controlling pressure maintenance, CO₂ utilization efficiency, and volumetric sweep during inter-fracture asynchronous CO₂ flooding, and provide both theoretical insights and practical guidance for the efficient development of ultra-low-permeability fractured tight oil reservoirs. Full article

Water blockage severely restricts gas transport in deep shale reservoirs, while effective mitigation requires a precise balance of multiple operational variables. This study utilizes core-flooding experiments to optimize the treatment processes of an amphiphobic fluorinated copolymer, focusing on the coupled roles of surfactant concentration, injected volume, and shut-in duration. The results show that permeability damage decreases rapidly with surfactant concentration, optimizing at 0.5 wt.%. Conversely, excessive liquid retention beyond a critical injection threshold of 1.0 PV triggers secondary water-blocking. Extending the shut-in duration to 8 days facilitates surfactant redistribution and interfacial equilibrium, gradually reversing rock wettability to a stable amphiphobic state. Crucially, the concurrent reduction in interfacial tension markedly lowers capillary resistance, allowing trapped water to detach and flow back under significantly lower driving pressures. This optimization effectively minimizes the energetic barrier for fluid displacement and creates a gas-preferential flow environment. The proposed laboratory operational window balances surfactant dosage, injection volume, and shut-in duration under the tested conditions, providing an experimental reference for optimizing post-fracturing cleanup, controlling liquid retention, and improving early-time gas flowback in shale gas reservoirs. Full article

Traditional reservoir flood control operations in China have long relied on a fixed flood-limited water level (FLWL), which frequently results in the underutilization of water resources during flood seasons. Dynamic FLWL regulation and joint reservoir operation have emerged as core strategies to optimize floodwater resource utilization while ensuring flood control safety. However, these approaches typically treat the flood control storage capacity of individual reservoirs as fixed constraints, failing to consider the potential for reallocating this capacity within a cascade reservoir system. This study explores the concept of “equivalent utilization of flood control storage capacity” among cascade reservoirs. Focusing on the four major reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) in the lower reaches of the Jinsha River, a methodology for analyzing the equivalent index of their flood control storage capacity is established. The core of this methodology involves a two-round scheduling simulation under various design flood scenarios. The first round of simulation adheres to standard operating rules, while the second round allows upstream reservoirs to retain additional flood volume—with downstream reservoirs correspondingly reducing their outflow—on the premise that downstream safety targets are satisfied. The equivalent index is defined as the ratio of the reduced storage capacity utilized downstream to the additional storage capacity utilized upstream. Nine design flood scenarios (covering three typical years with 1%, 2%, and 5% exceedance probabilities) for flood control in the Sichuan–Chongqing reach were analyzed, with the tightly coupled Wudongde–Baihetan and Xiluodu–Xiangjiaba reservoir pairs treated as two integrated units. The results indicate that the equivalent indices between these two reservoir groups range from 0.96 to 0.999, demonstrating near-perfect functional interchangeability of their flood control storage capacities for the specified research objective. For practical engineering application, a value of 0.96 is recommended as the lower-bound equivalent index. This study provides a methodological framework and specific index to support the dynamic, coordinated, and more efficient utilization of flood control storage capacity in large-scale cascade reservoir systems. Full article

The beneficial utilization of small and medium floods requires a clear flood-control safety boundary before floodwater can be moderately stored and regulated as a water resource. For the Three Gorges Reservoir, a large river-type reservoir with long-distance backwater effects and tributary blocking, this boundary cannot be determined solely from the dam-front water level. This study developed a one-dimensional unsteady hydrodynamic model with dynamic roughness calibration to investigate the risk-constrained flood-season operating water level of the Three Gorges Reservoir. Typical flood events and the 20-year return period design flood were used to examine the responses of the maximum dam-front flood-regulation water level, excess flood volume, longitudinal water levels, and exceedance risk at key reservoir-area sections under different initial regulation water levels and release-discharge conditions. The results show that the Changshou reach is the main control section for high-water-level inundation risk under the study scenarios. When the initial regulation water level is at or below 155 m, the dam-front flood-regulation water level, the peak water level at Changshou, and the exceedance duration generally vary only slightly. When the initial regulation water level exceeds 155 m, these risk indicators increase markedly, indicating a reduced flood-control safety margin. Perturbation analysis further shows that the dam-front flood-regulation indicators are relatively insensitive to small roughness and dam-front boundary perturbations, whereas the Changshou water level and exceedance duration are more sensitive to roughness and flood-volume perturbations. Therefore, 155 m should be interpreted as a conservative operational reference boundary under the current design-flood framework, existing operation rules, and the assumption of no forecast-based pre-release, rather than as an absolute safety threshold. Increasing release discharge can reduce high-water-level risk in the reservoir area under preset release limits, but its practical application must remain conditional on downstream flood-control constraints and real-time flood-conveyance capacity. The results provide a hydrodynamic basis for risk-constrained flood-season operation of large river-type reservoirs. Full article

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application. Full article

Efficient operation of rainwater pumping stations is essential for mitigating urban flooding under climate change. This study focuses on the Samcheok Osipcheon watershed, located in Gangwon-do, South Korea, and proposes a deep learning-based inflow prediction framework for the Samcheok-si drainage system using SWMM-simulated datasets. A total of 900 rainfall scenarios were generated and used to train three models: ANN, CNN, and LSTM. All models reproduced inflow hydrographs with high accuracy, but the CNN model showed overfitting with oscillations in the recession limb. The LSTM model demonstrated the best performance, achieving an NSE of 0.97 and a PPE of 3.45%. Based on the predicted inflow, two pump operation strategies were evaluated. The proactive operation considering upstream surcharge conditions, combined with second-level control, reduced peak water levels from 2.585 m to 2.439 m (approximately 5.6%) compared to the conventional operation. In addition, second-level pump operation reduced excessive discharge and stabilized detention basin water levels. The results indicate that the proposed framework can support real-time pump operation, enhance the resilience and sustainability of urban drainage systems, and contribute to sustainable urban flood mitigation. Full article

Accurate and continuous high-frequency water level monitoring is essential for flood control, water resource regulation, and hydrological studies in the Yellow River. However, traditional methods are often limited in complex inland river environments with insufficient temporal resolution, poor continuity, and weak robustness. In this study, high-frequency water level changes and their dynamic responses to water-sediment regulation were estimated at Huayuankou and Xiaolangdi stations based on a sliding window, variational mode decomposition (VMD), and multi-GNSS interferometric reflectometry (GNSS-IR). The results show that high-temporal-resolution water level series with a 5-minute interval were achieved at Huayuankou and Xiaolangdi. When compared with in situ gauge measurements, the GNSS-IR estimated water level has a root mean square error (RMSE) of 0.09 and 0.14 m, and coefficient of determination (R²) values with 0.92 and 0.96, respectively. These results demonstrated strong effects by reservoir regulation in both wide-and-shallow wandering reaches and canyon-controlled reaches. Water level responses to the 2025 water-sediment regulation operation showed that Xiaolangdi responded rapidly to upstream reservoir releases, whereas Huayuankou exhibited a delayed response, with the flood peak arriving about 21 h later and attenuating during downstream propagation. The proposed method shows strong potential for high-frequency water level monitoring and dynamic response analysis from GNSS-IR in complex inland rivers. Full article

This study presents a laboratory-scale prototype that integrates a zero-trust-based permission-control mechanism with real-time DDoS traffic detection in a 5G NSA IoT testbed. The proposed system was evaluated under three controlled traffic conditions: normal traffic, TCP SYN flood traffic, and UDP flood traffic. Packet-level data were collected from the experimental testbed and used to train and compare LSTM and SVM classifiers. Under the evaluated conditions, the LSTM model achieved the highest accuracy of 99.56%, outperforming the best SVM result of 93.20%. The selected LSTM detector was further deployed in the edge-computing pipeline and correctly identified the three tested traffic conditions during real-time operation. After malicious traffic was identified, the permission-control mechanism updated the corresponding authorization status, generated an alert, and restricted suspicious communication within the testbed. These results demonstrate the feasibility of linking traffic detection with authorization adjustment in a controlled 5G NSA IoT prototype. The findings should not be interpreted as a general validation against all DDoS variants or large-scale commercial 5G IoT deployments. Full article

In recent decades, flood drought abrupt alternation (FDAA) events have intensified markedly in the middle and lower reaches of the Yangtze River Basin (MLYRB), exposing limitations of the conventional single flood-limited water level (FLWL) operation of the Three Gorges Reservoir. To better address drought risk during the flood season, this study develops a phased and graded drought-limited water level (DLWL) operation framework. FDAA events were identified using a hybrid method combining the Short-term Flood-Drought Abrupt Alternation Index and the Standardized Runoff Index. A multi-objective optimization model solved by NSGA-III was employed to determine staged DLWLs across five operational periods with tiered thresholds prioritizing urban, ecological, and irrigation water demands. Results show that FDAA events are mainly concentrated in June–October and have intensified significantly since 2010. Compared with conventional operation, the optimized DLWL framework substantially improves irrigation water supply reliability and reservoir fullness, while maintaining urban and ecological water supply security. Validation during typical wet years indicates that the proposed strategy introduces no evident reduction in flood control safety. Full article

Quantifying river surface velocity and discharge is essential for flood control and mitigation. Traditional contact measurement methods are capable of providing precise results, yet they demand considerable manpower and material resources and face implementation challenges in flood seasons. Image velocimetry methods have attracted extensive attention due to their low cost, simplicity in operation, and safety. However, most of them lack a physical basis and interpretability. This paper introduces a river flow estimation algorithm combined with Physics-Informed Neural Networks (PINNs). The introduction of the convection–diffusion equation based on optical flow enables the model to better fit the flow characteristics of water and provides stronger physical support for the measurement results. The adoption of this equation as the loss function and the introduction of multiple scenarios eliminate the need for labeled data in the PINNs training process. The experimental results in both artificial and natural river channels demonstrate that the relative errors of the discharge measured by the proposed method are 0.66% and −1.75%, and the relative errors of the mean velocity are 0.64% and −2.33%. Compared with other methods, the proposed method exhibits superior performance. Full article

Intensive human activities are reshaping inland tail-end deltas. Based on hydrological and sediment data from 1950 to 2023 and physical model experiments, this study examines the Ganjiang tail-end delta to analyze channel evolution, driving mechanisms, and management pathways. Results indicate that the Wan’an Reservoir and large-scale sand mining are the dominant drivers of flow-sediment regime shifts and channel reshaping. Sand mining has caused severe riverbed incision, with a local maximum depth of 16.5 m. During the dry season, the flow diversion ratio of the West Branch exceeds 90%, fundamentally altering the flow distribution pattern. Although riverbed incision has enhanced local flood conveyance, the overall flood discharge capacity of the tail-end delta remains limited due to backwater from Poyang Lake, introducing new flood risks. Reduced sediment supply and hydrological changes have exacerbated wetland shrinkage and eutrophication. Physical model experiments show that the comprehensive regulation project can raise dry-season water levels by approximately 5 m through sluice operation, optimize flow diversion, and increase wetland surface water area by 56%. This project integrates flood control, ecological protection, and water resource utilization, representing a proactive exploration of adaptive management for deltas and providing scientific references for understanding evolution and guiding management in similar inland tail-end deltas. Full article

This study proposes a time-domain feature-based anomaly detection method for vibration data of bridge piers collected by underwater seismometers operating under alternating submerged and exposed conditions. The method aims to accurately identify anomalies under both normal and extreme events. Taking the Fuzhou Pushang Bridge as a case study, the acceleration root mean square (a_RMS) is adopted as the representative vibration feature to investigate the effects of vehicular loads, water level variations, and tidal fluctuations. The results show that pier vibrations are primarily dominated by vehicular loads, exhibiting pronounced daily periodicity, intraday non-stationarity, and non-normality, while the influences of water level and tidal variations are relatively minor. Based on these characteristics, an anomaly detection framework integrating STL decomposition (Seasonal-trend decomposition using Loess), Yeo–Johnson transformation, and control charts is developed. Historical data are used to establish control limits and conduct self-validation, yielding an anomaly rate of 0.14%, which is consistent with the theoretical probability of ±3σ control limits. When applied to the subsequent monitoring period, the anomaly rate under normal conditions is 0.18%, demonstrating the stability of the proposed method. Further analysis reveals that anomalies are primarily caused by direct hydrodynamic impacts on the instrument. Under flood conditions, continuous anomalies occur during nighttime, with the anomaly rate increasing to 4.44%. Under seismic conditions, the control chart statistic reaches 5.03, significantly exceeding the control limits. Comparative analysis shows that the percentile-based method yields a higher anomaly rate (0.65%), indicating a higher false alarm rate. Overall, the proposed method demonstrates strong generalization capability and reliability, providing effective support for long-term structural health monitoring of bridge substructures in complex environments. Full article

Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, message queuing telemetry transport (MQTT), and distributed network protocol 3 (DNP3), which were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure and is vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational technology (OT) environments. This paper presents an unsupervised convolutional neural network–long short-term memory (CNN-LSTM) model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when the prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios—Modbus supervisory control and data acquisition (SCADA) flooding, MQTT publish storm, and DNP3 response flooding—none of which are seen during training. Compared against an isolation forest baseline and an autoencoder baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging. Full article

Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly generation and hydro-meteorological records from eight hydropower plants in Kazakhstan. Two tasks are addressed: deterministic multi-step forecasting for D+1–D+7 and uncertainty-aware envelope forecasting for D+8–D+14 using MIN and Q90 targets. The benchmark uses Persistence as the primary baseline, against which RIDGE, SARIMAX, Random Forest, HistGradientBoosting, MLP, and LSTM are compared using Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). For D+1–D+7, the results reveal strong cross-station heterogeneity and the expected decline in skill with increasing lead time. In the aggregated comparison, SARIMAX achieves the highest mean NSE at D+1 (0.903), while RIDGE becomes strongest by D+7 (0.625), both outperforming Persistence (0.534 at D+7). At the station level, SARIMAX performs best for Kapch, Kask, Moin, Bukh, and Ustk, RIDGE is best for Shar and Lenin, and LSTM is best for Shulb. The strongest stations, Kapch and Kask, reach mean NSE values of 0.941 and 0.933, respectively, whereas Ustk and Bukh remain the most difficult cases. A central methodological contribution is a flood-sensitive switched hybrid strategy for Ust-Kamenogorsk based on an observed-generation high-flow window selected by a regime-score procedure. This strategy improves robustness at medium lead times: for SARIMAX, NSE increases from 0.587 to 0.739 at D+2 and from 0.161 to 0.559 at D+7, while for RIDGE, NSE increases from 0.549 to 0.701 at D+2 and from 0.109 to 0.435 at D+7, together with substantial RMSE and MAE reductions. For D+8–D+14, envelope forecasting remains informative, but model ranking becomes target-dependent: SARIMAX and RIDGE provide the strongest mean performance for MIN (0.664 and 0.658), whereas LSTM and RIDGE are strongest for Q90 (0.746 and 0.743). Overall, the results show that hydropower forecasting in Kazakhstan is best approached as a station-wise, regime-aware, and horizon-specific problem. Full article

Continuous streamflow monitoring is fundamental for water management in high-mountain Andean basins. Traditionally, this process relies on empirical regressions, although artificial intelligence (AI) has recently emerged as a robust alternative. However, extreme geomorphological dynamics compromise classical hydraulic methods, while AI models frequently lack physical validation. In this context, this study compares the performance of Artificial Neural Networks against traditional methods to reduce uncertainty in stage–discharge rating curves. The methodology, applied to a nested basin scheme in Loja, Ecuador, contrasted traditional exponential fits with a Multilayer Perceptron optimized using the Levenberg–Marquardt algorithm. The analysis included the evaluation of uncertainty bands and a sub-hourly spatial validation based on the principle of mass conservation. Results evidence that AI refines statistical accuracy (NSE > 0.95) and effectively adapts to bed non-linearity; nevertheless, cross-validation revealed a high susceptibility to algorithmic overfitting. It is concluded that while AI offers superior analytical flexibility for interpolating non-linear dynamics, traditional methods remain more robust for extreme flood extrapolation. Furthermore, while AI reduces computational complexity, it entails a higher “data cost” requiring denser field gauging campaigns. Operational viability requires rigorous dynamic uncertainty controls and spatial water balance validation. Full article