Reservoir Operation Research Articles

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

The spatiotemporal variations of microbial community in relation to water quality in a tropical drinking water reservoir, Southmost China.

It is well-known that water quality has great significance on microbial community composition in aquatic environments. In this study, we detected water column indicates the microbial community composition of nine sampling sites over two seasons using Illumina TruSeq sequencing in Songtao Reservoir, Hainan Province, Southmost China. The study indicated that the dominant phylum was Actinobacteria, Proteobacteria, Bacteroidetes, and Cyanobacteria. The diversity parameters showed that the microbial community composition had significant spatiotemporal variations, including the significantly higher Shannon index and Simpson index upstream than those midstream and downstream. Besides, there were significantly higher Chao1 index, Shannon index, and Simpson index in winter than in summer. Principal coordinates analysis (PCoA) showed the microbial structural composition had significant seasonal differences. The results of microbial community composition further revealed that the eutrophication level upstream was higher than that of midstream and downstream. The redundancy analysis (RDA) diagram indicated that the abundance of microbiology species significantly correlated with temperature, total phosphorus, Se, and Ni. Furthermore, the mantel's test showed that the temperature and total phosphorus significantly affected the community composition of archaea and bacteria. Overall, our finding here partially validated our hypothesis that the spatiotemporal variations of microbial community composition are significantly related to nutrients, physicochemical factors and metals, which has been unknown previously in tropical drinking waterbodies. This study substantially contributed to understanding of the composition of microbial community in tropical drinking water reservoirs and the main environmental driving factors in tropical zones. It also provided a reference for the management of reservoir operation to ensure drinking water safe.

Rainfall-runoff Modelling Using MIKE11 NAM Model for Ravishankar Sagar Catchment, Chhattisgarh, India

Estimating rainfall-runoff within a catchment is inherently intricate and crucial for water resource planning via hydrological evaluations. The study focuses on utilizing the MIKE 11 NAM model to simulate rainfall-runoff dynamics within the Ravishankar Sagar Reservoir catchment in the Chhattisgarh state. In order to ensure accurate estimation, data on stream flows from 2004 to 2015 was used for calibration, and from 2016 to 2020 was used for validation. The MIKE 11 NAM model accurately predicted daily runoff and adequately reproduced the hydrological response of the Ravishankar Sagar watershed to rainfall. The calibrated model outputs were good to employ in the water resources management model, specifically for MIKE BASIN. During calibration, the optimal values of the nine NAM model parameters were determined and subsequently employed in the simulation. The reliability of the MIKE 11 NAM model was assessed for the study area using the Coefficient of Determination (R2) and the Nash–Sutcliffe Efficiency Index (EI). The sensitivity analysis helped to determine the most important model parameters. R2 values of 0.730 and 0.704 were obtained from the model's calibration and validation, respectively. With an Efficiency Index of 81%, the model demonstrated its efficiency and ability to forecast runoff for Ravishankar Sagar Reservoir over an extended period. This study helps to manage the water resource and to improve the reservoir operation policy for the reservoir.

Hydrology and oxygen addition drive nutrients, metals, and methylmercury cycling in a hypereutrophic water supply reservoir

Impaired water quality in Mediterranean climate reservoirs is mainly associated with eutrophication and internal nutrient loading. To improve water quality in hypereutrophic Hodges Reservoir, California, United States, a hypolimnetic oxygenation system (HOS), using pure oxygen gas, was implemented in 2020. This study encompasses 3 years of pre-oxygenation data (2017–2019) and 2 years of post-oxygenation data (2020–2021) to understand the cycling of nutrients, metals, and mercury in the reservoir. During the wet year of 2017, mildly reduced conditions lasted until mid-summer in the enlarged reservoir. Nutrients and metals were seen in the hypolimnion including ammonia (~2 mg-N/L), manganese (~0.5 mg/L), phosphate (~0.5 mg-P/L), and sulfide (~10 mg/L). Production of methylmercury (MeHg), an important bioaccumulative toxin, was favored from April to June with a hypolimnetic accumulation rate of around 200 ng/m2·d. In contrast, the dry year of 2018 exhibited higher hypolimnetic concentrations of ammonia (~4 mg-N/L), manganese (~1 mg/L), phosphate (>0.5 mg-P/L), and sulfide (>15 mg/L). The rapid onset of highly reduced conditions in 2018 corresponded with low MeHg hypolimnetic accumulation (~50 ng/m2·d). It seems that mildly reduced conditions were associated with higher MeHg accumulation, while sulfidic, reduced conditions impaired inorganic mercury bioavailability for MeHg production and/or promoted microbial demethylation. Sulfide also appeared to act as a sink for iron via FeS precipitation, and potentially for manganese via MnS precipitation or manganese coprecipitation with FeS. Mass flux estimates for 2017–2019 indicate that much of the nutrients that accumulated in the hypolimnion moved via turbulent diffusion into the epilimnion at loading rates far exceeding thresholds predicting eutrophic conditions. After oxygenation in 2020–2021, the reservoir water column was highly oxidized but showed a lack of thermal stratification, suggesting reservoir operations in combination with HOS implementation inadvertently mixed the water column in this relatively shallow reservoir. Post-oxygenation, concentrations of ammonia, phosphate, manganese, and mercury in bottom waters all decreased, likely in response to oxidized conditions. Oxygenated bottom waters exhibited elevated nitrate, a byproduct of ammonia nitrification, and iron, a byproduct of FeS oxidation, indicating a lake-wide response to oxygenation.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Field monitoring and modelling of sediment transport, hydraulics and hydroabrasion at Sediment Bypass Tunnels

Sediment Bypass Tunnels (SBTs) are proven to be an effective measure to reduce or even stop reservoir sedimentation by bypassing sediment laden flows around reservoir dams to the downstream river reach. They are mostly used in Switzerland, Japan, and Taiwan. However, hydraulic and sedimentological operating conditions and the resistance of the invert materials against hydroabrasive erosion affect their cost-effectiveness. Hydroabrasion is a pressing issue at SBTs, other hydraulic structures and steep bedrock rivers exposed to high sediment transport rates under supercritical flow conditions. The present study was therefore conducted to address this issue by aiming at improving knowledge on abrasion mechanics and calibrating a mechanistic saltation abrasion model enhanced by Demiral-Yüzügüllü (2021). To this end, the abrasion resistance of fourteen different invert materials installed at Solis, Pfaffensprung and Runcahez SBTs in Switzerland was quantified by annual 3D laser scanning and the hydraulic conditions and sediment transport rates were regularly monitored between 2017 and 2021. The analysis of invert scans and hydraulic conditions revealed that Prandtl’s first and second kinds of secondary currents occurring in the bends and straight sections of the SBTs, respectively, and the observed abrasion patterns were strongly interrelated. The tested potassium aluminate cement and steel fibre concretes, granite, cast basalt and steel plates had better abrasion resistance against impact of sediment-laden flows compared to other materials. Sediment mineralogical composition i.e., bulk hardness relative to the invert material properties significantly affected hydroabrasion. The enhanced abrasion prediction model was calibrated with the present data and a quasi-constant abrasion coefficient of kv = (4.8 ± 2.2) × 104 was obtained. The enhanced model is well-suited for both laboratory and field scales. The present findings will contribute to the sustainable utilization and operational safety of hydraulic structures, optimization of SBT and reservoir operations regarding bypassing efficiency and reservoir lifetime and modelling of bedrock river erosion.

Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms

The operational rule curve is significant for proper reservoir operations and water resources management of the dam. The optimisation of the release policy is rather a complicated and stochastic problem. Various types of algorithms have been used to develop the release policy for the Klang Gate Dam (KGD), but alas, climate parameters associated with climate change, be it regional or globally, e.g., temperature factors, had not been considered then. In this study, the consideration of maximum and minimum temperature factors is given emphasis, while water demand is evaluated in the optimisation problem using simulation-optimisation approaches. The support vector regression simulation model (SVRS), and the multilayer perceptron simulation model (MLPS) were used to simulate the demand for the scenario cases. SVRS utilises the hyperplane theory to study the relationship between the input and output data, whereas MLPS employs the human neural system to weight and process the inputs via multiple layers of neurons into a targeted output. Four distinct types of simulation-optimisation combination model, including the SVRS-firefly algorithm (SVRS-FA), the SVRS-particle swarm optimisation (SVRS-PSO), the MLPS-firefly algorithm (MLPS-FA), and the MLPS-particle swarm optimisation (MLPS-PSO), were applied and investigated. The ultimate goal of this study was to minimise the water deficit. The PSO optimisation models overall have outperformed the FA optimisation models in all aspects. The periodic reliability of the MLPS-PSO was found to be the highest when the minimum temperature scenario was considered, and is 0.6491, with the least shortage period of 80 months. The MLPS-PSO is also the most resilient model in the same scenario, with a resilience value of 0.6750. Whereas the SVRS-PSO was the least vulnerable model in the minimum temperature scenario with the lowest shortage index of 0.0047 and a volumetric deficiency of about 2.44 MCM in total. The SVRS-PSO model showed excellent performance for high and low inflow conditions, while the MLPS-PSO model was better during medium inflows. In short, each of the models investigated has its particular field of advancement, but since the maximum temperatures sounded critical, additional research for the maximum temperature case could be pursued further in depth.

Modeling Heat and Water Exchanges between the Atmosphere and an 85-km2 Dimictic Subarctic Reservoir Using the 1D Canadian Small Lake Model

Abstract Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020. Significance Statement Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

Rock strength weakening subject to principal stress rotation: Experimental and numerical investigations

During the construction and operation of gas storage reservoirs, changes in the principal stress direction can induce fracture propagation under conditions of lower differential stress, potentially leading to failure in the surrounding rock. However, the weakening of strength due to pure stress rotation has not yet been investigated. Based on fracture mechanics, an enhanced Mohr-Coulomb strength criterion considering stress rotation is proposed and verified with experimental and numerical simulations. The micro-damage state and the evolution of the rock under the pure stress-rotation condition are analyzed. The findings indicate that differential stress exceeding the crack initiation stress is a prerequisite for stress rotation to promote the development of rock damage. As the differential stress increases, stress rotation is more likely to induce rock damage, leading to a transition from brittle to plastic failure, characterized by wider fractures and a more complex fracture network. Overall, a negative exponential relationship exists between the stress rotation angle required for rock failure and the differential stress. The feasibility of applying the enhanced criterion to practical engineering is discussed using monitoring data obtained from a mine-by tunnel. This study introduces new concepts for understanding the damage evolution of the surrounding rock under complex stress paths and offers a new theoretical basis for predicting the damage of gas storage reservoirs.

The Impacts of Changing Winter Warm Spells on Snow Ablation Over Western North America

AbstractAn increase in winter air temperature can amplify snowmelt and sublimation in mountain regions with implications to water resources and ecological systems. Winter Warm Spells (WWS) are defined as a winter period (December to February, DJF) of at least 3 consecutive days with daily maximum temperature anomaly above the 90th percentile (using a moving‐average of 15 days between 2001 and 2013). We calculate WWS for every 4‐km grid cell within an atmospheric model over western North America to characterize WWS and analyze snow ablation and their changes in a warmer climate. We find that days with ablation during WWS represent a small fraction of winter days (0.6 days), however, 49% of total winter ablation (33.4 mm/DJF) occurs during WWS. Greater extreme ablation rates (99th percentile) occur 18% more frequently during WWS than during non‐WWS days. Ablation rates during WWS in humid regions are larger (9 mm d−1) than in dry regions (7 mm d−1) in a warmer climate, which can be explained by differences in the energy balance and the snowpack's cold content. We find that warmer (0.8°C), longer (1.8 days) and more frequent (3.7 more events) WWS increase total winter ablation (on average 109% or 18 mm/DJF) in a warmer climate. Winter melt during WWS in warm and humid places is expected to increase about 3 times more than in the cold and dry region. This study provides a comprehensive description of WWS and their impact on snowpack dynamics, which is relevant to reservoir operations and water security.

Wonorejo dam operation and storage analysis against climate change

Climate change affecting rain intensity within catchment and increase the potential hydrometeorology disasters. Wonorejo Reservoir as a multipurpose dam requires an operational plan and water allocation to fulfill downstream needs, such as irrigation, hydropower, microhydropower, domestic industry uses and flood control. Wonorejo Dam is a backfill type dam with High Water Level (HWL) elevation is +183.00 mSHVP and Low Water Level (LWL) elevation is +141.00 mSHVP, Total storage capacity Wonorejo Reservoir is 109.118 million m3 (2021) or 89.4% from initial total storage capacity (2000), in 2050 predicted sediment volume increase to 17.73 million m3 or 17.6% reservoir capacity and 2050 capacity volume is 100,585 million m3 or 82.4% from initial total storage capacity. Annual Reservoir Operation Plan and Annual Water Allocation Plan for Wonorejo Reservoir in 2022/2023 as water services December 2022 to November 2023 periode, Wonorejo Reservoir elevation at the end of May 2023 reaches HWL and lowest water elevation is +162.64 mSHVP in the first week of November 2023.

A study on the division and determination of characteristic indexes of underground reservoirs under inclined bottom plates

ABSTRACT In recent years, the slope of the bottom plate of the underground reservoir in the loose porous medium constructed in the middle and upper reaches of the river is relatively large, which makes the underground water surface incline to a certain slope as a whole, near-horizontal section will appear in front of the underground dam during continuous recharge. Given this situation, the definition and calculation of the characteristic index of the underground reservoir in line with the actual changes are carried out. The characteristic water level of the underground reservoir is defined based on the groundwater surface with a certain slope at the inclined level in the case of the lower dam (single dam) impoundment. The characteristic capacity is calculated through the water level at the typical section defined by the inclined surface. A new set of definition and calculation method based on the characteristic index of the underground reservoir with a dam on the inclined bottom plates is proposed and applied to the Rushan River underground reservoir, which more accurately reflects the water level change and the corresponding actual reservoir capacity during the operation of the underground reservoir.

Water Level Fluctuation Effect on Fish Reproduction Success

Alteration of water levels of the lake/reservoir due to changes in river discharges, upstream abstraction, and drawdown regulation due to hydropower generation has been reported as among the major challenges to fish physiology and ultimately reproduction. Variations in lake/reservoir water levels influence biodiversity and the abundance of lake/reservoir biota. Variations have the greatest impacts in the littoral zones hence, interfering with fish spawning, incubation, and hatching of eggs, as well as the development of larvae, postlarvae, and juveniles. This particular paper reviews the literature available on the effect of the reservoirs/lakes’ water level fluctuations on fish reproduction success using the documentary view method. The literature argues that water level fluctuations have both positive and negative impacts depending on frequency, magnitude, and duration and the species exposed. Extreme fluctuations tend to bring more adverse impacts. It further indicates that extreme and untimely water level fluctuation has direct impacts on the aquatic habitats and ultimately impacts fish assemblage and their populations. Few articles indicate the range of decrease or increase of water level, duration of such events, and effects they have on reservoir/lake ecosystem functions and fish physiology. Nevertheless, quantification of moderate and extreme water level fluctuation and associated effects is lacking. Although water level fluctuation is an important environmental cue for aquatic organisms, literature shows that the reduction of extreme water level variations especially multiannual variations is vital for fish reproduction. Therefore, upstream water use and reservoir operations should take into consideration the effects of water level fluctuations on reservoir structure, biological functions, and ultimate effects on fish reproduction.

Oxygen evolution and its drivers in a stratified reservoir: A supply-side perspective for informing hypoxia alleviation strategies

Hypoxia in stratified waters greatly threatens aquatic ecology and societal development owing to enhanced nutrient discharge and increasing global temperature. Current research predominantly alleviates hypoxia by reducing dissolved oxygen (DO) consumption or conducting hypolimnetic oxygenation, yet their implementation has encountered bottlenecks. Therefore, this study explores the potential of increasing the inherent DO supplies in stratified reservoirs to mitigate hypoxia. High-frequency in situ observations and massive modeling experiments are integrated to discern the DO supply mode and the dominant driver of DO evolution. Results indicate that periodic thermodynamic conditions determine the DO supply relationships between oxygen sources (inflow carriage, reaeration, and photosynthesis) for different water layers. Thermal stratification causes the hypolimnion to rely mostly on the inflow for DO supply, leading to a fragile budget prone to hypoxia. However, episodic hydrodynamic events (turnover, wind stir, density current, and flood) can promote DO supply and inhibit hypoxia. Temperature and DO regimes are primarily driven by outflow conditions, followed by inflow and meteorology conditions. Furthermore, hypolimnetic hypoxia can be regulated by altering inflow volume, outflow volume, and outlet elevation. These findings highlight the importance of longitudinal solute exchange in DO evolution in stratified reservoirs, providing a basis for alleviating hypoxia through cascade reservoir operations.

IMPROVING THE SEALING EFFECT OF AN ECCENTRIC DOUBLE-DOOR HYDRAULIC PACKER

This research's primary goal is to identify ways for enhancing the hydraulic packer with eccentric power's molding impact and removing any potential causes. Since they ensure the efficacy of multi-stage hydraulic fracturing, packers are a well-known and essential component of hydraulic fracturing operations in low-permeability reservoirs. Packers enable the application of hydraulic pressure to specific wellbore segments. However, packer cuffs need to be dependable and have long-lasting qualities in order to survive formations subjected to high pressure and temperature (such as 80 MPa and 150 °C). In this study, a software program is employed to numerically optimize the rubber cuff constructions of the packer through trials, and the findings are verified. The rubber cuff with a regular structure serves as the starting point for the optimization process. The software then identifies the weakest places and improves them by making minor structural changes. The final, optimal construction of the expandable cuff is achieved by repeating this process. The findings demonstrate that the single rubber barrel's optimized construction works incredibly well in horizontal wells for packer rubbers and expanding reserve rings. Keywords: Packer collar, shaping effect, structure optimization, packer.

AI APPLICATIONS IN RESERVOIR MANAGEMENT: OPTIMIZING PRODUCTION AND RECOVERY IN OIL AND GAS FIELDS

This paper explores various AI applications in reservoir management, highlighting how machine learning, data analytics, and advanced algorithms contribute to optimizing production and recovery in oil and gas fields. From predictive modeling to real-time monitoring and control, AI offers promising solutions to address the industry's evolving demands and enhance operational performance Reservoir management in oil and gas fields has historically presented challenges demanding continuous monitoring, analysis, and optimization for maximizing production and recovery. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. This paper delves into diverse AI applications within reservoir management, emphasizing the pivotal roles played by machine learning, data analytics, and advanced algorithms in optimizing production and recovery processes in oil and gas fields. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. Keywords: Reservoir Management, Oil And Gas Fields, Optimization, Recovery, Machine Learning, Data Analytics, Predictive Modeling.

Optimal operation of the dam reservoir in real time based on generalized structure of group method of data handling and optimization technique

The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

Effect of total dissolved gas supersaturation on the passage behavior of silver carp (Hypophthalmichthys molitrix) and ya-fish (Schizothorax prenanti) through an experimental vertical slot fishway

Total dissolved gas (TDG) supersaturation caused by flood discharge water poses a threat to vital activities such as migration, foraging, and evasion in fish species upstream of the Yangtze River, which may impair the ability of fish to pass through fishways during the migration period, causing poor utilization of fishways. Previous studies have shown that TDG supersaturation reduces the critical and burst swimming abilities of fish, suggesting potential adverse effects on swimming performance. However, studies focusing on the impact of TDG on fish swimming behavior in experimental vertical-slot fishways remain scarce. Therefore, in this study, silver carp (Hypophthalmichthys molitrix) and ya-fish (Schizothorax prenanti) were used as the study species, and comparative passage experiments were carried out in an experimental vertical slot fishway to systematically analyze the effects of TDG supersaturation on their passage behavior. The passage success of the silver carp was 57%, 39%, 26%, and 27% at TDG levels of 100%, 110%, 120%, and 130%, respectively. Passage success of ya-fish was 73%, 37%, 31%, and 35% at TDG concentrations of 100%, 110%, 120%, and 130%, respectively. The passage time for both species increased significantly with increasing TDG levels. Furthermore, the passage routes of silver carp changed significantly compared to the control group, whereas the passage routes of ya-fish changed insignificantly. High levels of TDG supersaturation (≥120%) also contributed to a higher mortality rate of ya-fish passing through the vertical slot fishway. The research results provide valuable data on the influence of TDG supersaturation on fish movement behavior responses in experimental vertical slot fishways, offering a reference for the design of fishways and the formulation of reservoir operation schemes.

Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community

Abstract. In the modern world, dams and the artificial reservoirs behind them serve the increasing demand for water across diverse needs such as agriculture, energy production, and drinking water. As dams continue to proliferate, monitoring water availability influenced by reservoir operations is now of paramount importance. The Reservoir Assessment Tool (RAT) is a data-driven software platform that integrates satellite remote sensing with hydrological models, enabling the estimation of key reservoir parameters such as inflow, outflow, surface area, evaporation, and storage changes. The earliest version of RAT (version 1.0) was set up for 1598 reservoirs around the world with limitations in functional robustness, updating frequency, and scalability. Some of these limitations on updating frequency and functional robustness were addressed in version 2.0 that was later made operational for the intergovernmental agency of the Mekong River Commission. Recognizing the need for scalability to mobilize the global water management community to benefit from satellite remote sensing, we hereby introduce RAT version 3.0. This version is optimized for accelerating open collaboration among users for continuous improvement and customization of RAT to enable reservoir management breakthroughs. RAT 3.0 represents a wholesale overhaul from the previous versions to empower the global community of users and developers in the spirit of the open-source movement. RAT 3.0 allows reservoir monitoring advancements and new functional developments that can be freely exchanged and seamlessly integrated for continuous evolution of the software. A centralized web application has also been established to facilitate the storage and dissemination of global reservoir monitoring information along with comprehensive training resources. RAT 3.0 aspires to bridge the traditional practices of water management community with the capabilities of satellite remote sensing. The global impact of the software can be expected to increase as uptake spreads, enabling a more sustainable and equitable utilization of our planet's water resources.

Hydropeaking mitigation with re‐regulation reservoirs

AbstractThe role of hydropower as a renewable and balancing power source is expected to increase in a Net Zero Emissions by 2050 scenario. As a common phenomenon in hydropower plants, hydropeaking will become more prominent, resulting in additional stresses on the ecological status of rivers. Here we propose a novel approach to design and operate auxiliary reservoirs called re‐regulation reservoirs (RRR) that aim to mitigate the adverse impacts of hydropeaking on rivers. A re‐regulation reservoir aims at smoothing flow fluctuations caused by hydropeaking by diverting and retaining parts of high flows and returning them back to river corridors during low flows. Using actual data from a hydropeaking‐influenced river system, the operation and efficiency of potential reservoirs have been investigated. An open‐access algorithm was developed to analyze the influence of the reservoirs to mitigate hydropeaking, considering peak and minimum flow and up‐ and down‐ramping rates. The findings illustrate that, in most cases, the required reservoir volume increases as the flow thresholds become more stringent. Nonetheless, several exceptions were observed, where larger reservoir volumes were required compared with cases with more stringent thresholds. These findings highlight the importance of understanding the impact of flow adjustments, while carefully considering the river regime, sub‐daily flow patterns, and unique characteristics of the river's ecosystem. Our approach shows theoretical possibilities for regulating hydropeaking and provides a basis for optimizing re‐regulation reservoirs, contributing to practical and adaptable strategies for sustainable hydropower management without increasing the operational cost of power systems.