Single Reservoir Research Articles

Prediction of grain shoal reservoirs via seismic forward modeling and waveform classification: Application to the northern slope of the central Sichuan Uplift, Sichuan Basin, SW China

Grain shoals are one of the principal reservoir facies of carbonate rocks in oil and gas exploration domains. With the discovery of oil and gas in the grain shoal reservoirs of the Longwangmiao Formation in the central Sichuan paleo-uplift, the characteristics and distribution of grain shoal reservoirs are still unclear in the northern slope of the central Sichuan paleo-uplift. Through integrated analysis of wireline logs and high-quality 3D seismic data, combined with seismic forward modeling and waveform classification, reservoir seismic facies, reservoir classification, and the reservoir distribution of different grades are investigated. The results show that (1) under the thin thickness (i.e., the thickness is around 80 m) of the Longwangmiao Formation (i) when reservoirs are not developed, the seismic waveform is manifested as a single peak, and the top interface of the Longwangmiao Formation corresponds to 1/8 [Formula: see text] above the single peak. (ii) When a single set of reservoirs is merely developed in the second member of the Longwangmiao Formation (SMLF), the waveform displays a single peak, and the top peak moves downward. In addition, when multiple sets of reservoirs are developed in the SMLF, the seismic reflections of multiple reservoirs are consistent with those of a single reservoir with the same cumulative thickness. (iii) When the reservoirs are developed in the first member of the Longwangmiao Formation (FMLF) and SMLF, the single peak moves downward, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (2) Under the thick thickness (i.e., the thickness is approximately 110 m) of the Longwangmiao Formation, (i) when reservoirs are not developed, the waveform of the Longwangmiao Formation exhibits a double peak, and the top interface of the Longwangmiao Formation corresponds to the extreme value of the upper peak. (ii) When only a single set of reservoirs or multiple sets of reservoirs are developed in the SMLF, the Longwangmiao Formation exhibits a single peak, and the top interface of the Longwangmiao Formation corresponds to the trough. (iii) With the reservoir development in the first and second members of the Longwangmiao Formation, the waveform of the Longwangmiao Formation shows an asymmetric low-frequency peak, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (3) Using the waveform classification method, seismic waveforms corresponding to the Longwangmiao Formation could be summarized into four types (i.e., two types of waveforms associated with reservoir development and two types of waveforms associated with reservoir un-development). Two reservoir waveforms are distributed as two strips oriented approximately east–west and spread in the central study area, according to the planar study results that combine seismic attributes and waveform classification. The reservoir stripes near well PS1 represent the better-developed reservoir zones in the study area.

Modelling and evaluating restricted ESNs on single- and multi-timescale problems

AbstractReservoir Computing is a computing model ideal for performing computation on varied physical substrates. However, these physical reservoirs can be difficult to scale up. We propose joining various reservoirs together as an approach to solving this problem, simulating physical reservoirs with Echo State Networks (ESNs). We investigate various methods of combining ESNs to form larger reservoirs, including a method that we dub Restricted ESNs. We provide a notation for describing Restricted ESNs, and use it to benchmark a standard ESN against restricted ones. We investigate two methods to keep the weight matrix density consistent when comparing a Restricted ESN to a standard one, which we call overall consistency and patch consistency. We benchmark restricted ESNs on NARMA10 and the sunspot prediction benchmark, and find that restricted ESNs perform similarly to standard ones. We present some application scenarios in which restricted ESNs may offer advantages over standard ESNs. We then test restricted ESNs on a version of the multi-timescale Multiple Superimposed Sines tasks, in order to establish a baseline performance that can be improved upon in further work. We conclude that we can scale up reservoir performance by linking small homogeneous subreservoirs together without significant loss in performance over a single large reservoir, justifying future work on using heterogeneous subreservoirs for greater flexibility.

Reservoir-based flood forecasting and warning: deep learning versus machine learning

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, while GRU performed 4.69% (in R2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas.

The Legal System of Natural Ecological Protection and Restoration in Newly Built Areas Based on the Multi-Dimensional Character-Istics of the Ecosystem

Natural environment protection compensation refers to the legal system that protects the natural ecological environment, protects the natural environment and makes the beneficiaries of the natural environment get compensation by some means, so as to adjust the interests of the relevant subjects of natural ecological environment protection. This paper discusses the ecosystem service function and its type division of newly built areas in Ganjiang, and the emergy evaluation of ecosystem service function of newly built areas in Ganjiang, establishes the regular scheduling and joint optimal scheduling models of natural ecosystem service value single reservoir, intro-duces the corresponding model solving methods, and applies the ant colony algorithm to the optimal schedule is a lesson. According to the ant colony algorithm, the best way to study the region is to determine these algorithms. Combined with the kernel density analysis method, the spatial scope, potential corridors and key recovery points of ecological corridors are identified, and the optimization mode of natural ecological security pattern of Shule River is constructed. The experimental results show that the optimized ant colony algorithm proves that joint scheduling plays a more prominent role in ecological environment protection, mainly in ecological support and ecological regulation. At the same time, it verifies the applicability of ant colony algorithm in joint scheduling, and improves the average protection efficiency of natural ecology to 20.9%.

From single to multi‐purpose reservoir: A framework for optimizing reservoir efficiency

AbstractThe prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization.

Control and prediction of bedding-parallel fractures in fine-grained sedimentary rocks: A case from the Permian Lucaogou Formation in Jimusar Sag, Junggar Basin, Western China

The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration, enrichment, and efficient development of oil and gas. However, because of their variety and the complexity of the factors that affect them, their spatial prediction by the industrial community becomes challenging. Based on sample cores, thin sections, and well-logging and seismic data, this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution. Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces. Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity. They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves. Overall, the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness. The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures. Here, an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks. The method was based on the matched reservoir interval density, reservoir interval density, and matched sweet spot density of bedding-parallel fractures. The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data. The average relative prediction error was 8% in the upper sweet spot in the Lucaogou Formation, indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods. The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks, providing reliable technological support for the exploration and development of hydrocarbons.

Optimization Study on Sequential Emptying and Dredging for Water Diversity Reservoir Group

Reservoir sediment severely impacts water supply in water-scarce regions, making reservoir dredging an urgent global issue. The investment required for deep-water dredging far exceeds that for dry land dredging. Therefore, against the backdrop of the national water network construction, this study focuses on a typical inter-basin water transfer project in Northern China. To increase the proportion of dry land dredging volume and save costs, this study uses compensation reservoirs to replace the emptied reservoir in undertaking water supply tasks as a constraint. Single-objective optimization models for single reservoirs and multi-objective optimization models for reservoir groups are established, using game theory comprehensive subjective and objective weighting methods to select the optimal solution. The following conclusions are drawn from comparing the water supply effects under various emptying sequences: the optimal sequence for emptying reservoirs should be determined through precise quantitative analysis; as the dredging is completed, the water supply tends to stabilize; the satisfaction with the water supply and the variance of the water shortage rate are primarily related to reservoirs with a large inflow and storage capacity; dredging occurs according to the descending order of the storage capacity of reservoirs; and the startup proportion of pump stations shows an increasing trend.

Isotopic evidence for a common parent body of IIG and IIAB iron meteorites

Magmatic iron meteorites are thought to sample the metallic cores of differentiated planetesimals and are subdivided into several chemical groups, each representing a distinct parent body. The only exceptions are the groups IIAB and IIG, which have been proposed to sample two immiscible melts from the same core. To test this model, we report the first Fe, Ni, O, and Cr isotope data for IIG iron meteorites and the first high-precision O isotope data for IIAB iron meteorites. The new data demonstrate that IIG iron meteorites belong to the non-carbonaceous (NC) meteorites. This is evident from the isotope anomaly of each of the four elements investigated, where the IIG irons always overlap with the compositions of NC meteorites but are distinct from those of carbonaceous (CC) meteorites. Moreover, among the NC meteorites and in particular, the NC irons, the isotopic composition of the IIG irons overlaps only with that of the IIAB irons. The combined Fe-Ni-O-Cr isotope data for IIAB and IIG iron meteorites, therefore, reveal formation from a single isotopic reservoir, indicating a strong genetic link between the two groups. The indistinguishable isotopic composition of the IIAB and IIG irons, combined with chemical evidence for the formation of IIG irons as late-stage liquids of the IIAB core, strongly suggests that both groups originate from the same core. The results underscore the strength of utilizing multiple elements and their isotopic compositions to establish genetic links among meteorites, rather than using a single element. They also highlight the significance of integrating multiple geochemical tracers and petrologic observations to accurately determine genetic relationships and the formation of meteorites within the same parent body.

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

The Fagradalsfjall and Sundhnúkur Fires of 2021–2024: A single magma reservoir under the Reykjanes Peninsula, Iceland?

AbstractThe Reykjanes Peninsula (RP) hosts several volcanic lineaments that have been periodically active over the last 4000 years. Since 2021, following a ca. 800‐year quiescence, eight eruptions have occurred on the RP, with more expected in the future. To better understand the origins of this renewed volcanism and help forecast future eruptions, we examine (i) if the ongoing volcanism is fed from a single or multiple magma storage zone(s) or from several smaller reservoirs and; (ii) where the zone(s) are located (i.e. mantle or lower or upper crustal depths). Using major and trace element geochemistry, oxygen isotopes, and seismic tomography we rule out a single, RP‐scale, deep‐seated magma storage zone. Instead we propose the presence of a ca. 10‐km‐wide region of crustal‐level (9–12 km) magma accumulation beneath the Fagradalsfjall volcanic lineament that fed both the 2021–23 eruptions of the Fagradalsfjall Fires and the 2023–24 eruptions of the Sundhnúkur Fires.

Abundance, sizes, and major element compositions of components in CR and LL chondrites: Formation from single reservoirs

AbstractAbundances, apparent sizes, and individual chemical compositions of chondrules, refractory inclusions, other objects, and surrounding matrix have been determined for Semarkona (LL3.00) and Renazzo (CR2) using consistent methods and criteria on X‐ray element intensity maps. These represent the non‐carbonaceous (NC, Semarkona) and carbonaceous chondrite (CC, Renazzo) superclans of chondrite types. We compare object and matrix abundances with similar data for CM, CO, K, and CV chondrites. We assess, pixel‐by‐pixel, the major element abundance in each object and in the entire matrix. We determine the abundance of “metallic chondrules” in LL chondrites. Chondrules with high Mg/Si and low Fe/Si and matrix carrying opposing ratios complement each other to make whole rocks with near‐solar major element ratios in Renazzo. Similar Mg/Si and Fe/Si chondrule–matrix relationships are seen in Semarkona, which is within 11% of solar Mg/Si but significantly Fe‐depleted. These results provide a robust constraint in support of single‐reservoir models for chondrule formation and accretion, ruling out whole classes of astrophysical models and constraining processes of chondrite component formation and accretion into chondrite parent bodies.

A self-organization reconstruction method of ESN reservoir structure based on reinforcement learning

The dynamic reservoir of the randomly generated Echo State Network (ESN) contains numerous redundant neurons, resulting in collinearity in the high-dimensional state space matrix. This collinearity impacts the prediction performance of the network. In order to address this issue, this paper introduces a self-organizing ESN structure optimization model that is based on reinforcement learning, called SR-ESN. The SR-ESN model employs pruning methods to reconstruct the reservoir using contribution and decision mechanisms. To mitigate potential instability caused by high coupling among neurons in a single reservoir, the concept of ensemble learning is applied to create multiple initial reservoir pools, thereby enhancing screening diversity. Simultaneously, the model utilizes reinforcement learning's decision mechanism to identify effective neurons. Neurons with low contribution are pruned, while those with high contribution are retained for self-organizing reconstruction. This optimization of the network structure enhances its prediction performance. Based on both artificial and real datasets, the proposed SR-ESN model demonstrates superior prediction performance with minimal structural complexity compared to other prediction models.

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

On the second law of thermodynamics: A global flow spontaneously induced by a locally nonchaotic energy barrier

It is well known that, inherently, certain small-scale nonchaotic particle movements are not governed by thermodynamics. Usually, such phenomena are studied by kinetic theory and their energy properties are considered “trivial”. In current research, we show that, beyond the boundaries of the second law of thermodynamics where Boltzmann’s H-theorem does not apply, there may also be large-sized systems of nontrivial energy properties: when the system is isolated, entropy can decrease; from a single thermal reservoir, the system can absorb heat and produce useful work without any other effect. The key concept is local nonchaoticity, demonstrated by using a narrow energy barrier. The barrier width is much less than the nominal particle mean free path, so that inside the barrier, the particle-particle collisions are sparse and the particle trajectories tend to be locally nonchaotic. Across the barrier, the steady-state particle flux ratio is intrinsically in a non-Boltzmann form. With a step-ramp structure, the nonequilibrium effect spreads to the entire system, and a global flow is generated spontaneously from the random thermal motion. The deviation from thermodynamic equilibrium is steady and significant, and compatible with the basic principle of maximum entropy. These theoretical and numerical analyses may shed light on the fundamentals of thermodynamics and statistical mechanics.

A multiscale attribution framework for separating the effects of cascade and individual reservoirs on runoff

Climate change and human activities have great impacts on runoff. With the gradual development of cascade hydropower in the watershed, the reservoirs have increasingly impacted runoff. However, the current study mainly focuses on quantifying the impacts of human activities and climate change on runoff, lacking the exploration of the impacts of cascade reservoirs, and the attribution results are relatively rough. Therefore, this study utilized data-driven models to establish a runoff attribution framework with the basic steps of “interval runoff prediction and scheduling rule extraction”, which achieved the spatial scale separation of the impacts of cascade and individual reservoirs on the runoff, and the analysis of the impacts of each factor at multiple time scales. Taking the upper reaches of the Yangtze River mainstem as an example, we verified the applicability and accuracy of the framework, explored the impacts of climate change, human activities (without reservoir scheduling), and reservoir scheduling on runoff during the period 1980–2018. The research found: (1) Compared to the base period 1980–2005, the average multi-year runoff changes at Pingshan Station (during 2013–2018), Yichang Station (during 2006–2012) and Yichang Station (during 2013–2018) were − 2.61 %, −4.33 % and − 0.89 %, respectively, with decreasing, increasing, and flattening trends over time. (2) Reservoir scheduling is the main factor leading to runoff change, showing negative impacts during flood season and positive impacts during non-flood season. (3) Under the control domain of single and cascade reservoirs, the annual scale impacts of climate change, human activities, and reservoir scheduling on runoff accounted for approximately 1:1:8 and 2:2:6, respectively, showing a complex nonlinear relationship between the impacts of single and cascade reservoirs on runoff. This study provides ideas for quantitatively assessing the impacts of cascade reservoirs on runoff and provide a basis for comprehensively assessing the ecosystem and socio-economic impacts of reservoirs on future runoff changes.

Existence of reservoir with finite-dimensional output for universal reservoir computing

In this paper, we prove the existence of a reservoir that has a finite-dimensional output and makes the reservoir computing model universal. Reservoir computing is a method for dynamical system approximation that trains the static part of a model but fixes the dynamical part called the reservoir. Hence, reservoir computing has the advantage of training models with a low computational cost. Moreover, fixed reservoirs can be implemented as physical systems. Such reservoirs have attracted attention in terms of computation speed and energy consumption. The universality of a reservoir computing model is its ability to approximate an arbitrary system with arbitrary accuracy. Two sufficient reservoir conditions to make the model universal have been proposed. The first is the combination of fading memory and the separation property. The second is the neighborhood separation property, which we proposed recently. To date, it has been unknown whether a reservoir with a finite-dimensional output can satisfy these conditions. In this study, we prove that no reservoir with a finite-dimensional output satisfies the former condition. By contrast, we propose a single output reservoir that satisfies the latter condition. This implies that, for any dimension, a reservoir making the model universal exists with the output of that specified dimension. These results clarify the practical importance of our proposed conditions.

Factors аffecting mineralization of groundwater in deep oil and gas bearing horizons in the western part of the West Siberian megabasin

The purpose of this study was to identify the causes of the observed hydrogeochemical situation and the factors contributing to its formation within the Middle Jurassic hydrogeological complex in the western part of the West Siberian megabasin, using the Talinskoye oil field as an example. The research primarily involved constructing dependencies between mineralization (y) and various factors including porosity, reservoir pressure, modern and paleotemperature of the foundation, and depth of the foundation (x). The study evaluated the degree of connection between each parameter and mineralisation. The results showed that the initial hydrochemical state in the area is heterogeneous, with significant variations in mineralisation levels within a single reservoir. The closest dependencies have been revealed between mineralization and porosity of reservoir rocks (an inverse dependence), mineralization and reservoir pressure (a direct dependence). The practical significance of the study is justified by the need to understand the nature of the formation of the hydrogeochemical environment in order to preserve it, control changes, as well as solve issues related to the forecasting, preservation, and destruction of oil deposits.

Production forecasting methods for different types of gas reservoirs

Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up, plateau (peak), and decline during field development, with the whole process primarily modeled and forecasted using lifecycle models. SINOPEC's conventional gas reservoirs are dominated by carbonates, low-permeability tight sandstone, condensate, volcanic rocks, and medium-to-high-permeability sandstone. This study identifies the optimal production forecasting models by comparing the fitting coefficients of different models and calculating the relative errors in technically recoverable reserves. To improve forecast precision, it suggests substituting exponential smoothing method-derived predictions for anomalous data caused by subjective influences like market dynamics and maintenance activities. The preferred models for carbonate gas reservoir production forecasts are the generalized Weng's, Beta, Class-I generalized mathematical, and Hu-Chen models. The Vapor pressure and Beta models are optimal for forecasting the annual productivity of wells (APW) from gas-bearing low-permeability tight sandstone reservoirs. The Wang-Li, Beta, and Yu QT tb models are apt for moderate-to-small-reserves, single low-permeability tight sandstone gas reservoirs. The Rayleigh, Hu-Chen, and generalized Weng's models are suitable for condensate gas reservoirs. For medium-to-high-permeability sandstone gas reservoirs, the lognormal, generalized gamma, and Beta models are recommended.

Optimizing performance assessment of multi-reservoir operations for sustainable water management in a semi-arid region

Abstract Global challenges, such as population growth, rapid urbanization, and the impacts of climate change, are creating unprecedented demands on water resources in semi-arid regions. Meeting the surging needs for irrigation and water supply requires a departure from the limitations of single reservoir systems. Instead, the construction of multi-reservoir systems within semi-arid river basins is imperative. The research employs an integrated reservoir operation approach that facilitates the controlled release of surplus water from upstream reservoirs to downstream ones. The novel Teaching-Learning-Based Optimization (TLBO) model is utilized to determine optimal irrigation releases, subsequently forming the basis for evaluating reservoir operation performance through the lenses of reliability, resilience, and vulnerability. The findings of this study shed light on the performance of these reservoirs under different models. Notably, the Aji-2 reservoir operation exhibits higher levels of reliability and resilience when the TLBO model is employed, surpassing the outcomes of the Linear Programming (LP) model. Conversely, the vulnerability of the Aji-3 reservoir operation is more pronounced with the TLBO model, albeit reduced when compared to actual release years 2005, 2009, and 2013. This study adds to the development of reservoir operation policies that favor reduced vulnerability and increased reliability.

Re–operating the Bhumibol and Sirikit Dams Using Hybrid Neuro–Fuzzy Technique to Solve the Water Scarcity and Flooding Problems in the Chao Phraya River Basin

The decision support system to reservoir re–operation using Artificial Intelligence has been broadly studied and proven in term of the operational performances for both single and multiple reservoir system, this study applied Adaptive Neuro Fuzzy Inference System (ANFIS) technique for reservoir re–operation in Chao Phraya River Basin aiming to reduce water scarcity and flooding problems in the central region of Thailand. ANFIS is an integrated approach in which neural networks are utilized to enhance the fuzzy inference system and create fuzzy “IF–Then” reservoir operational guidelines with proper membership functions for reservoir re–operation. In this study, ANFIS operating rules were trained using two different datasets; long–term dataset (scenario 1) and water year–based dataset (scenario 2). It is revealed that the extent of yearly water deficit in critical dry years are totally reduced to nearly zero when re–operating with ANFIS operation rules, except in the year 2012. However, the yearly water deficit in year 2012 is also substantially reduced from 504 MCM by the current operation to 127 and 119 MCM for scenario 1 and scenario 2, respectively when two scenarios of ANFIS–based reservoir re–operation model were performed. Moreover, considerable total amount of spilled water from BB and SK Dams is definitely declined to 0 and 37 MCM in years 2002 and 2011, respectively when water year–based ANFIS model was implemented. In addition, it is expressed that average water storages of two main dams obtained from two scenarios of ANFIS model are substantially increased up to +6.08% and +6.94% for BB Dam and +0.09% and +1.62% for SK Dam in comparison with the current operation. This signifies that supplying water from dams to meet the target water demand through adaptive fuzzy–rules can be well handled and flooded water can be minimized.