Reservoir Network Research Articles

Modeling the Inference between Upstream Inflow Hydrographs and Downstream Flooded Areas in a Reservoir Driven System

Management of a river imposes a task for several institutional levels in every part of the world, especially when reservoirs are the main flow drivers. The research presented herein shows how management of the flow in a reservoir driven system can be modeled by using an inference simulation model between two sets of variables, as an alternative to an approach based on an optimization model. The first selected variables are the explanatory ones, which refers to the relation between the operation of reservoirs and the input hydrographs to each of the considered reservoirs. The second set of variables are descriptive and refers to the resulting flooding hydrograph at a hydrological station located downstream of the reservoir network. The Yellow River, in China, is chosen to demonstrate the concept, however the methodology can be applied in practice for any reservoir driven system. In the mid stream of the Yellow River, a system of four reservoirs was built to manage flooding, using daily information and taking daily decisions. The proposed method uses a simplified simulation model of the actual reservoir operation to determine a multiple linear regression model between the set of explanatory and descriptive variables. The set of explanatory variables is very large and the demonstration is done on a selected subset of variables. Each selection of an explanatory variable is based on a correlation analysis with respect to the original set. Analysis of the model results shows that the reduction of the number of variables does not decrease the model fitness and robustness.

Climate change impacts on hydropower in an alpine catchment

Abstract Among the issues that are most relevant in the study of climate change (CC), hydropower plays a double role. On one hand, it will be affected by the change in water availability for hydropower plants. On the other hand, as the present major source of renewable energy, it is useful to support and increase the energy production and to reduce the human induced CC. The aim of the activity presented in this paper is the impact evaluation of expected CC on future reservoir control, the assessment of variations in hydropower production, and the identification of possible changes in water management. This activity is part of ACQWA project whose aim is the use of advanced modeling techniques to quantify the influence of CC on the major determinants of river discharge and to analyze their impact on society and economy. The activity has been focused on the development of a model of the management of a complex hydropower system as a function of climate conditions and electricity prices. The methodology was applied to a case study, the hydropower system in Valle d’Aosta Region in Italy. A conceptual scheme of the network of plants and reservoirs was developed and its optimal management was computed by means of a simple optimization tool, using energy prices and inflows as the main drivers of the system. The expected variations in reservoir management and power production were quantified through a comparison between optimization results of present and future years. Results show a statistically significant decrease in overall hydropower production; they give indications of variations in monthly production and interannual variability, causing an increase in water deficits and, as a consequence, in possible water conflicts among water uses. These results point out the limits of the current water policy and call for the introduction of a true integrated and adaptive approach.

Intelligent Inflow Tracers Obtain Information With Less Risk, Cost

Young Technology Showcase Today’s field developments are increasingly characterized by complex wells in demanding and costly operating environments. Not only are individual wells challenging, but most are part of a complex reservoir network that must be managed over an extended period. The high cost and risk of acquiring reservoir surveillance information using conventional technology, such as production logging tools, is forcing operators to manage these fields without knowledge of the inflow distribution across the reservoir interval. In addition, permanent reservoir surveillance solutions based on optical or electronic sensors require major modifications of the completion design and installation procedure. Consequently, a significant amount of complexity and risk are added to the project, making the use of such technologies prohibitive. Intelligent chemically based inflow tracer systems are an emerging technology that consist of engineered polymers and chemical compounds combined into a product that resembles strips of plastic. The tracer system is designed to react to either oil or water. For example, when a water-sensitive tracer system is contacted by water, the tracer system releases its unique chemical identification (inflow tracer) at a prescribed release rate, irrespective of the flowing conditions. The water-sensitive tracer system is dormant when contacted by oil, gas, or air. The oil-sensitive tracer system behaves similarly as the tracer is released only when it comes in contact with oil. Strips of the tracer system are readily integrated into almost any completion configuration including conventional sand screens, inflow control device (ICD) sand screens, multistage fracturing systems, pup joints, and intelligent completions. Additionally, the tracer system can be cemented behind casing and perforated through, providing contact between reservoir fluids and the intelligent tracer strips. In summary, the intelligent tracer strips can be deployed in almost any well type without affecting the installation process. Samples of produced fluid are analyzed for the concentration of each intelligent tracer. Tracer concentration data, combined with model-based interpretation techniques, enables the operator to understand where oil is entering the well, where water influx is occurring, and relative oil contributions of monitored zones.

Stochastic Programming for Improved Multiuse Reservoir Operation in Burkina Faso, West Africa

A network of reservoirs and diversion structures in the Comoé River Basin in southwestern Burkina Faso, West Africa, provides municipal water supply and irrigation for sugarcane agribusiness and downstream farmers. The region is characterized by severe intraseasonal and inter-annual variability of precipitation and reservoir inflows. Reservoir operations are generally conservative, even during relatively wet years. A stochastic decision support tool (DST) is described which translates a scenario-tree of streamflow and precipitation forecasts, conditionally weighted by a tercile seasonal precipitation forecast, into optimal release schedules for the upcoming year. It is shown that the DST coupled with the seasonal precipitation forecasts may provide more efficient and equitable release decisions that benefit the variety of stakeholders in the basin.

An analytical approach to photonic reservoir computing – a network of SOA's – for noisy speech recognition

This paper seeks to investigate an approach of photonic reservoir computing for optical speech recognition on an examination isolated digit recognition task. An analytical approach in photonic reservoir computing is further drawn on to decrease time consumption, compared to numerical methods; which is very important in processing large signals such as speech recognition. It is also observed that adjusting reservoir parameters along with a good nonlinear mapping of the input signal into the reservoir, analytical approach, would boost recognition accuracy performance. Perfect recognition accuracy (i.e. 100%) can be achieved for noiseless speech signals. For noisy signals with 0–10db of signal to noise ratios, however, the accuracy ranges observed varied between 92% and 98%. In fact, photonic reservoir application demonstrated 9–18% improvement compared to classical reservoir networks with hyperbolic tangent nodes.

Water Quality Evaluation Using Fuzzy Neural Network in Reservoir

Water Quality Evaluation Using Fuzzy Neural Network in Reservoir

Modeling Cytoskeletal Traffic: An Interplay between Passive Diffusion and Active Transport

We introduce the totally asymmetric simple exclusion process with Langmuir kinetics on a network as a microscopic model for active motor protein transport on the cytoskeleton, immersed in the diffusive cytoplasm. We discuss how the interplay between active transport along a network and infinite diffusion in a bulk reservoir leads to a heterogeneous matter distribution on various scales: we find three regimes for steady state transport, corresponding to the scale of the network, of individual segments, or local to sites. At low exchange rates strong density heterogeneities develop between different segments in the network. In this regime one has to consider the topological complexity of the whole network to describe transport. In contrast, at moderate exchange rates the transport through the network decouples, and the physics is determined by single segments and the local topology. At last, for very high exchange rates the homogeneous Langmuir process dominates the stationary state. We introduce effective rate diagrams for the network to identify these different regimes. Based on this method we develop an intuitive but generic picture of how the stationary state of excluded volume processes on complex networks can be understood in terms of the single-segment phase diagram.

Determination of Normal Operating Heights of Reservoirs in a Network Using Non-linear Reservoir Method

In reservoir modeling, making the steady-state policy of the flood control is a challenging task. In this paper, the normal operating heights of a reservoir network is discussed. Initially, the coupled tank system is employed to model the reservoir network. At the equilibrium state, the normal operating heights are then derived analytically. As the linearization is taken, the associated Jacobian matrix, which is a diagonally dominant tridiagonal matrix with negative diagonal entries, is obtained. Because of the negative eigenvalues of this matrix, the stability condition of the reservoir network can be made. For illustration, the different rain rates are used to calculate the surface area of the reservoir and the corresponding normal operating heights of the reservoir are then determined. The result obtained shows the stability of the reservoir. In conclusion, the approach discussed is efficient to the decision making on the stability of a reservoir network.

“On the Network, off the Map”: Developing Intervillage and Intragender Differentiation in Rural Water Supply

Despite decades of water-supply development programs in the Global South, their effect on gendered access to water remains both unclear and contradictory. This paper addresses this lacuna by examining the expansion of a rural water-supply network aimed at reducing household water scarcity in the arid zone of Rajasthan, India. Specifically, the Indira Gandhi Canal was conceived and constructed during the green revolution to ‘green the Thar Desert’. But now, through a complex network of reservoirs, treatment facilities, distribution centers, and supply pipelines, it connects much of rural and urban western Rajasthan to a drinking water-supply network. The paper examines the interaction of water-supply technologies, social power relations and dynamic socioecological change operating within these development processes. To do so it draws on household surveys, interviews with water users and government engineers, and participant observation with women and children water collectors. The paper finds that this ongoing water development project rendered the water provision landscape technical on the surface, but that uneven flows of water between villages and people reveal a more complex water provision landscape. The expansion of the network based on a technical reimagining of water supply has resulted in intervillage scarcity, intragender differential access, usurious private water markets, the abandonment and then the proposed rehabilitation of traditional water bodies, and urban water logging. In the conclusion I argue for a rethinking of water-supply development programs through a political ecology approach that focuses on the emergent capacities of water-supply technologies to redirect existing socioecological associations in unanticipated ways. Looking at the relationship between nature—society and technology may illuminate the possible ruptures in these associations and the ways that they may be rearticulated to produce less differentiating modes of accessing water.

二维裂隙密度及其渗透率的逾渗关系研究

在低渗透介质储存中，裂隙是石油运移的主要通道。因而裂隙介质的渗透率是石油勘探的一个重要参数。应用连续逾渗模型分析，把低渗透储存的裂隙网络合理简化，能够对深层复杂介质的渗透规律进行研究。使用排除体积对裂隙密度进行无量纲化，从而使裂隙的宏观性与裂隙的形状无关。使用Monte Carlo方法模拟得到不同裂隙密度时的裂隙网络图，然后基于连续逾渗模型，采用有限元方法利用COMSOL Multiphysics求解器解，数值模拟得到裂隙的渗透率与无量纲化密度成乘幂关系。该模拟结果可能为裂隙网络渗透率的定量评价提供一个简单而又实用的方法。这一规则的发现也增强了逾渗理论在油气田勘探中的应用。 Fracture networks strongly influence oil migration in reservoirs, especially in low-permeability reservoirs. So the parameter of fracture permeability plays an important role during the exploration of oil and gas fields. Simplifying the fracture network of reservoirs, and continuum percolation theory are successfully applied for studying the fracture permeability in deep and complex media. By using excluded area to dimensionless the fracture density, macroscopic properties of fracture networks become independent of fracture shape. Using Monte Carlo simulation, the facture net-work with different fracture density is obtained, and then based on continuum percolation theory and finite element analysis software, called COMSOL Multiphysics, we get the relationship between dimensionless density and macro-scopic permeability, which is scaling law. The simulated results may offer a simple and practical method to evaluate frac-ture permeability quantitatively and enhance the applications of percolation theory in the exploration of oil and gas fields.

Impact of dense reservoir networks on water resources in semiarid environments

Impact of dense reservoir networks on water resources in semiarid environments

Impact of Dense Reservoir Networks on Water Resources in Semiarid Environments

The northeast of Brazil is a semiarid region where water scarcity is a major problem dealt with by the construction of dams. This policy generated a dense reservoir network in the region, resulting in a complex system. The impacts of the network have been assessed, and the results showed that the existence of a large number of small dams upstream the strategic ones, impact both negatively and positively the overall water availability. The negative effects of the network are mainly high evaporation losses from small reservoirs, and the fact that they add considerable complexity to the management of the system. On the other hand, the reservoirs generate a more democratic water distribution and higher energy rationality, as a consequence of the better spatial distribution of the water resources. In addition, sediment retention in the network leads to lower silting rate of strategic reservoirs, meaning lower temporal decay in water availability in the already water-scarce region.

Coordinating multiple model predictive controllers for the management of large-scale water systems

The optimal management of multi-purpose water reservoir networks is a challenging control problem, because of the simultaneous presence of multiple objectives, the uncertainties associated with the inflow processes and the several interactions between the subsystems. For such systems, model predictive control (MPC) is an attractive control strategy that can be implemented in both centralized and decentralized configurations. The latter is easy to implement and is characterized by reduced computational requirements, but its performance is sub-optimum. However, individual decentralized controllers can be coordinated and driven towards the performance of a centralized configuration. Coordination can be achieved through the communication of information between the subsystems, and the modification of the local control problems to ensure cooperation between the controllers. In this work the applicability of coordination algorithms for the operation of water reservoir networks is evaluated. The performance of the algorithms is evaluated through numerical simulation experiments on a quadruple tank system and a two reservoir water network. The analysis also includes a numerical study of the trade-off between the algorithms' computational burden and the different levels of cooperation. The results show the potential of the proposed approach, which could provide a viable alternative to traditional control methods in real-world applications.

The effect of reservoir operational features on recycled wastewater quality

Israel's wastewater recycling program is unprecedented internationally, with close to 80% of sewage treated and reused in agriculture. This article presents findings from a national survey of Israel's wastewater reservoir network. Israel's extensive reuse of wastewater is possible due to a national network of reservoirs that has been established in recent years. Sixty effluent reservoirs presently operating in all parts of Israel, were reviewed and evaluated for static data and sampled for water quality analysis during the intensive irrigation season of 2008. The measurement of 16 chemical, physical and biological parameters, along with 21 major and trace elements was carried out, revealing a wide range of concentrations that reflect reservoir physical and operational characteristics. Results indicate that 65% and 78% of the supplied wastewater volume met the old 20mg/L BOD and 30mg/L TSS regulations for unlimited irrigation. With reference to Israel's updated, more stringent, regulations, only 22% and 28% of the supplied wastewater volume met the new TSS and E.C. regulations for unlimited irrigation, respectively, and only 48%, 58%, 58% and 62% met the new BOD, fecal coliforms, chloride and sodium regulations for unlimited new irrigation, respectively. All measured major and trace elements were well under existing standards, although there were considerable regional variation. Both principal component analysis and redundancy analysis were used to examine how the wastewater quality relates to multiple static construction features and operational parameters of the studied reservoirs. Sewage treatment plant technology, along with the reservoir operational regime, were found to have the greatest influence on the organic, nutrient and suspended solids loads of the wastewater supplied. The geographic location of the reservoirs along with their designed area to volume ratio, were found to have the largest influence over salinity components loads. Careful consideration should be given to the aforementioned features in future the reservoir planning process.

Overspill Avalanching in a Dense Reservoir Network

Sustainability of communities, agriculture, and industry is strongly dependent on an effective storage and supply of water resources. In some regions the economic growth has led to a level of water demand which can only be accomplished through efficient reservoir networks. Such infrastructures are not always planned at larger scale but rather made by farmers according to their local needs of irrigation during droughts. Based on extensive data from the upper Jaguaribe basin, one of the world's largest system of reservoirs, located in the Brazilian semiarid north-east, we reveal that surprisingly it self-organizes into a scale-free network exhibiting also a power-law in the distribution of the lakes and avalanches of discharges. With a new self-organized-criticality-type model we manage to explain the novel critical exponents. Implementing a flow model we are able to reproduce the measured overspill evolution providing a tool for catastrophe mitigation and future planning.

Overspill avalanching in a dense reservoir network

Sustainability of communities, agriculture, and industry is strongly dependent on an effective storage and supply of water resources. In some regions the economic growth has led to a level of water demand that can only be accomplished through efficient reservoir networks. Such infrastructures are not always planned at larger scale but rather made by farmers according to their local needs of irrigation during droughts. Based on extensive data from the upper Jaguaribe basin, one of the world's largest system of reservoirs, located in the Brazilian semiarid northeast, we reveal that surprisingly it self-organizes into a scale-free network exhibiting also a power-law in the distribution of the lakes and avalanches of discharges. With a new self-organized-criticality-type model we manage to explain the novel critical exponents. Implementing a flow model we are able to reproduce the measured overspill evolution providing a tool for catastrophe mitigation and future planning.

Regularization and stability in reservoir networks with output feedback

Output feedback is crucial for autonomous and parameterized pattern generation with reservoir networks. Read-out learning affects the output feedback loop and can lead to error amplification. Regularization is therefore important for both generalization and reduction of error amplification. We show that regularization of the reservoir and the read-out layer reduces the risk of error amplification, mitigates parameter dependency and boosts the task-specific performance of reservoir networks with output feedback. We discuss the deeper connection between regularization of the learning process and stability of the trained network.

Regularization by Intrinsic Plasticity and Its Synergies with Recurrence for Random Projection Methods

Neural networks based on high-dimensional random feature generation have become popular under the notions extreme learning machine (ELM) and reservoir computing (RC). We provide an in-depth analysis of such networks with respect to feature selection, model complexity, and regularization. Starting from an ELM, we show how recurrent connections increase the effective complexity leading to reservoir networks. On the contrary, intrinsic plasticity (IP), a biologically inspired, unsupervised learning rule, acts as a task-specific feature regularizer, which tunes the effective model complexity. Combing both mechanisms in the framework of static reservoir computing, we achieve an excellent balance of feature complexity and regularization, which provides an impressive robustness to other model selection parameters like network size, initialization ranges, or the regularization parameter of the output learning. We demonstrate the advantages on several synthetic data as well as on benchmark tasks from the UCI repository providing practical insights how to use high-dimensional random networks for data processing.

Multiple mechanisms generate Lorentzian and 1/fα power spectra in daily stream-flow time series

Power-law scaling is an ubiquitous feature of the power spectrum of streamflow on the daily to monthly timescales where the spectrum is most strongly affected by hydrologic catchment-scale processes. Numerous mechanistic explanations for the emergence of this power-law scaling have been proposed. This study employs empirical spectra obtained for eight river basins in the South Eastern US and synthetic spectra generated from a range of proposed mechanisms to explore these explanations. The empirical analysis suggested that streamflow spectra were characterized by multiple power-law scaling regimes with high-frequency exponents α in the range −1 to −5. In the studied basins, α tended to increase with drainage area. The power-law generating mechanisms analyzed included linear and nonlinear catchment water balance arguments, power-law recession behavior, autonomous and non-autonomous responses of channel hydraulics and the n-fold convolution of linear reservoirs underpinning Dooge or Nash hydrographs. Of these mechanisms, only n-fold convolutions with n=2 or 3 generated power spectra with features that were consistent with the empirical cases. If the effects of daily streamflow sampling on truncating power spectra were considered, then the trends in α with drainage area were also consistent with this mechanism. Generalizing the linear convolution approach to a network of reservoirs with randomly distributed parameters preserved the features of the power spectrum and maintained consistency with empirical spectra.

Domain decomposition and balanced truncation model reduction for shape optimization of the Stokes system

The optimal design of structures and systems described by partial differential equations (PDEs) often gives rise to large-scale optimization problems, in particular if the underlying system of PDEs represents a multi-scale, multi-physics problem. Therefore, reduced-order modelling techniques such as balanced truncation model reduction (BTMR), proper orthogonal decomposition, or reduced basis methods are used to significantly decrease the computational complexity while maintaining the desired accuracy of the approximation. In this paper, we are interested in such shape optimization problems where the design issue is restricted to a relatively small portion of the computational domain. In this case, it appears to be natural to rely on a full-order model only in that specific part of the domain and to use a reduced-order model elsewhere. A convenient methodology to realize this idea consists of a suitable combination of domain decomposition techniques and BTMR. We will consider such an approach for shape optimization problems associated with the time-dependent Stokes system and derive explicit error bounds for the modelling error. As an application in life sciences, we will be concerned with the optimal design of capillary barriers as part of a network of microchannels and reservoirs on microfluidic biochips that are used in clinical diagnostics, pharmacology, and forensics for high-throughput screening and hybridization in genomics and protein profiling in proteomics.