Sediment Distribution In Reservoirs Research Articles

Empirical approaches in prediction of reservoir sediment distribution—An experience of 57 reservoirs in the USA and India

An accurate prediction of sediment distribution may minimize economic losses through proper and timely planning of the functional activities of a reservoir. This study assesses different temporal and spatial factors that affect for sediment deposition in a reservoir and its distribution. This study also focuses on evaluation of two popular distribution prediction methodologies, Area Increment and Empirical Area Reduction, based on experience with sediment distribution in 57 reservoirs in the USA and India. A non-iterative processed empirical distribution model (NPEDM) and a linear regression trend model (LRTM) are proposed to predict sediment distribution. Silt contributing area and inflow entering a reservoir are found to be the most significant factors affecting in reservoir sediment deposition. Compared to the Empirical Area Reduction method, the Area Increment method provided better prediction. The reservoir classification approach and empirical design distribution type curves given by Borland and Miller (1960) are found to be rational. Shape factor values for different periods indicate that reservoir shape (type) changes with time. Thus, long term prediction is not desirable in Type-II & III reservoirs using the Empirical Area Reduction method. Newly developed the NPEDM shows reasonably good prediction of sediment distribution. The NPEDM is very easy to apply and can be used in any reservoir of any size. Extrapolation of the trend of sediment distribution obtained from the LRTM indicates an accurate short term prediction in a few reservoirs as causes of temporal and spatial variations of sediment distribution including the factors of uncertainties of sediment deposition are implicit within the methodology.

Calibration the area-reduction method in sediment distribution of Ekbatan reservoir dam using genetic algorithms

Dam reservoirs usually play the most important role in the water resources systems and their optimal utilization in economic and social terms is indispensable. Sedimentation in dam’s reservoirs is one of the destructive phenomena which leads to reduction of useful volume of reservoirs and also damages the installations and disturbs their functions. Area reduction method is the most common experimental method to measure the sediment distribution in reservoirs. In this method, reservoirs are geometrically divided into four types. Parameters obtained for each type are based on limited number of chosen reservoirs and consequently the results lead to large scale errors for accuracy of this method. Therefore choosing appropriate parameters can help us to have more acceptable accuracy. In this study, first based on area reduction method a model was made by using MATLAB software and optimized by GA. Error declined by 46.7 %. Then elevation–area–capacity curves for following years were predicted by best coefficients.

New proposed method for prediction of reservoir sedimentation distribution

Prediction of sediment distribution in reservoirs is an important issue for dam designers to determine the reservoir active storage capacity. Methods proposed to calculate sediment distribution are varied, and mainly empirical. Among all the methods currently available, only area-reduction and area-increment methods are considered as the principal methods for prediction of sediment distribution. In this paper, data of 16 reservoirs in the United States are used to propose a new empirical method for prediction of sediment distribution in reservoirs. In the proposed method, reservoir sediment distribution is related to sediment volume and original reservoir characteristics. To validate the accuracy of the new proposed method, obtained results are compared with survey data for two reservoirs. The results of this investigation showed that the proposed method has an acceptable accuracy.

Pinpointing the key parameters in estimating the bed-load sediment flux in reservoir engineering

When a dam is constructed on a river to store water, sediments transported by the water flow are also stored and reservoir capacity is gradually reduced by sediment accumulation. Prediction of sediment distribution in reservoirs is an important issue for dam designers to determine the reservoir active storage capacity, outlet sill elevation, dam stability, recreational facilities, and backwater conditions. The main objective of this study is to develop the most reliable parameters of sedimentation that are directly or indirectly influencing in the equations and measured dataset. For validation of the proposed parameters, data of 40 reservoir sets gathered from different reliable sources, rather than focusing entirely on bed-load equations. Artificial neural network (ANNs) method was used to validate this study. Several graphs and statistical analysis were presented to emphasize the influencing effect of those parameters that were detected by ANNs and are directly controlling the error in the bed-load sediment flux using measured particles datasets.

New Empirical Method for Prediction of Sediment Distribution in Reservoirs

When a dam is constructed on a river to store water, sediments transported by the water flow are also stored and reservoir capacity is gradually reduced by sediment accumulation. Prediction of sediment distribution in reservoirs is an important issue for dam designers to determine the reservoir active storage capacity, outlet sill elevation, dam stability, recreational facilities, and backwater conditions. In this study, original and secondary area-capacity data of 40 reservoirs in the United States are used and a new empirical method is proposed for the prediction of sediment distribution in reservoirs. In the proposed method, reservoir sediment distribution is related to the sediment volume and original reservoir characteristics. To demonstrate the proposed method, two examples are presented.

Derivation of Reservoir’s Area-Capacity Equations

Area-capacity curves are usually used for reservoir flood routing, reservoir operation, determination of water surface area, and capacity corresponding to each elevation, reservoir classification, and reservoir sediment distribution. In this study, the similarity between the natural logarithmic function curve and the reservoir capacity curve is used to obtain the mathematical equation for dimensionless capacity curve. The obtained equation has only one unknown dimensionless parameter, named the “reservoir coefficient” N . If the obtained dimensionless capacity equation is plotted on a log-log paper, for any values of N , it will be approximately a straight line and the reciprocal of the line slope will be equal to the reservoir shape factor. The reservoir area equation and equation of reservoir coefficient are obtained by differentiating the reservoir capacity equation. Also, more precise values of N for the area and capacity equations can be obtained by using the least-squares method. The obtained equati...

A THIN SKINNED MODEL FOR THE PAPUAN THRUST BELT AND SOME IMPLICATIONS FOR HYDROCARBON EXPLORATION

In the Papuan thrust belt the main risk in hydrocarbon exploration lies in identifying structural traps which are detached below the level of the primary reservoir. Because of the difficulty in obtaining usable seismic in the remote, inaccessible terrain, identification of prospects has been based on the interpretation of structural cross-sections drawn from geological maps. The structural models commonly used have been geometrically inadmissible, and as a result, some wells have penetrated thrusts at depth, before reaching the reservoir horizon.More rigorously constrained regional cross sections through the thrust belt may be constructed using the principles of thin skinned tectonics. These may be used to identify structural provinces, characterised by different types of thrust pattern. In turn, the probability of reservoir involvement in a particular structure is directly related to its location in a particular province.The cross-sections show that there has been as much as 100 km of shortening in the hinterland of the thrust belt. Restored versions of the cross sections may be used to reconstruct the shape of the basin and ideally to identify regions favourable for source and reservoir sediment distribution. In Papua New Guinea, palinspastic maps are currently feasible only for Miocene strata, but with the present level of exploration activity increased stratigraphic information should soon permit reconstructions for older formations.

Forecasting sediment distribution in reservoirs during unstable conditions

A method for rapid estimation of slopes of deposited sediment in reservoirs during unstable conditions is presented. As the limiting factor determining sediment load in a reservoir is the sediment carrying capacity of a current flowing through a reservoir, it is arqued that the slope of deposited sediment must be a function of the latter. After deriving an equation relating sediment slope to stream power, the latter being regarded as representative of sediment carrying capacity, a comparison between calculated and observed sediment slopes for average annual conditions in two South African reservoirs are presented. Using this equation as basic guideline, a semi-empirical graph is then presented which relates sediment slope to a factor representing average stream power in reservoirs.

On the Empirical Prediction of Sediment Distribution in Reservoirs

On the Empirical Prediction of Sediment Distribution in Reservoirs

Methods of Predicting Sediment Distribution in Reservoirs

Methods of Predicting Sediment Distribution in Reservoirs