Reservoir Computing is a framework for computation like a recurrent neural network that allows for the black box modeling of dynamical systems. In contrast to other recurrent neural network approaches, Reservoir Computing does not train the input and internal weights of the network, only the readout is trained. However it is necessary to adjust parameters to create a ”good” reservoir for a give...

avo as a known methodology is used to identify fluid type and reservoir lithology in subsurface exploration. method discussed in this paper, consists of three stages, including: direct modeling, inverse modeling and cross plot interpretation. by direct modeling we can clarify lithology or fluid dependent attributes. analysis performed using both p-p and p-sv attributes. inverse modeling deals w...

A survey of new RNN training methods that follow the Reservoir paradigm Summary Echo State Networks (ESNs) and Liquid State Machines (LSMs) introduced a simple new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, made RNNs accessible for practical...

Watershed nutrient management is essential in minimizing the eutrophication rate of reservoirs. A shortcoming in nutrient management of watershed-reservoir systems has been the lack of successful linking of watershed and water body models to evaluate impact of watershed management options on reservoir water quality. The objectives of this research were to 1) calibrate and validate a Soil and Wa...

In developing TMDL waste-load allocations for the Snake River-Reservoir system in Western Idaho and Eastern Oregon, determinations of the assimilative capacity of the system and the impact of pollutant reduction strategies has been performed using both a statistical-correlation approach and a mechanistic modeling approach. The system included the Lower Snake River, Brownlee Reservoir, Oxbow Res...

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) ...