Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

Modeling, analysis, and screening of cyclic pressure pulsing with nitrogen in hydraulically fractured wells

Cyclic pressure pulsing with nitrogen is studied for hydraulically fractured wells in depleted reservoirs.A compositional simulation model is constructed to represent the hydraulic fractures through local-grid refinement.The process is analyzed from both operational and reservoir/hydraulic-fracture perspectives.Key sensitivity parameters for the operational component are chosen as the injection rate,lengths of injection and soaking periods and the economic rate limit to shut-in the well.For the reservoir/hydraulic fracturing components,reservoir permeability,hydraulic fracture permeability,effective thickness and half-length are used.These parameters are varied at five levels.A full-factorial experimental design is utilized to run 1250 cases.The study shows that within the ranges studied,the gas-injection process is applied successfully for a 20-year project period with net present values based on the incremental recoveries greater than zero.It is observed that the cycle rate limit,injection and soaking periods must be optimized to maximize the efficiency.The simulation results are used to develop a neural network based proxy model that can be used as a screening tool for the process.The proxy model is validated with blind-cases with a correlation coefficient of 0.96.

Memristive cyclic three-neuron-based neural network with chaos and global coexisting attractors

It has been documented that a cyclic three-neuron-based neural network with resistive synaptic weights cannot exhibit chaos.Towards this end,a memristive cyclic three-neuron-based neural network is presented using a memristive weight to substitute a resistive weight.The memristive cyclic neural network always has five equilibrium points within the parameters of interest,and their stability analysis shows that they are one index-2 saddle-focus,two index-1 saddle-foci,and two stable node-foci,respectively.Dynamical analyses are performed for the memristive cyclic neural network by several numerical simulation methods.The results demonstrate that the memristor synapse-based neural network with the simplest cyclic connection can not only exhibit chaos,but also present global coexisting attractors composed of stable points and unstable periodic or chaotic orbits under different initial conditions.Besides,with the designed implementation circuit,Multisim circuit simulations and hardware experiments are executed to validate the numerical simulations.