SUMMARYIn mesh networks architecture, it should be permitted to visit the mobile client points. Whereas in mesh networks environment, the main throughput flows usually communicate with the conventional wired network. The so-called gateway nodes can link directly to traditional Ethernet, depending on these mesh nodes, and can obtain access to data sources that are related to the Ethernet. In wireless mesh networks (WMNs), the quantities of gateways are limited. The packet-processing ability of settled wireless nodes is limited. Consequently, throughput loads of mesh nodes highly affect the network performance. In this paper, we propose a queuing system that relied on traffic model for WMNs. On the basis of the intelligent adaptivenes, the model considers the influences of interference. Using this intelligent model, service stations with boundless capacity are defined as between gateway and common nodes based on the largest hop count from the gateways, whereas the other nodes are modeled as service stations with certain capacity. Afterwards, we analyze the network throughput, mean packet loss ratio, and packet delay on each hop node with the adaptive model proposed. Simulations show that the intelligent and adaptive model presented is precise in modeling the features of traffic loads in WMNs.
Frac‐packing is an attractive technique to stimulate production for gas hydrate reservoirs with the additional benefit of solving the problematic sand production problems. So far, there have been no documented mathematical models to predict the propagation of fractures and to forecast gas production for frac‐packed gas wells. An analytical model was derived to predict the propagation of a horizontal fracture and to assess the well productivity in frac‐packed gas wells in a gas hydrate reservoir. The model assumes a steady, single‐gas, Darcy flow in the matrix and fracture. Case analyses were performed for key design and operational parameters with the analytical model. The result shows that it is easy to control the relationship between the wellbore fracturing pressure and injecting flow rate, and thus, fractures of any length can be produced in the fracture penetration process of frac‐packed wells. Case analysis also shows that the gas production rate increases nonlinearly with the fracture propagation and increases linearly with the fracture width. The increase in fracture width turns out to be surprisingly effective in improving well productivity without threshold within the investigated range of width. It was also found that the increase in fracture permeability contributes more to the productivity of the frac‐packed wells than the increase in matrix permeability. The model also assumes no flow at the boundary of the reservoir, which may underestimate those gas hydrate reservoirs with pressure supply. This work uses a theoretical approach to estimate the productivity of the frac‐packed gas hydrate reservoirs, which may benefit in solving the sand production issue during the production process as well.
Computer tomography technology is widely used in geological exploration because it is a nondestructive and three-dimensional imaging method that can be integrated with computer simulation. However, the large-scale application of the computer tomography technique is limited by economic costs and time consumption. Therefore, it is challenging and intractable to indicate the pore structure characteristics of rock. To address this issue, a super-resolution reconstruction algorithm based on convolutional neural networks, residual learning, and attention mechanism was proposed to generate super-resolution images in this study. This algorithm was applied to the reconstruction of carbonate rock and sandstone. The performance of two-dimensional image reconstruction was evaluated by quantitative extraction and qualitative visualization. The results from experiments indicate that the built model performs well on different upscaling factors and is superior to the existing super-resolution approaches based on convolutional neural network.
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