Detection of reservoir fractures in imaging logs using directional filtering
Every rock, which has pores connected together, can be considered as a reservoir rock. Existing fractures in the reservoir rock are the most effective factors, which increase the production and harvesting of the well. Detection of reservoir rock fractures is a very complicated process, due to factors such as poor contrast, fracture variable thickness, noise, discontinuities in the curve of a fracture, and the existence of some phenomenon like fracture bedding. In the well-log images, some sinusoidal curves are fractures. The directions of fractures are shown as some special curves. According to this concept, in this paper, fractures are extracted based on an optimized algorithm using directional filters. The first order derivative of the imaging log is calculated for each pixel. Then, the direction filters are designed based on a different direction of each pixel, and applied to the original imaging log. Finally, using the Hough transform, some parameters of each fracture are obtained, and are displayed on a real imaging log.
A 340-GHz thin film polarisation converter
ABSTRACT This paper introduces a 340-GHz polarisation converter. The polarisation converter consists of a bilayer metamaterial, which is processed by the flexible thin film lithography method. Two-dimensional metamaterial offer such advantages over traditional quarter-wave-plates as compactness, low profile and simple fabrication process, but the insertion loss is comparably high. In order to lower the insertion loss, the 10-μm ultrathin polyimide substrate and a bilayer structure separated by a quarter wavelength air impedance match layer are adopted. After the horn antenna’s far-field test by vector network analyser and the Gaussian beam test based on Terahertz time-domain spectroscopy (THz-TDS), the results show that in the band of 325–350 GHz, the transmission loss is less than 2 dB, and the axial ratio is less than 3 dB; besides, simulation and test insertion loss performance difference is less than 1 dB, which indicates that the method we adopt is effective. In addition, based on transmission line theory, we analyse its working principle and the influence from the fabrication error.
Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification
A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.