Research on Identification Method of Argillaceous Interlayer in Compound Sand

For the compound sand body, the interlayer is an important factor affecting the adjustment of oil production structure and remaining oil distribution. According to the origin of argillaceous interlayer, the interlayer is divided into three types, including barriers between two single layers, intercalations between two single sands and intercalations in a single sand. In this study, the upper limit of physical properties of interlayer was obtained by analyzing the relationship between physical parameters and production index per-meter. The discriminant index and comprehensive discriminant chart of interlayer were obtained by grey correlation method, which realize the quantitative identification of different types of interlayer. The intercalations between two single sands in the target area are distributed almost in the whole area, which is one of the most important factors influencing the mining effect of compound sand, so the planar distribution is mainly aimed at it. Firstly, through cross-well comparison, we summarize three interlayer patterns, then establish their forward modeling, so as to obtain the vertical seismic characteristics of different patterns. Secondly, according to the thickness of intercalations between two single sands, we take the top of bottom sand as the baseline, extract the average amplitude attribute from the upper and lower 3 ms, then, according to the seismic section and planar characteristics of the well, the interlayer structures represented by different seismic section and planar characteristics are summarized. Finally, starting from the real drilling interlayer of the well, the planar spread of interlay can be obtained according to their variation trend and distribution.

Spatial evolution of the near-field distribution on planar gold nanoparticles with the excitation wavelength across dipole and quadrupole modes

We investigate the superposition properties of the dipole and quadrupole plasmon modes in the near field both experimentally, by using photoemission electron microscopy(PEEM), and theoretically. In particular, the asymmetric near-field distributions on gold(Au) nanodisks and nanoblocks under oblique incidence with different polarizations are investigated in detail. The results of PEEM measurements show that the evolutions of the asymmetric near-field distributions are different between the excitation with s-polarized and p-polarized light.The experimental results can be reproduced very well by numerical simulations and interpreted as the superposition of the dipole and quadrupole modes with the help of analytic calculations. Moreover, we hypothesize that the electrons collected by PEEM are mainly from the plasmonic hot spots located at the plane in the interface between the Au particles and the substrate in the PEEM experiments.

Use of artificial neural networks to identify and analyze polymerized actin-based cytoskeletal structures in 3D confocal images

Background:Living cells need to undergo subtle shape adaptations in response to the topography of their substrates.These shape changes are mainly determined by reorganization of their internal cytoskeleton,with a major contribution from filamentous(F)actin.Bundles of F-actin play a major role in determining cell shape and their interaction with substrates,either as“stress fibers,”or as our newly discovered“Concave Actin Bundles”(CABs),which mainly occur while endothelial cells wrap micro-fibers in culture.Methods:To better understand the morphology and functions of these CABs,it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles,which is a demanding and time-consuming task.In this study,we present a novel algorithm to automatically recognize CABs without further human intervention.We developed and employed a multilayer perceptron artificial neural network(“the recognizer”),which was trained to identify CABs.Results:The recognizer demonstrated high overall recognition rate and reliability in both randomized training,and in subsequent testing experiments.Conclusion:It would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.