Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

利用红外相机技术对安徽省鹞落坪国家级自然保护区大中型兽类及林下鸟类的调查

鹞落坪国家级自然保护区位于大别山脉南麓的核心地带。2014–2017年,作者采用红外相机技术对鹞落坪国家级自然保护区的大中型兽类及林下鸟类进行调查。研究共布设了72个相机位点,累计完成16,658个相机工作日,获得独立有效照片2,142张,共记录野生兽类9种、鸟类15种,隶属8目15科。包括国家一级重点保护野生动物1种,即安徽麝(Moschusanhuiensis),国家二级重点保护野生动物2种,分别是勺鸡(Pucrasiamacrolopha)和白冠长尾雉(Syrmaticusreevesii)。相对多度排名前五的兽类分别为小麂(Muntiacusreevesi)、野猪(Susscrofa)、赤腹松鼠(Callosciuruserythraeus)、猪獾(Arctonyxcollaris)和岩松鼠(Sciurotamiasdavidianus)。相对多度排名前五的鸟类为白冠长尾雉、勺鸡、松鸦(Garrulus glandarius)、灰背鸫(Turdus hortulorum)、红嘴蓝鹊(Urocissa erythroryncha)。此外红外相机还拍摄到大量人类活动照片,表明当地人类活动较为严重,应加强管理。本研究初步了解了保护区内大中型兽类和林下鸟类群落信息及人类活动的干扰情况,为保护区未来的保护和管理工作提供了数据基础。

Three-dimensional numerical simulation of flow and splash behavior in an oxygen coal combustion melting and separating furnace

The change of bubbles and the position of the tuyere in an oxygen coal combustion melting and separating furnace affect the flow and splash behavior of the molten pool.To analyze this problem further,a three-dimensional numerical simulation method was used to explore the behavior and change of the flow field inside the molten pool during double-row tuyere injection.In addition,the arrangement of the tuyere was changed for a more detailed understanding of the internal phase distribution and splashing in a molten pool.The results indicated that under three-dimensional numerical simulation conditions,bubbles rise after leaving the tuyere and break on the surface of the molten pool,which results in certain fluctuations in the nearby melt.During the injection process of the tuyere,the meteorological accumulation in the middle part of the molten pool formed part of the foam slag because of the influence of surface tension.When the layout of the upper and lower exhaust tuyeres was changed from staggered to symmetrical,or when the spacing of the upper and lower exhaust tuyeres changed,it had an effect on the phase distribution and splash behavior.