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 o...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.展开更多
Determination of dry bulk density and water content measurement of magnetic susceptibility (x) and saturation isothermal remanent magnetization (SIRM), determination of carbonate content, and determination of total o...Determination of dry bulk density and water content measurement of magnetic susceptibility (x) and saturation isothermal remanent magnetization (SIRM), determination of carbonate content, and determination of total organic carbon (TOC) content nitrogen content (N%) and carbon/nitrogen (C/N) ratio are some of the techniques which have been widely applied to lacustrine-sediment analyses. The techniques,complemented by others, are usually useful for revealing characteristics of lacustrine-sediments and thus for postulating hydrological regimes in the lake and environmental conditions and human activity around it in palaeolimnological studies. A very brief review is presented on recent applications of these techniques in palaeolimnological work with English literatures published mainly since 1985 and focus given on interpretations of results of these analyses related to palaeoenvironmental reconstructions. Low dry bulk density and high water content often imply relatively warm and wet conditions. High X and SIRM are usually resulted from reduced dilutions in the lake and intensified erosions on its catchment. both of which can be in turn attributed to environmental changes. While variations in patterns of X and SIRM may give further insight on mineral magnetism and thus implications on environmental conditions. Increased carbonate content seems likely to associate to warm and dry conditions.Increased TOC content is virtually used as one of indicators of warm and wet conditions and variations in C/N ratio may hint variations in relative contributions of different sources, aquatic and terrestrial, to the total organic matter in lake sediments and hence to lake-level fluctuations and climate changes.展开更多
基金This project was funded by the Open Fund of the Key Laboratory of Exploration Technologies for Oil and Gas Resources,the Ministry of Education(No.K2021-03)National Natural Science Foundation of China(No.42106213)+2 种基金the Hainan Provincial Natural Science Foundation of China(No.421QN281)the China Postdoctoral Science Foundation(Nos.2021M690161 and 2021T140691)the Postdoctorate Funded Project in Hainan Province.
文摘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.
文摘Determination of dry bulk density and water content measurement of magnetic susceptibility (x) and saturation isothermal remanent magnetization (SIRM), determination of carbonate content, and determination of total organic carbon (TOC) content nitrogen content (N%) and carbon/nitrogen (C/N) ratio are some of the techniques which have been widely applied to lacustrine-sediment analyses. The techniques,complemented by others, are usually useful for revealing characteristics of lacustrine-sediments and thus for postulating hydrological regimes in the lake and environmental conditions and human activity around it in palaeolimnological studies. A very brief review is presented on recent applications of these techniques in palaeolimnological work with English literatures published mainly since 1985 and focus given on interpretations of results of these analyses related to palaeoenvironmental reconstructions. Low dry bulk density and high water content often imply relatively warm and wet conditions. High X and SIRM are usually resulted from reduced dilutions in the lake and intensified erosions on its catchment. both of which can be in turn attributed to environmental changes. While variations in patterns of X and SIRM may give further insight on mineral magnetism and thus implications on environmental conditions. Increased carbonate content seems likely to associate to warm and dry conditions.Increased TOC content is virtually used as one of indicators of warm and wet conditions and variations in C/N ratio may hint variations in relative contributions of different sources, aquatic and terrestrial, to the total organic matter in lake sediments and hence to lake-level fluctuations and climate changes.
