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
Global warming has become an increasing concern, and using soil as a carbon sink to sequester carbon dioxide has attracted much attention in recent years. In this study, soil organic carbon (SOC) content and organic c...Global warming has become an increasing concern, and using soil as a carbon sink to sequester carbon dioxide has attracted much attention in recent years. In this study, soil organic carbon (SOC) content and organic carbon density were estimated based on a soil survey of a small landscape in Dongguan, South China, with spatial heterogeneity of SOC distribution and the impacts of land-use patterns on soil organic carbon content assessed. Field sampling was carried out based on a 150 m×150 m grid system overlaid on the topographic map of the study area and samples were collected in three 20-cm layers to a depth of 60 cm. Spatial variability in the distribution of SOC was assessed using the Kruskal-Wallis test. Results showed that SOC in the topsoil layer (0-20 cm) was not much higher or even lower in some sites than the underlying layers, and except for the two sites covered with natural woodland, it did not exhibit a pronounced vertical gradient. The difference in both horizontal and vertical distribution of SOC was not statistically significant. However, in the topsoil layer among land-use/land-cover patterns, significant differences (P≤0.05) in SOC distribution existed, indicating that management practices had great impact on SOC content. SOC storage in the study area to a depth of 20, 40, and 60 cm was estimated as 2.13×106 kg, 3.46×106 kg, and 4.61×106 kg, respectively.展开更多
The source rock model used in this project was developed by French Petroleum Research Institute. The total organic carbon content was estimated primarily and directly by using continuous conventional logging curves (s...The source rock model used in this project was developed by French Petroleum Research Institute. The total organic carbon content was estimated primarily and directly by using continuous conventional logging curves (such as sonic and resistivity curves), which are calibrated through the laboratory analysis data of organic carbon of cores, cuttings or sidewall cores. Regional evaluations have been carried out in downwarping basins abroad. The Haila′er Basin is a faulted basin and the evaluation of such a basin is a new subject. On the basis of a regional evaluation method for the downwarping basins, a new method suitable to faulted basins was developed. The effect is satisfactory when this new method is applied to the Wu′erxun Sag and the Bei′er Sag.展开更多
Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of ...Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.展开更多
The explosion at a plant of the Jilin Petrochemical Corporation on 13 November,2005,and the spill of an estimated 100 t of toxic substances (nitrobenzene as the main component) into the Songhua River received worldwid...The explosion at a plant of the Jilin Petrochemical Corporation on 13 November,2005,and the spill of an estimated 100 t of toxic substances (nitrobenzene as the main component) into the Songhua River received worldwide attention.This study has focused on the adsorption behavior of nitrobenzene that spilled onto sediments along the Songhua River,which was one of the efforts to evaluate the fate of nitrobenzene after the spillage event.The organic carbon contents of these sediments along the Songhua River var...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
There are about 5 million ha of strongly acid soils (pH < 4.8 in 0.01 mol·L -1 CaCl 2 ) in Victoria and about 11 million ha of mildly acid soils (pH 4.8～5.5) that are considered susceptible to furthe...There are about 5 million ha of strongly acid soils (pH < 4.8 in 0.01 mol·L -1 CaCl 2 ) in Victoria and about 11 million ha of mildly acid soils (pH 4.8～5.5) that are considered susceptible to further acidification under current agricultural use. However, there appear to be differences in the rate of acidification, as measured by soil pH change, between soils under perennial pastures in the higher rainfall areas of southern Victoria and soils under annual pastures in the sheep-wheat areas of the north-east. Measurements made on representative soils from both regions showed that the southern soils generally had a higher pH buffer capacity, which was primarily determined by the organic carbon content. There was a consistent relationship between the short-term buffer capacity (measured by titration) and the long-term buffer capacity (measured by incubation), irrespective of the origin of the soils. Exchangeable Al, measured in 0.01 mol·L -1 CaCl 2 , was strongly negatively correlated with pH and the relationship for all soils suggested that Al was adsorbed as a cation with an average charge of 1.2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.
基金Project supported by the Key Research Program of Guangdong Province (No. 2002C20703)the Key Research Program of the Forestry Administration of Guangdong Province (No. 2002-12).
文摘Global warming has become an increasing concern, and using soil as a carbon sink to sequester carbon dioxide has attracted much attention in recent years. In this study, soil organic carbon (SOC) content and organic carbon density were estimated based on a soil survey of a small landscape in Dongguan, South China, with spatial heterogeneity of SOC distribution and the impacts of land-use patterns on soil organic carbon content assessed. Field sampling was carried out based on a 150 m×150 m grid system overlaid on the topographic map of the study area and samples were collected in three 20-cm layers to a depth of 60 cm. Spatial variability in the distribution of SOC was assessed using the Kruskal-Wallis test. Results showed that SOC in the topsoil layer (0-20 cm) was not much higher or even lower in some sites than the underlying layers, and except for the two sites covered with natural woodland, it did not exhibit a pronounced vertical gradient. The difference in both horizontal and vertical distribution of SOC was not statistically significant. However, in the topsoil layer among land-use/land-cover patterns, significant differences (P≤0.05) in SOC distribution existed, indicating that management practices had great impact on SOC content. SOC storage in the study area to a depth of 20, 40, and 60 cm was estimated as 2.13×106 kg, 3.46×106 kg, and 4.61×106 kg, respectively.
文摘The source rock model used in this project was developed by French Petroleum Research Institute. The total organic carbon content was estimated primarily and directly by using continuous conventional logging curves (such as sonic and resistivity curves), which are calibrated through the laboratory analysis data of organic carbon of cores, cuttings or sidewall cores. Regional evaluations have been carried out in downwarping basins abroad. The Haila′er Basin is a faulted basin and the evaluation of such a basin is a new subject. On the basis of a regional evaluation method for the downwarping basins, a new method suitable to faulted basins was developed. The effect is satisfactory when this new method is applied to the Wu′erxun Sag and the Bei′er Sag.
基金Under the auspices of National High-tech R&D Program of China(No.2013AA102301)National Natural Science Foundation of China(No.71503148)
文摘Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.
文摘The explosion at a plant of the Jilin Petrochemical Corporation on 13 November,2005,and the spill of an estimated 100 t of toxic substances (nitrobenzene as the main component) into the Songhua River received worldwide attention.This study has focused on the adsorption behavior of nitrobenzene that spilled onto sediments along the Songhua River,which was one of the efforts to evaluate the fate of nitrobenzene after the spillage event.The organic carbon contents of these sediments along the Songhua River var...
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.
文摘There are about 5 million ha of strongly acid soils (pH < 4.8 in 0.01 mol·L -1 CaCl 2 ) in Victoria and about 11 million ha of mildly acid soils (pH 4.8～5.5) that are considered susceptible to further acidification under current agricultural use. However, there appear to be differences in the rate of acidification, as measured by soil pH change, between soils under perennial pastures in the higher rainfall areas of southern Victoria and soils under annual pastures in the sheep-wheat areas of the north-east. Measurements made on representative soils from both regions showed that the southern soils generally had a higher pH buffer capacity, which was primarily determined by the organic carbon content. There was a consistent relationship between the short-term buffer capacity (measured by titration) and the long-term buffer capacity (measured by incubation), irrespective of the origin of the soils. Exchangeable Al, measured in 0.01 mol·L -1 CaCl 2 , was strongly negatively correlated with pH and the relationship for all soils suggested that Al was adsorbed as a cation with an average charge of 1.2
