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
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
有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了...有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了一种基于PCA-BP(Principal Component Analysis and Back Propagation)神经网络的有机碳含量智能预测方法。该方法以敏感测井曲线的加权平均值和TOC测试结果为原始数据集,首先利用方差膨胀因子检测测井曲线之间共线性,然后采用主成分分析PCA(Principal Component Analysis)技术对原始数据集进行去共线性和降维处理,确定出2个主成分,最后结合中子、自然伽马、密度、声波时差曲线值,建立出6个输入节点的3层BP(Back Propagation)神经网络预测模型,对研究区三叠系白碱滩组烃源岩潜力进行精细评价。3口取心井累积410m井段的预测结果表明,模型的决定系数高达0.879,预测结果平均绝对误差和均方误差分别为0.220和0.107,平均相对误差为16.1%。研究结果为准噶尔盆地勘探领域优选提供了可靠参考。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.
文摘有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了一种基于PCA-BP(Principal Component Analysis and Back Propagation)神经网络的有机碳含量智能预测方法。该方法以敏感测井曲线的加权平均值和TOC测试结果为原始数据集,首先利用方差膨胀因子检测测井曲线之间共线性,然后采用主成分分析PCA(Principal Component Analysis)技术对原始数据集进行去共线性和降维处理,确定出2个主成分,最后结合中子、自然伽马、密度、声波时差曲线值,建立出6个输入节点的3层BP(Back Propagation)神经网络预测模型,对研究区三叠系白碱滩组烃源岩潜力进行精细评价。3口取心井累积410m井段的预测结果表明,模型的决定系数高达0.879,预测结果平均绝对误差和均方误差分别为0.220和0.107,平均相对误差为16.1%。研究结果为准噶尔盆地勘探领域优选提供了可靠参考。
