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Defining kerogen maturity from orbital hybridization by machine learning

文献类型：期刊论文

作者Ma J(马俊)1,2; Kang DL(康东亮)1,2; Wang XH(王晓荷)1,2; Zhao YP(赵亚溥)1,2
刊名FUEL
出版日期2022-02-15
卷号310页码:10
关键词Kerogen maturity Orbital hybridization Machine learning Quantum chemistry
ISSN号0016-2361
DOI10.1016/j.fuel.2021.122250
通讯作者Zhao, Ya-Pu(yzhao@imech.ac.cn)
英文摘要Kerogen is the primary material for oil and gas. Its maturity is used to determine the potential for hydrocarbon generation. Nowadays, kerogen maturity is mainly measured experimentally and characterized by its chemical composition. The fundamental reason for the change in its chemical composition during the maturation is the breaking and recombination of chemical bonds, manifested by the transformation in atomic hybridization based on quantum mechanics. While traditional methods are time-consuming and labor-intensive, machine learning technique has been introduced to clarify the relationship between hybridization and maturity. A kerogen maturity prediction model based on hybridization is constructed. The average error of the predicted values is only 4.91%, and more than 87% of the test samples have an error of less than 10%. The results demonstrate that the model can accurately predict the maturity of kerogen. As the evolution of kerogen maturity increases the proportion of sp(2) hybridized carbons, the orbital hybridization maturity index (OrbHMI) is proposed. The chemical changes in the thermal evolution and pyrolysis mechanism of kerogen can be explained and understood more essentially by OrbHMI. The results provide a basis for guiding artificial maturation and pave a promising path toward studying the kerogen structure and predicting hydrocarbon generating potential.
分类号一类
WOS关键词NUCLEAR-MAGNETIC-RESONANCE ; ROCK-EVAL PYROLYSIS ; OIL-SHALE KEROGEN ; SOLID-STATE NMR ; C-13 NMR ; CHEMICAL-STRUCTURE ; ORGANIC-MATTER ; KINETIC-MODEL ; EXPERIMENTAL SIMULATION ; THERMAL MATURATION
资助项目National Natural Science Foundation of China (NSFC)[12032019] ; National Natural Science Foundation of China (NSFC)[11872363] ; National Natural Science Foundation of China (NSFC)[51861145314] ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences[QYZDJ-SSW-JSC019] ; CAS Strategic Priority Research Program[XDB22040401]
WOS研究方向Energy & Fuels ; Engineering
语种英语
WOS记录号WOS:000710700100004
资助机构National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences ; CAS Strategic Priority Research Program
其他责任者Zhao, Ya-Pu
源URL[http://dspace.imech.ac.cn/handle/311007/87765]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
推荐引用方式
GB/T 7714		 Ma J,Kang DL,Wang XH,et al. Defining kerogen maturity from orbital hybridization by machine learning[J]. FUEL,2022,310:10.
APA 马俊,康东亮,王晓荷,&赵亚溥.(2022).Defining kerogen maturity from orbital hybridization by machine learning.FUEL,310,10.
MLA 马俊,et al."Defining kerogen maturity from orbital hybridization by machine learning".FUEL 310(2022):10.

入库方式： OAI收割

来源：力学研究所

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