Defining kerogen maturity from orbital hybridization by machine learning

doi:10.1016/j.fuel.2021.122250

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Defining kerogen maturity from orbital hybridization by machine learning
	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
	2022-02-15
卷号	310 页码:10
关键词	Kerogen maturity Orbital hybridization Machine learning Quantum chemistry
ISSN号	0016-2361
DOI	10.1016/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
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
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
内容类型	期刊论文
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.