| 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
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| Corresponding Author | Zhao, Ya-Pu([email protected]) |
| Source Publication | FUEL
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| 2022-02-15 | |
| Volume | 310Pages:10 |
| ISSN | 0016-2361 |
| Abstract | 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. |
| Keyword | Kerogen maturity Orbital hybridization Machine learning Quantum chemistry |
| DOI | 10.1016/j.fuel.2021.122250 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000710700100004 |
| WOS Keyword | 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 Research Area | Energy & Fuels ; Engineering |
| WOS Subject | Energy & Fuels ; Engineering, Chemical |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences ; CAS Strategic Priority Research Program |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Zhao, Ya-Pu |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/87766 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation 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.Rp_Au:Zhao, Ya-Pu |
| 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. |
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| Jp2021F526.pdf（4071KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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