Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

doi:10.1021/acs.energyfuels.2c03307

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	Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation
	Kang DL(康东亮); Ma J(马骏); Zhao YP(赵亚溥)
通讯作者	Zhao, Ya -Pu(yzhao@imech.ac.cn)
发表期刊	ENERGY & FUELS
	2023-01-05
卷号	37 期号:1 页码:98-117
ISSN	0887-0624
摘要	The shale revolution has provided abundant shale oil/gas resources for the world, but the efficient, sustainable, and environmentally friendly exploitation of shale oil/gas is still challenging. Kerogen is the primary hydrocarbon source of shale oil/gas. The research on the kerogen chemo-mechanical properties significantly influences the development of shale oil/gas extraction technology. Rapid reconstruction of the kerogen molecular models is the most effective way to study the generation mechanism of shale oil/gas from the bottom-up molecular level. However, due to the combinatorial explosion problem, the reconstruction complexity of kerogen increases sharply because of the kerogen's characteristics of complex origin, large molecular weight, and diverse functional groups. The traditional kerogen molecular reconstruction methods require professionals to comprehensively analyze various experimental information to approximate the actual kerogen molecular models through trial-and-error. So, the traditional methods are time and material-consuming and extremely inefficient. These shortcomings make researchers spend too much strength on the reconstruction of kerogen molecular models and cannot focus on the study of kerogen chemo-mechanical properties. For the past few years, state-of-the-art machine learning (ML) methods have been applied to intelligently reconstruct the kerogen molecular models through high-throughput and predict shale oil/gas production mechanisms. Although the current work is still in the infancy stage, ML methods are believed to be the most promising way to solve the drawbacks of traditional methods and reconstruct kerogen in reliable and large molecular weight. Hence, mechano-energetics is proposed to study the efficient development and utilization of energy based on mechanics and ML. This paper briefly reviews the development history of kerogen molecular model reconstruction methods and the research of ML in the fields of kerogen reconstruction and shale oil/gas exploitation. Some recommendations for further ML-based work are also suggested. We are convinced that the ML methods will accelerate the research of kerogen and promote the significant development of unconventional oil/gas exploitation technologies.
DOI	10.1021/acs.energyfuels.2c03307
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000895518200001
关键词[WOS]	SEDIMENTARY ORGANIC-MATTER ; NATURAL SULFURIZATION ; ALIPHATIC STRUCTURES ; MECHANICAL PROPERTY ; METHANE ADSORPTION ; CHEMICAL-STRUCTURE ; ORIGIN ; DYNAMICS ; STATE ; GAS
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
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]
项目资助者	National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) Key Research Program of Frontier Sciences ; CAS Strategic Priority Research Program
论文分区	二类
力学所作者排名	1
RpAuthor	Zhao, Ya -Pu
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/91267
专题	非线性力学国家重点实验室
作者单位	1.{Kang Dongliang, Ma Jun, Zhao Ya -Pu} Chinese Acad Sci Inst Mech State Key Lab Nonlinear Mech Beijing 100190 Peoples R China 2.{Kang Dongliang, Ma Jun, Zhao Ya -Pu} Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China
推荐引用方式 GB/T 7714	Kang DL,Ma J,Zhao YP. Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation[J]. ENERGY & FUELS,2023,37,1,:98-117.
APA	康东亮,马骏,&赵亚溥.(2023).Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation.ENERGY & FUELS,37(1),98-117.
MLA	康东亮,et al."Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation".ENERGY & FUELS 37.1(2023):98-117.

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