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

干酪根 油页岩 石油工程 化石燃料 页岩油 非常规油 页岩气 致密油 烃源岩 地质学 生化工程 化学 有机化学 古生物学 工程类 构造盆地
作者
Dongliang Kang,Jun Ma,Ya‐Pu Zhao
出处
期刊:Energy & Fuels [American Chemical Society]
卷期号:37 (1): 98-117 被引量:10
标识
DOI:10.1021/acs.energyfuels.2c03307
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
七日春信完成签到,获得积分10
刚刚
研友_Zzrx6Z发布了新的文献求助10
刚刚
xxddw发布了新的文献求助10
3秒前
乐乐应助星空采纳,获得30
4秒前
舒心渊思完成签到,获得积分10
8秒前
iNk应助经绮梅采纳,获得10
8秒前
savesunshine1022完成签到,获得积分10
8秒前
Koi发布了新的文献求助20
9秒前
11秒前
Owen应助娜娜采纳,获得10
11秒前
七日春信发布了新的文献求助10
12秒前
fanfan完成签到 ,获得积分10
14秒前
14秒前
17秒前
旧梦发布了新的文献求助10
22秒前
李爱国应助大青山采纳,获得10
23秒前
24秒前
NexusExplorer应助潇湘雪月采纳,获得10
27秒前
27秒前
娜娜完成签到,获得积分20
29秒前
wanci应助zpc采纳,获得10
31秒前
33秒前
一行完成签到,获得积分10
33秒前
xxddw发布了新的文献求助10
34秒前
36秒前
英姑应助fengliurencai采纳,获得10
39秒前
39秒前
xxddw完成签到,获得积分10
41秒前
逝月完成签到,获得积分10
41秒前
Koi完成签到,获得积分10
42秒前
思源应助大青山采纳,获得10
42秒前
yuqinghui98发布了新的文献求助10
42秒前
科研通AI2S应助mariawang采纳,获得10
43秒前
恋雅颖月应助潇湘雪月采纳,获得10
43秒前
Rain发布了新的文献求助10
43秒前
43秒前
44秒前
归尘应助小绵羊采纳,获得10
44秒前
我爱看文献是假的完成签到,获得积分10
47秒前
JW完成签到,获得积分10
48秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989390
求助须知:如何正确求助?哪些是违规求助? 3531487
关于积分的说明 11254109
捐赠科研通 3270153
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809174