机器学习和深度学习在地热资源开发中的应用:趋势与前景

Abdulrahman Al-Fakih, Abdulazeez Abdulraheem, Sanlinn Kaka
{"title":"机器学习和深度学习在地热资源开发中的应用:趋势与前景","authors":"Abdulrahman Al-Fakih,&nbsp;Abdulazeez Abdulraheem,&nbsp;Sanlinn Kaka","doi":"10.1002/dug2.12098","DOIUrl":null,"url":null,"abstract":"<p>This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post-2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.</p>","PeriodicalId":100363,"journal":{"name":"Deep Underground Science and Engineering","volume":"3 3","pages":"286-301"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.12098","citationCount":"0","resultStr":"{\"title\":\"Application of machine learning and deep learning in geothermal resource development: Trends and perspectives\",\"authors\":\"Abdulrahman Al-Fakih,&nbsp;Abdulazeez Abdulraheem,&nbsp;Sanlinn Kaka\",\"doi\":\"10.1002/dug2.12098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post-2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.</p>\",\"PeriodicalId\":100363,\"journal\":{\"name\":\"Deep Underground Science and Engineering\",\"volume\":\"3 3\",\"pages\":\"286-301\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.12098\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Deep Underground Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/dug2.12098\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Deep Underground Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/dug2.12098","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本研究深入探讨了机器学习和深度学习在地热资源开发中应用的最新进展,并将分析延伸至 2024 年。研究重点关注人工智能在地热行业中的变革作用,分析了 Scopus 和谷歌学术的最新文献,以确定新兴趋势、挑战和未来机遇。研究结果表明,人工智能(AI)的应用明显增加,尤其是在储层工程方面,2019 年后将有显著进步。本研究强调了人工智能在加强钻井和勘探方面的潜力,同时强调了详细案例研究与实际应用的结合。鉴于该领域的快速技术进步和未来趋势,它还强调了正在进行的研究和量身定制的人工智能应用的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post-2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.20
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信